ClaimFlow

Angle Assist

Live AR guides frame VIN plates, serial stickers, and damage zones with alignment borders and a quality meter for glare, blur, and shadows. Ensures first-shot captures that maximize OCR accuracy, reducing retakes and speeding the 2‑second extraction. Ideal for field adjusters and FNOL agents who need fast, reliable shots.

Requirements

Smart Alignment Overlay

"As a field adjuster, I want live alignment guides that adapt to VIN plates and damage zones so that I can frame shots correctly on the first try and avoid retakes."

Description

Live, on-screen AR borders and corner anchors that dynamically conform to VIN plates, serial stickers, and damage zones using edge detection and device pose data. Supports portrait/landscape, variable plate sizes, and custom templates defined in ClaimFlow admin. Provides real-time tilt/level guidance via gyroscope, high-contrast guides for outdoor glare, and haptic cues when alignment enters acceptable bounds. Designed to minimize framing errors and speed first-shot capture while integrating seamlessly with the Angle Assist camera view.

Acceptance Criteria

Dynamic AR Border Conformance to Plate Edges

Given the camera preview shows a VIN plate or serial sticker with detectable edges and sufficient lighting When edge detection confidence is >= 0.85 Then the AR borders and corner anchors align to detected edges within max(4 px, 1.5% of the shorter edge) mean error over 10 consecutive frames And overlay updates have a per-frame latency <= 33 ms (measured end-to-end from detection to render) And if tracking confidence drops < 0.6, a "tracking lost" indicator appears within 100 ms and snap cues are hidden And after brief occlusion or motion blur, tracking re-acquires and re-aligns within 200 ms at confidence >= 0.85

Gyro-Based Tilt and Level Guidance

Given the device gyroscope is available and calibrated When device roll or pitch deviates more than ±1° from level while an alignment target is active Then a visible tilt indicator appears and updates at least 30 fps with angular resolution ≤ 0.5° And when tilt is within ±2° and edge detection confidence is >= 0.85 for ≥ 500 ms Then the state changes to Aligned and a light haptic tick is triggered within 50 ms

Orientation and Reflow (Portrait/Landscape)

Given the user rotates the device between portrait and landscape during active alignment When the OS orientation change event is received Then the overlay, anchors, and quality meter reflow to the new orientation within 250 ms without visible flicker or layout jump > 8 px And the alignment ROI preserves its relative position and aspect to the detected plate within ±5% And the capture button and core controls remain unobstructed and tappable (>= 48x48 dp)

Variable Sizes and Admin Templates Application

Given ClaimFlow admin defines templates for VIN plates, serial stickers, and damage zones with aspect and padding metadata When a capture session starts with a selected or auto-detected target type Then the corresponding template is applied and the overlay conforms to the target with aspect variance ≤ ±5% and padding variance ≤ ±2% And if no matching template exists, a generic rectangular template is applied and the system logs a template_miss event And on capture, metadata includes template_id, template_version, applied_padding, and aspect_ratio

High-Contrast Guides Under Outdoor Glare

Given ambient lighting produces glare or high luminance backgrounds (quality meter glare flag = true) When the overlay is rendered over the target area Then guide colors and strokes auto-adjust to maintain a contrast ratio ≥ 4.5:1 against the median 10x10 px sampling window beneath each guide segment And stroke width scales between 2–4 dp based on estimated camera distance to maintain visibility And contrast adjustments occur within 100 ms of glare detection

Haptic Cues on Acceptable Alignment

Given device haptics are enabled at the OS level When alignment enters the acceptable window (tilt within ±2°, confidence >= 0.85) for ≥ 500 ms Then a single light haptic tick is emitted and debounced so that subsequent ticks occur no more than once every 1,000 ms And no haptic feedback is produced if OS haptics are disabled And the haptic call is non-blocking with execution time ≤ 5 ms on the UI thread

Performance and Integration with Angle Assist View

Given Angle Assist camera view is active with Smart Alignment Overlay enabled When operating on a supported mid-tier device Then camera preview maintains ≥ 30 fps at p50 and ≥ 25 fps at p90 with CPU utilization ≤ 60% and additional memory footprint ≤ 50 MB And overlay layers do not occlude the capture button or quality meter; z-order places guides below CTA and above preview And no network calls are required for alignment; feature operates fully offline And on capture, alignment metrics (tilt_deg, confidence, template_id, timestamp) are attached to the image payload

Real-time Quality Meter

"As an FNOL agent, I want a live quality score with clear guidance so that I can fix issues before I capture and reduce the need for retakes."

Description

Continuous on-device assessment of blur, glare, shadows, motion, and occlusions that renders a visible score and pass/fail indicator at 25–30 fps. Thresholds are configurable per template to optimize OCR accuracy. Provides corrective prompts (e.g., tilt device, move closer, reduce glare) and gates the shutter until minimum quality is reached. Uses lightweight ML/computer vision for low latency and includes a developer API to log quality metrics for tuning and A/B tests.

Acceptance Criteria

Real-time Frame Rate and Latency Compliance

Given the Angle Assist camera preview is active on a supported device and the Quality Meter is enabled When frames are evaluated continuously for 10 seconds under typical indoor lighting Then the quality score and pass/fail indicator update at an average of >= 25 fps (minimum instantaneous rate 20 fps), per-frame processing time is <= 30 ms at p95, and end-to-end preview-to-render latency is <= 120 ms at p95 And the Quality Meter executes fully on-device with no network calls on the frame-evaluation path

Multi-Dimension Quality Assessment Accuracy

Given a labeled validation set covering blur, glare, shadows, motion, and occlusions across VIN plate, serial sticker, and damage zone templates When the Quality Meter evaluates the dataset using the active template thresholds Then per-dimension classification achieves F1 >= 0.90 and AUC >= 0.95, and overall pass/fail decision accuracy >= 95% And score calibration error (ECE) <= 0.05 against the validation set

Per-Template Threshold Configuration and Application

Given template-specific threshold configuration is available locally or via remote config with versioning When an adjuster selects a template (e.g., VIN Plate) or a template is auto-detected Then the Quality Meter loads and applies the corresponding thresholds within 200 ms and uses them for scoring, gating, and prompts And if configuration is missing or invalid, the system falls back to default thresholds and logs a recoverable error with config_version="default" And changes to thresholds via remote config are applied without app restart within 60 seconds

Shutter Gating Behavior

Given a minimum quality threshold for the active template When the user attempts to capture while quality < threshold Then the shutter is disabled, capture is blocked, and the pass/fail indicator shows Fail And when quality >= threshold for at least 3 consecutive frames within the last 200 ms, the shutter becomes enabled, a haptic tick is emitted, and the indicator shows Pass And gating behavior is enforced in offline mode and during transient connectivity changes

Corrective Prompting Logic and Responsiveness

Given real-time per-dimension quality scores are computed When a dominant deficiency is detected (e.g., glare exceeds threshold by >10% while others are within threshold) Then a single highest-priority corrective prompt is displayed within 200 ms (e.g., Reduce glare, Hold steady, Move closer, Tilt device, Uncover target) And prompts auto-clear within 300 ms after the deficiency resolves, are fully localizable, and do not stack And no prompt is shown when pass state is maintained continuously for >= 1 second

Visible Score and Pass/Fail Indicator UX

Given the preview is visible and the Quality Meter is running When quality evaluations are produced each frame Then the UI displays a numeric score (0–100) updating each frame and a pass/fail indicator And the indicator meets accessibility: contrast ratio >= 4.5:1, screen-reader labels announce transitions ("Quality Pass"/"Quality Fail"), and a haptic feedback occurs on Fail->Pass And the meter UI does not occlude critical AR alignment borders (overlap area <= 5% of overlay bounds)

Developer Metrics API for A/B and Tuning

Given the developer metrics API is enabled When the Quality Meter evaluates frames during a capture session Then the API emits batched events at up to 2 Hz containing timestamp, device model, app version, template_id, config_version, overall score, per-dimension scores, pass/fail, prompt_shown, and shutter_enabled And events are queued offline and delivered within 10 minutes of connectivity, with at-most-once semantics and <1% drop rate over 1,000 test events And an optional A/B assignment key can be included and contains no PII; no image pixels are transmitted And metrics emission does not degrade frame rate below 25 fps average during the session

Auto-Capture Lock

"As a field adjuster, I want the camera to auto-capture when the shot is good so that I can work faster and ensure consistent, high-quality images."

Description

Automatic shutter trigger when framing and quality thresholds are met, with focus/exposure lock to prevent last-moment blur or exposure shifts. Provides haptic/audio confirmation, captures a short burst (e.g., 3 frames), and selects the best frame based on sharpness and glare metrics. Includes manual override, retry flow, and safeguards for low-light. Designed to hand off the selected frame to the 2‑second extraction pipeline immediately after capture.

Acceptance Criteria

Auto-trigger on alignment and quality readiness

Given the VIN/serial plate is framed within the AR alignment borders and the quality meter is in Ready (glare, blur, shadow thresholds met) for at least 250 ms while device motion is below the stability threshold When these conditions are sustained Then the shutter auto-triggers within 200 ms, AE/AF are locked at trigger time, and haptic plus audio confirmations are emitted immediately

Focus and exposure lock stability during auto-capture

Given auto-capture has been triggered When the burst begins Then AE/AF lock engages before the first frame and remains locked across the burst with exposure variance ≤ 5% and no refocus events until the burst completes

Burst capture and best-frame selection

Given auto-capture is triggered When a burst of 3 frames is captured within 500 ms Then a quality score is computed per frame using sharpness and glare metrics, the highest-scoring frame is selected; on ties select the frame with lowest motion blur, else the earliest; the selected frame’s quality score ≥ the auto-trigger threshold; the selected frame ID and quality metrics are stored with the capture

Haptic/audio confirmation with mute and visual fallback

Given a capture (auto or manual) occurs When feedback is emitted Then a 50–80 ms haptic is played on supported devices; an audio click is played unless the device is muted or in Do Not Disturb; a visual flash indicator appears in all cases; all feedback starts within 100 ms of the trigger

Manual override and cancel of auto-capture

Given the user is in Angle Assist preview When the user taps the shutter before auto-trigger Then a capture is taken immediately (≤ 100 ms) with AE/AF lock and auto-capture is suppressed for the next 3 seconds; and when the user taps Cancel while auto-trigger is pending, no capture occurs and auto-trigger is reset

Low-light safeguards and torch assist

Given estimated scene luminance is below the low-light threshold or device motion exceeds the blur-safe threshold When preview is active Then auto-capture is disabled and a Low light/motion warning with a torch toggle is shown; when the torch is enabled, thresholds are re-evaluated every 100 ms and auto-capture proceeds only when quality and stability thresholds are met; exposure time of captured frames must be ≤ the device’s blur-safe maximum

Immediate handoff to 2-second extraction pipeline

Given the best frame has been selected When the capture concludes Then the image is handed off to the extraction pipeline within 100 ms, UI shows Extracting, exactly one selected frame is sent, and 95th-percentile time from handoff to first OCR result is ≤ 2.0 s under normal connectivity; user cannot trigger another capture until handoff starts or they cancel

OCR-Ready Metadata Packaging

"As a claims manager, I want captured images sent with context and quality data so that OCR is more accurate and intake workflows can auto-route without manual review."

Description

On capture, packages the selected frame with structured metadata (bounding boxes, quality scores, angle, exposure, timestamps, device model) and securely posts to ClaimFlow’s OCR/extraction service. Associates capture with the active claim, entity type (VIN, serial, damage), and workflow step. Implements retry/backoff, response parsing, and error surfaces in the UI. Stores metadata for analytics while following data retention policies, enabling auto-tagging and task routing upon OCR completion.

Acceptance Criteria

Package Frame With OCR-Ready Metadata

Given an Angle Assist capture is finalized for an active claim When the app packages the selected frame Then the payload conforms to metadata schema v1 containing: imageId (UUID v4), claimId, entityType ∈ {VIN, SERIAL, DAMAGE}, workflowStep, boundingBoxes[] with {x, y, width, height} within image bounds, qualityScores {glare, blur, shadow, overall} in [0.0,1.0], angle {pitch, roll, yaw} in degrees within [-180,180], exposure {iso > 0, shutter > 0, ev within [-10,10]}, timestamp in UTC ISO-8601 with millisecond precision, device {model, osVersion}, appVersion And all required fields are non-null and pass client-side validation And only the selected frame is included with no additional frames or thumbnails

Secure Submission to OCR Service

Given the device has network connectivity When the app posts the payload to the OCR/extraction endpoint Then the request is sent over HTTPS with TLS 1.2+ and certificate pinning enabled And the request includes a valid OAuth2 Bearer token in the Authorization header And no secret or token is written to logs or persisted in plaintext And if TLS pin validation fails or the certificate is untrusted, the post is aborted and no retries are attempted And if the server returns 401/403, the token is refreshed once and the request retried a single time; thereafter the user is prompted to re-authenticate

Associate Capture With Claim and Workflow Context

Given a capture is initiated within a specific claim and Angle Assist mode (VIN/Serial/Damage) When the capture is posted Then the payload includes claimId, entityType, and workflowStep matching the app’s current context at shutter time And the server response echoes claimId and entityType, which are stored with the capture record And if the user navigates away or switches claims before delivery, the capture remains associated to the original claim and workflowStep

Reliable Delivery With Retry and Idempotency

Given a transient failure occurs (network timeout, DNS error, HTTP 5xx) When posting the capture Then the client retries with exponential backoff (initial 1s, factor 2.0, max interval 30s) with jitter, up to 5 attempts And an Idempotency-Key header equal to the imageId is included on every retry And duplicate acknowledgements from the server return the same result without creating duplicate records And while offline, the payload is queued locally (encrypted at rest) and automatically sent when connectivity resumes And if all retries exhaust, the capture is marked “Delivery failed” with a retry action available

Parse OCR Response and Trigger Auto-Routing

Given the OCR service responds with 200 and a JSON body containing extracted fields and tags When the app receives the response Then the response is validated against response schema v1 and parsed without error And the claim is auto-tagged with returned tags and the appropriate workflow task(s) are created within 2 seconds of receipt And the capture record is updated with ocrStatus=Complete and a reference to the OCR job/result ID And if the OCR service responds 202 with a jobId, the client subscribes/polls and processes the completion callback within the configured timeout; on completion, the same tagging and task routing occur And if the OCR service responds with a failure code and reason, the capture is marked ocrStatus=Failed and retake/retry guidance is presented

Surface Errors and Recovery Paths in UI

Given an error occurs during packaging, submission, or OCR processing When the error is classified (validation, auth, network, server, quality) Then the UI displays a user-friendly message with specific cause and a recommended action (Retake, Retry, Sign in) And technical details are captured in logs with correlationId and imageId but without PII or tokens And the user can retry failed submissions without losing the captured frame And error banners and controls are accessible (screen-reader labels present; contrast >= AA) And all error states are tracked in telemetry with error codes and latency

Analytics Storage and Retention Compliance

Given analytics storage is enabled with retentionTTL days configured When a capture is completed and posted Then metadata (quality scores, angle, exposure, device, timestamps, entityType, workflowStep, success/failure flags, durations) is stored in the analytics store without image bytes or OCR text content And records include a pseudonymized claim identifier suitable for aggregation And access to analytics data requires authenticated role-based access And a daily purge job removes analytics records older than retentionTTL days, verified by backdated test records And when a claim is deleted or a data subject erasure request is processed, associated analytics records are purged within 24 hours

Cross-Device Performance & Offline Mode

"As a field adjuster working in low-connectivity areas, I want Angle Assist to function smoothly offline so that I can capture high-quality images without waiting for network connectivity."

Description

Optimized performance across iOS 14+/Android 9+ with adaptive quality checks to maintain 25–30 fps on mid-tier devices. Runs models on-device to minimize latency and supports offline operation for up to 15 minutes, queueing captures and quality metrics and syncing automatically when online. Includes thermal/battery safeguards, graceful degradation of AR effects under load, and a QA matrix covering common devices and camera modules.

Acceptance Criteria

Maintain 25–30 FPS on mid-tier devices

Given Angle Assist camera preview is active on a QA Tier-2 device running iOS 14+ or Android 9+ And default capture settings and normal indoor lighting (300–500 lux) When the user continuously frames a target for 3 minutes Then median render FPS >= 27 and p90 FPS >= 25 And frame drop rate <= 5% And touch-to-frame interaction latency <= 80 ms p95 And no crash or OS kill occurs

Adaptive AR effect degradation under load

Given Angle Assist is running And the system detects either FPS < 25 for >= 2 continuous seconds or device thermal state >= Elevated/Throttling or battery <= 15% When the condition is detected Then the system degrades visual effects in this order: (1) disable shadow mask, (2) disable reflections, (3) reduce overlay refresh to 15 Hz, (4) reduce mesh complexity by 50% And the quality meter remains visible and updates at >= 10 Hz And upon 5 seconds of sustained recovery (FPS >= 27, normal thermal, battery > 15%), effects are restored stepwise in reverse order And a single, unobtrusive banner informs the user of performance mode (no more than once per session) And all state transitions are logged with timestamps

On-device model latency and autonomy

Given network connectivity is absent or < 100 kbps When initializing Angle Assist on-device models Then model cold-start completes in <= 1000 ms on QA Tier-2 devices And during active preview, per-frame inference time p95 <= 120 ms And no network calls are made during capture and quality assessment And after capture, OCR extraction for VIN/serial images completes in <= 2.0 seconds p95 on QA Tier-2 devices

Offline capture queuing for up to 15 minutes

Given the device goes offline during an Angle Assist session When the user captures images and associated quality metrics for up to 15 minutes Then all captures and metrics are queued locally with original order preserved And the queue supports at least 150 images and 500 metric records without loss And additional local storage consumption stays <= 50 MB And the session shows a visible "Queued" state within 1 second of each capture And upon reconnection, sync starts within 10 seconds and completes without user action

Sync resilience, persistence, and conflict handling

Given there is a non-empty offline queue And the app is force-quit or the OS restarts while still offline When the app relaunches Then queued items persist and resume syncing automatically when online And each item is submitted idempotently via content-hash deduplication to prevent server duplicates And transient sync failures are retried with exponential backoff up to 5 attempts And permanent failures surface as user-visible errors with a retry option, while successful items are not re-sent And if offline duration exceeds 15 minutes, capture is paused, queued data is retained, and the user is prompted to resume when online

Thermal and battery safeguards enforcement

Given Angle Assist is running When the device thermal state reaches Serious/Critical or battery <= 5% Then the camera preview pauses automatically, current capture is safely saved, and a blocking alert explains the condition and next steps And background processing is suspended during the pause to reduce load And capture cannot resume until thermal state returns to Nominal/Moderate and battery > 5% And no crash or app-not-responding occurs during transitions And all events are logged for diagnostics

QA matrix device and camera coverage

Given a release candidate build is ready When executing the QA device/camera matrix Then coverage includes >= 90% of the top 20 device models by active user share across target regions And tests span camera modules with and without OIS/EIS and multiple lenses where available And each run records FPS (median, p90), inference p95, thermal events, and battery delta per 3-minute session And release gates require 0 Critical, <= 2 High severity open defects, and overall pass rate >= 95% And the signed QA matrix report is attached to the release notes prior to launch

On-Device Privacy, Consent & Audit

"As a compliance officer, I want on-device processing with audit trails so that sensitive data is protected and our organization meets regulatory and client requirements."

Description

Performs quality analysis on-device, with ephemeral image buffers and no persistent storage unless policy requires it. Encrypts data in transit, supports configurable consent prompts, and masks sensitive regions (e.g., faces) when capturing broader damage zones. Generates an audit trail including model version, thresholds used, and operator identity, enabling compliance with SOC 2/ISO 27001 and insurer retention policies.

Acceptance Criteria

On-Device Quality Analysis and Ephemeral Buffers

Given a capture session starts, When quality analysis runs, Then all computations occur on-device with no network calls to analysis services. Given capture completes, When the session ends or the app backgrounds, Then all raw frames and intermediate buffers are purged from memory within 300 ms and never written to persistent storage unless enforce_retention=true. Given enforce_retention=false, Then no image files exist in the app sandbox after session end (verified via file system scan APIs). Given an unexpected crash, When the app next launches, Then a startup routine zeroizes any residual temp files and records audit event "buffer_purge_after_crash". Given enforce_retention=true, When storing is required by policy, Then only masked images are stored and are encrypted using hardware-backed keys; storage path and retention_expiry are recorded in the audit trail.

Consent Prompt and Capture Gate

Given consent_required=true and no prior consent for the current claim, When the user opens Angle Assist, Then display the localized consent prompt with configured text/version and require explicit accept to proceed. Given the user declines consent, Then block capture, do not process camera frames, and record audit event "consent_declined" with operator_id, timestamp, and jurisdiction_code. Given the user accepts consent, Then record audit event "consent_accepted" with operator_id, timestamp, jurisdiction_code, and prompt_version, and do not re-prompt within consent_ttl unless policy_version changes. Given jurisdiction_code maps to prompt_variant, When opening the prompt, Then the correct variant is shown based on configured mapping. Given consent_required=false, When opening Angle Assist, Then skip the prompt and record audit event "consent_not_required".

Sensitive Region Masking Enforcement

Given damage-zone capture mode, When faces or license plates are detected with confidence >= 0.60, Then apply real-time masks in the viewfinder and ensure only masked images are stored or transmitted. Given masking_enabled_policy=true, Then any UI control to disable masking is hidden/disabled; bypass attempts block capture and add audit event "masking_policy_enforced". Given manual masking is used by the operator, Then manual masks are composited with automatic masks and persist to any stored/transmitted images. Given previews/thumbnails are generated, Then only masked variants are cached and all are purged at session end. Quality target: On validation set, masks achieve IOU coverage >= 0.90 on detected regions; false-negative rate <= 5% at configured threshold.

Encrypted Data in Transit and Domain Allowlisting

Given any network transmission of images (masked) or extracted text, Then transport uses TLS v1.2+ with strong ciphers and certificate pinning to the configured allowlist of backend domains; requests to non-allowlisted domains are blocked. Given pin verification fails or TLS version < 1.2, Then the upload is aborted, the user sees "secure transport required", and audit event "tx_blocked_insecure_channel" is recorded. Given a MITM proxy intercept attempt, Then the client rejects the connection due to pin mismatch and no data leaves the device. Given background uploads, Then the same TLS and pinning policies apply; OS photo library is never used as a transport path.

Comprehensive Audit Trail and Tamper Evidence

Given any capture session lifecycle event, Then create an append-only audit record with fields: audit_id, claim_id, session_id, timestamp(UTC), operator_id, consent_status, model_version, quality_thresholds, policy_rule_ids, masking_status, network_security_status, storage_decision, and outcome. Given audit records are stored locally pending sync, Then each record is HMAC-SHA256 signed with a rotating key; tampered records are rejected on server verification and flagged. Given an authorized export request, Then audit records for a claim export as JSON within <= 2 seconds per 1,000 records and include signatures for integrity verification. Given offline mode, Then audit records queue locally and sync in original order when online with zero loss across app restarts.

Retention and Erasure Compliance

Given retention_policy = none, Then no images (masked or unmasked) persist beyond the session; only audit records and extracted structured data are retained per policy. Given retention_policy duration D > 0, Then only masked images persist and are auto-deleted at or before retention_expiry (drift <= 5 minutes); deletion events are logged in the audit trail. Given a data subject erasure request for claim_id, Then all retained images and derivatives are deleted within 24 hours and audit event "erasure_completed" is recorded; audit records persist but must exclude pixel data and direct PII beyond operator_id (per policy). Given an authorized operator triggers manual deletion, Then deletion occurs immediately, respects retention constraints, and is reflected in the audit trail.

Burst Boost

When confidence is low, PhotoFact Flash auto-captures a rapid burst of frames and fuses them to enhance clarity, then re-runs extraction in-line. Suggests alternate targets (door jamb VIN, dashboard plate, license plate lookup) to recover essentials fast. Delivers higher data confidence with less manual effort and fewer corrections.

Requirements

Confidence-Triggered Burst Capture

"As a field adjuster, I want the app to auto-capture a burst when the first photo is unclear so that I can quickly obtain a readable image without manually retaking multiple photos."

Description

Automatically initiates a rapid burst capture when PhotoFact extraction confidence for key fields (e.g., VIN, license plate, policy ID) falls below a configurable threshold. Captures 8–12 frames with exposure/focus bracketing and motion stabilization using native iOS/Android camera APIs and WebRTC where applicable. Provides real-time on-screen framing guidance, minimal-latency capture, and offline-safe operation with temporary, encrypted local storage that is purged post-fusion. Integrates with ClaimFlow’s workflow engine to pause the intake step, record trigger conditions, and resume once results are available. Includes guardrails for device thermals/battery, concurrency limits, and secure handling of transient media to meet privacy and data residency requirements.

Acceptance Criteria

Low-Confidence Auto-Trigger and Burst Parameters

Given ClaimFlow PhotoFact extraction is configured with key fields VIN, License Plate, and Policy ID and a per-field confidence threshold of 0.90 And burst parameters are configured to N=10 frames with exposure bracketing {-1EV, 0EV, +1EV}, focus bracketing {near, far}, and stabilization enabled when supported When a capture attempt yields a VIN confidence of 0.62 (< 0.90 threshold) Then Burst Boost auto-initiates within 300 ms of the low-confidence signal And captures exactly 10 frames within 1.5 seconds And the captured frames include at least 3 distinct exposure values and at least 2 distinct focus distances And camera stabilization is reported as enabled in capture metadata on devices that support it; otherwise a software stabilization path is used and reported And fusion completes and re-extraction is executed immediately on the captured burst And all trigger details (field=VIN, prior_confidence=0.62, threshold=0.90, device_model, os_version, timestamp) are recorded in the audit log for the claim

Real-Time Framing Guidance and Minimal Latency

Given burst capture is about to start due to a low-confidence signal When the burst trigger fires Then a framing guidance overlay with bounding guides and a steadiness indicator appears within 150 ms And the preview frame rate remains at least 24 fps during capture on devices capable of 30 fps in normal mode And the shutter begins capturing within 250 ms and completes the burst within 1.5 seconds And the user can cancel the burst within the first 500 ms via a visible cancel control, which immediately stops capture and logs the cancellation event

Offline-Safe, Encrypted Storage, and Purge

Given the device is offline (airplane mode) and local secure storage is available When a burst is captured and fusion/re-extraction runs Then all burst frames and intermediate fused artifacts are stored only in an encrypted app sandbox using platform keystore-backed encryption And no media bytes are transmitted off-device; only final extracted field values and confidences are queued for later sync And all transient media are purged within 30 seconds of fusion completion or immediately after successful extraction, whichever is sooner And after app restart, no burst media persists on disk and a secure deletion record is present in the audit log

Workflow Pause-Resume and Audit Trail

Given a claim intake step is active in ClaimFlow and burst capture is triggered for a key field When the burst begins Then the intake step status transitions to Paused with reason=burst_capture and a visible indicator is shown in the UI And trigger conditions (field, prior_confidence, threshold, device_state, thermal_state, battery_level) are recorded to the workflow event stream And upon fusion and re-extraction completion, the step automatically resumes within 500 ms and the updated field values and confidences replace prior values And events burst_started and burst_completed with a shared correlation_id are emitted to the workflow engine and stored for traceability

Thermal and Battery Guardrails

Given device thermal state is Severe or battery level is below 10% When a low-confidence condition occurs Then the system does not initiate a full burst; it reduces frames to at most 4 or defers with a user prompt indicating the constraint And no more than 1 burst attempt is allowed within any 60-second window under constrained conditions And a guardrail event with reason (thermal or battery) is logged and surfaced to telemetry And when conditions return to Normal and battery is at least 15%, full burst behavior is restored automatically

Concurrency Limits and Claim Context Integrity

Given a burst capture is already in progress for a claim session When a second low-confidence signal arrives for the same or another field before the first burst completes Then the system does not start a second burst concurrently; it queues a single follow-up burst request or collapses duplicates, ensuring max concurrent bursts per device equals 1 And camera access remains owned by the active burst without error or crash And all results are associated with the correct claim and step via a correlation_id; no cross-claim contamination occurs

Cross-Platform Capture Path and Fallbacks

Given the app runs on iOS 16+, Android 10+, and modern browsers supporting WebRTC When burst capture is triggered Then native AVFoundation (iOS) or CameraX/Camera2 (Android) APIs are used respectively, and WebRTC getUserMedia is used on web And if camera permission is denied or unsupported, the system does not trigger a burst; it logs the denial, shows non-blocking guidance to grant permission, and allows manual capture fallback And feature parity is maintained across platforms for frame count, bracketing behavior, stabilization flag reporting, and purge behavior

Multi-frame Fusion Engine

"As a claims manager, I want the system to automatically enhance low-quality captures so that extraction succeeds even in low light or when there is motion blur."

Description

Fuses captured burst frames into a single, higher-clarity image using deblurring, denoising, super-resolution, and HDR techniques to improve legibility of small text and embossed characters on VIN plates, stickers, and documents. Runs on-device with hardware acceleration (Neural Engine/NNAPI) when supported, with seamless fallback to secure cloud processing if device capabilities are insufficient. Exposes a deterministic API that returns the fused image, a quality score, and processing metadata within target latency budgets. Integrates with ClaimFlow’s media pipeline, supports model/version management, and enforces resource caps to protect app responsiveness.

Acceptance Criteria

On-Device Acceleration with Seamless Cloud Fallback

Given a device with supported Neural Engine/NNAPI and a 5–10 frame burst, When fusion is requested, Then processing_path="on_device", accelerator ∈ {"NeuralEngine","NNAPI"}, and runtime_ms is populated. Given a device without required acceleration or when resource caps are active, When fusion is requested, Then processing_path="cloud", all uploads occur over TLS 1.2+ with certificate pinning, request_id is present, and local raw frames are purged within 60 seconds after successful upload. Given identical inputs processed on-device and in-cloud with the same model_version, When comparing quality_score, Then absolute difference ≤ 0.02 and metadata.model_version matches. Given no network connectivity during required cloud fallback, When fusion is requested, Then a non-blocking error is returned (HTTP 503 with problem+json), UI remains responsive, and a retry token with exponential backoff policy is provided in metadata.

Fused Image Quality and Legibility for VIN/Text

Given the standard VIN/document low-light/blur dataset, When fusion completes, Then P90 quality_score ≥ 0.85 for VIN targets and ≥ 0.80 for document targets, and no fused image has SSIM < 0.90 versus the best input frame. Given baseline OCR on the best single frame, When OCR is run on the fused image, Then mean character accuracy improves by ≥ 15 percentage points and VIN checksum validity rate improves by ≥ 20 percentage points. Given a fused result with quality_score below threshold (VIN: 0.85, Document: 0.80), When metadata is generated, Then low_confidence=true and alternate_targets ∈ {"door_jamb_vin","dashboard_plate","license_plate_lookup"} are suggested.

Deterministic API Contract and Metadata

Given the same ordered burst, identical parameters, and the same model_version, When fusion runs twice, Then the SHA-256 of the fused image, the quality_score (±1e-6), and processing_metadata are identical. Given a successful fusion, When the API returns, Then the payload includes fused_image_uri, quality_score ∈ [0,1], and processing_metadata {model_version, processing_path, accelerator?, runtime_ms, frames_used, denoise_used, deblur_used, super_resolution_used, hdr_used, resource_caps_applied, request_id} with required fields non-null. Given invalid input (fewer than 2 frames, corrupt frame, or mixed resolutions), When the API is called, Then it returns HTTP 400 with problem+json including code, title, and detail, and it does not write any partial media artifacts.

Latency Budgets (On-Device and Cloud)

Given a 6×12MP burst on A14/SD778G-class devices, When processed on-device, Then P50 runtime_ms ≤ 700 ms and P95 ≤ 1200 ms recorded in metadata. Given a 6×12MP burst with required cloud fallback on a 20 Mbps uplink and 40 ms RTT, When processed, Then end-to-end P50 latency ≤ 1500 ms and P95 ≤ 2500 ms including upload, processing, and download. Given any processing path, When runtime_ms exceeds the P95 budget, Then metadata flags sla_breach=true and a telemetry event is emitted with request_id and model_version.

Resource Caps and App Responsiveness

Given on-device fusion running in the foreground, When monitored, Then app main-thread 99th percentile frame time ≤ 32 ms and dropped frames ≤ 1% during the operation. Given on-device fusion, When measured, Then peak additional memory usage ≤ 300 MB and average CPU/NN utilization ≤ 70% during the processing window. Given the thermal state approaches throttling, When detected, Then the engine throttles workload or switches to processing_path="cloud", sets resource_caps_applied=true, and completes without app termination.

Media Pipeline Integration and Re-Extraction

Given a successful fusion, When storing output, Then the fused image is written to the ClaimFlow media pipeline with a content-addressable ID, parent_burst_id linkage, and checksum verification passes. Given a new fused image, When the extraction service is invoked, Then extraction re-runs automatically and produces confidence ≥ the prior attempt OR the case is flagged for review with reason="no_improvement". Given media and metadata persistence, When retrieved later, Then the fused image and processing_metadata are accessible via canonical URIs and included in audit logs.

Model/Version Management and Rollback

Given multiple available model versions, When policy selects a version, Then metadata.model_version reflects the selected version and can be pinned via configuration for a tenant/device cohort. Given a model rollout, When failure rate or sla_breach exceeds 2× baseline over 100 consecutive runs, Then automatic rollback to the last stable model_version occurs within 10 minutes and is recorded in audit logs. Given a model update, When processed with older clients, Then the API contract remains backward compatible and A/B assignment is visible via metadata.ab_bucket.

Inline Re-extraction Pipeline

"As an adjuster, I want the system to retry extraction right after enhancement so that I don’t have to restart the intake step or correct fields manually."

Description

Re-runs OCR/VIN/plate extraction automatically on the fused image within the same intake step, merging results with prior attempts and updating field-level confidence scores. Publishes deltas to the UI and to ClaimFlow’s workflow/router so downstream tasks can auto-unblock without user intervention. Ensures idempotency, caching, and cost controls for repeated extraction attempts, and records provenance linking each extracted fact to its source image and processing path for auditability.

Acceptance Criteria

Inline fused-image re-extraction triggers on low confidence

Given an intake step where at least one required field has confidence < 0.85 on the initial extraction And a burst capture has completed and a fused image is available When the inline re-extraction pipeline is invoked Then OCR, VIN, and license plate extractors are re-run on the fused image within the same intake step without user action And the re-extraction completes with P95 latency <= 5s and a hard timeout of 10s And the pipeline records attempt_id and idempotency_key for this re-extraction

Confidence-aware field merge and non-regression

Given prior extraction results with field-level values and confidences And re-extraction results are available When merging results Then for each field, select the value with the highest confidence; if equal, prefer the most recent value And do not replace an existing value with a lower-confidence value And mark a field as updated only if the value changes or the confidence changes by >= 0.01 And unchanged fields retain their previous timestamps and provenance And merged results are stored atomically for the attempt

Delta publication to UI and workflow router

Given merged results with one or more updated fields When deltas are computed Then the system publishes one delta event per changed field containing claim_id, field_name, old_value, old_confidence, new_value, new_confidence, attempt_id, provenance_id, occurred_at And the UI receives the delta and updates the displayed field within 300ms of event receipt, visually indicating the change And the workflow/router consumes the delta within 500ms of publish and evaluates unblock rules And if a downstream task's rule is satisfied (confidence >= 0.85 and value present), the task transitions from Blocked to Ready automatically And no delta is published for fields that did not change by the defined threshold

Idempotent eventing and writes on retries

Given a re-extraction retry occurs with the same idempotency_key within a 10-minute window When the pipeline processes the retry Then no duplicate records are written for merged results, provenance, or audit logs And no duplicate delta events are emitted And the API response includes the same attempt_id as the original processing

Caching and cost controls for repeated extraction

Given a fused image with hash H and extractor config version M previously processed within the last 24 hours When a re-extraction is requested with the same H and M Then cached extractor outputs are reused and zero new external API calls are made And per-claim per-intake-step re-extractions are limited to a maximum of 3 attempts; subsequent requests are rejected with HTTP 429 and no processing occurs And at most 2 extractor types run concurrently per attempt; additional extractors queue And exponential backoff is applied between attempts with intervals of 2s and 4s

Provenance and audit trail completeness

Given any field value produced by the re-extraction When querying the provenance endpoint for that field Then the response includes value, confidence, source_image_ids, fused_image_id, extractor_type, model_version, extractor_config, attempt_id, idempotency_key, processing_node, started_at, finished_at And each provenance record includes a verifiable checksum of the fused image hash H And provenance records are immutable and retained for at least 7 years

Failure handling and safe fallback within intake step

Given re-extraction fails due to error or exceeds the 10s timeout When the failure occurs Then the UI continues to display prior extraction results without regressions And an error banner indicates the failure and any scheduled retry And the workflow/router receives no unblock events for fields affected by the failed attempt And automatic retries are attempted with exponential backoff starting at 2s, up to 2 retries total And errors are logged with a correlation_id and surfaced to monitoring within 60s

Alternate Target Guidance

"As an adjuster, I want clear prompts for alternative places to capture required identifiers so that I can proceed quickly when the initial target fails."

Description

Provides contextual guidance when primary targets remain unreadable, suggesting next-best capture targets such as door jamb VIN stickers, dashboard plates, license plates, or insurance cards. Ranks suggestions by vehicle type, region, and claim context, and presents overlay guides, microcopy, and haptic/audio cues to reduce capture friction. Supports quick target switching, offline heuristics, and streamlined manual entry with field validation as a last resort. Logs which suggestions succeed to improve future recommendations.

Acceptance Criteria

Trigger And Ranked Suggestions After Low-Confidence Primary Target

Given the user is capturing a primary target and extraction confidence remains below 0.75 for 2 consecutive evaluations after burst fusion When the system detects the low-confidence condition or unreadable primary target Then it displays an alternate-target panel within 500 ms containing at least 3 suggestions ranked by vehicle type, region, and claim context And the top-ranked suggestion matches the configured rules for the detected vehicle type and region in at least 95% of test cases And the panel can be dismissed or a suggestion accepted with a single tap

Overlay Guides And Multimodal Cues For Suggested Alternate Target

Given the user accepts a suggested alternate target When the overlay is presented Then the camera view shows target-specific framing guides aligned to expected geometry within ±5% and a one-sentence microcopy instruction localized to the app language And a short haptic pulse is emitted on target lock-on and a 250 ms confirmation tone plays only if system sound is enabled And all cues respect device mute, accessibility, and reduced motion settings And overlay elements meet minimum 12 pt text size and 4.5:1 contrast ratio for legibility

Quick Switch Between Capture Targets

Given alternate-target suggestions are visible When the user taps a suggestion chip or performs a single left/right swipe Then the capture mode switches to the selected target within 300 ms while maintaining a live preview And no more than one frame is dropped during the transition And the user can return to the previous target in two taps or fewer And the selected target is visually indicated and announced via screen reader

Offline Suggestion Heuristics Without Network

Given the device has no network connectivity When a low-confidence primary target is detected Then on-device heuristics generate a ranked list of alternate targets within 400 ms with no network calls attempted And an "Offline" badge is displayed in the suggestion panel And in offline mode the top-2 containment rate of suggestions is within 10% of the online baseline on the curated test set

Manual Entry Fallback With Field Validation

Given the user selects Manual Entry as a fallback When entering a VIN Then the form enforces 17-character ISO 3779 format, uppercases input, rejects I/O/Q, and validates the check digit before enabling submission And when entering a license plate, the form validates against the selected region's format and character set and auto-formats spacing where applicable And when entering a policy number, the form validates against the carrier profile's regex rules And invalid input produces an inline error message within 100 ms and blocks submission until corrected And all required fields must pass validation before the user can continue

Outcome Logging For Suggestions And Target Success

Given an alternate-target panel is shown or acted upon When capture completes (success) or is canceled (failure) Then the app logs timestamp, pseudonymous device/session IDs, claim context, suggestions shown and order, selection made, time-to-capture, and extraction confidences, plus success/failure outcome And analytics payloads exclude raw images and full-text PII; only hashed or redacted identifiers are included per policy And if offline, events are queued durably and transmitted within 5 minutes of connectivity with at least 99% delivery success And each log record includes the app version and model/ruleset identifiers for attribution

Suggestion Efficacy And Continuous Improvement

Given logging is enabled and data collection thresholds are met When evaluating a rolling 4-week window of auto claims Then the top-1 suggestion success rate is at least 60% and top-3 success rate is at least 85% on the validation cohort And if either metric falls below target for two consecutive weeks, a ruleset or model update is scheduled and deployed to adjust rankings within 24 hours of approval And post-deployment monitoring confirms metrics recover to target within 7 days

Plate-to-VIN Lookup Integration

"As a claims manager, I want the system to look up a VIN from a captured license plate when needed so that I can complete intake without returning to the vehicle."

Description

Performs VIN resolution from a high-confidence license plate capture via configurable third-party services. Implements secure API clients with authentication, rate limiting, retries, and circuit breakers, and enforces permitted-use, consent, and regional compliance rules. Masks sensitive data in transit and at rest, caches results per policy, and exposes feature flags per carrier/workflow. Provides graceful degradation and user messaging when services are unavailable.

Acceptance Criteria

VIN Resolution from High-Confidence Plate via Configurable Provider

Given a license plate extraction with confidence >= 0.90 and a recognized region/state When Plate-to-VIN lookup is triggered Then the system selects the highest-priority enabled provider for the carrier and region and invokes it with plate and region metadata Given the provider returns a VIN When the response is parsed Then the VIN is validated as 17 characters and checksum-valid, and the result is attached to the claim intake with a data-confidence score and provider provenance Given multiple providers are configured When the first provider returns a non-2xx status, empty result, or invalid VIN Then the system fails over to the next provider in the configured order within the same transaction Given lookup completes successfully Then the end-to-end call (including any failover) completes within 3 seconds p95 under nominal conditions and records correlation IDs and latency metrics

Resilient Secure API Client (Auth, Timeouts, Retries, Circuit Breaker, Rate Limits)

Given a provider requires API key or OAuth2 authentication When a lookup call is initiated Then credentials are loaded from a secret manager, injected only in memory, and never logged or persisted in plaintext Given outbound calls to providers Then HTTPS TLS 1.2+ is enforced with certificate validation; plaintext HTTP is rejected Given network variability and transient errors When calling a provider Then timeouts are set to connect <= 2s and read <= 5s; retry up to 2 times with exponential backoff (base 200ms, cap 1s) on 429, 5xx, and timeouts; respect Retry-After when present Given repeated provider failures When failure thresholds are met Then the circuit breaker opens after 5 consecutive failures or >=50% failure rate over 60s with a minimum of 20 requests; half-open after 30s; close on first successful probe Given provider-specific throughput constraints When issuing calls Then a client-side rate limiter enforces the configured QPS per provider; locally throttled 429s are not retried and are observed via metrics Given heterogeneous provider error models When errors occur Then errors are mapped to standardized error codes and messages for the workflow

Permitted Use, Consent, and Regional Compliance Enforcement

Given a carrier workflow with configured permitted-use basis and regional rules When a Plate-to-VIN lookup is requested Then the system verifies a valid permitted-use basis exists for VIN resolution for the subject region before sending any data Given the region requires explicit user consent When consent has not been captured Then the lookup is blocked, no data is sent to any provider, the UI displays a consent-required message, and an audit event is recorded Given the region is disallowed by policy When a lookup is attempted Then the request is blocked with a policy-violated error and the decision is audit logged with policy version and actor Given the lookup proceeds Then requests include required compliance parameters (e.g., purpose-of-use) if supported by the provider, and routing respects regional provider restrictions Given any allow/deny decision Then an immutable audit record is stored with timestamp, actor, carrier, workflow, policy version, and region

Sensitive Data Protection In Transit and At Rest

Given provider communications Then all requests use HTTPS TLS 1.2+ with strong ciphers; certificate pinning or trust store validation is enforced; plaintext endpoints are rejected Given logging and observability When recording requests and responses Then sensitive fields (license plate, customer identifiers, auth tokens) are redacted; only the last 3 characters of the plate are visible; VIN is logged only after checksum validation Given data persistence When storing provider responses and cache entries Then data at rest is encrypted using platform-standard encryption (e.g., AES-256) with managed keys; auth secrets reside only in a secret manager Given temporary payloads When processing lookups Then transient request/response bodies are retained no longer than 24 hours (configurable per carrier policy) and are automatically purged; purges are auditable Given access control When users or services request decrypted fields Then access is limited to authorized roles; unauthorized access attempts are denied and audited

Result Caching and Policy-Based Invalidation

Given a plate+region query matches a cache entry within the configured TTL (default 24h; per-carrier override) When a lookup is requested Then the cached VIN is returned without calling any provider, and the response indicates cache_hit=true with age seconds Given a cache miss or expired entry When a lookup succeeds Then the response is cached with key {plate, region, provider} and the policy-defined TTL; cache_write=true is recorded Given a carrier enables cache bypass for investigative workflows When bypass is true for a request Then no cache read/write occurs and the decision is audit logged Given a claim is withdrawn or a policy requires immediate invalidation When an invalidation event is received Then matching cache entries are purged within 60 seconds and the purge is reflected in metrics Given normal operations Then cache metrics (hit rate, evictions, stale fetches) are emitted and visible on dashboards

Feature Flags per Carrier and Workflow

Given Plate-to-VIN is feature-flagged per carrier and workflow When the flag is Off Then no provider calls are made and the UI indicates the feature is disabled for that workflow Given the flag is toggled On without redeploy When a new lookup is triggered Then the change takes effect within 60 seconds via remote configuration refresh Given a staged rollout is configured (e.g., 25%) When lookups occur Then only the configured percentage of eligible requests invoke provider calls and the remainder behave as flag Off; assignment is stable per claim ID within the rollout window Given governance needs When a flag is changed Then actor, timestamp, environment, rationale, and previous value are captured in an immutable audit log

Graceful Degradation and User Messaging on Provider Unavailability

Given the active provider returns 5xx, times out, or the circuit breaker is open When a lookup is requested Then the system returns a non-fatal failure to the workflow, does not block other intake steps, and displays a user-facing message indicating temporary unavailability and next steps Given multiple providers are configured When the primary is unavailable Then a fallback provider is attempted once according to the configured order before surfacing the degraded outcome Given all providers are unavailable When the lookup cannot be completed Then the UI offers configured alternatives (e.g., manual VIN entry) and logs a standardized error with correlation IDs for tracing Given repeated provider outages When incidents occur Then alerts are throttled to at most one per provider per 15 minutes to prevent alert storms; incident metrics are published

Configurable Thresholds and Policies

"As a product admin, I want to adjust thresholds and fallback order per workflow so that accuracy, cost, and user effort are balanced for each carrier."

Description

Offers admin settings to tune confidence thresholds, burst length, fusion parameters, retry limits, alternate target ordering, and on-device vs. cloud processing policies. Supports per-carrier and per-workflow overrides, environment-specific configurations, versioned rollouts, and instant rollback. Includes validation and guardrails to prevent overly aggressive capture loops and ensures all changes are recorded with user, timestamp, and reason in the configuration audit log.

Acceptance Criteria

Carrier-Level Confidence Threshold Override

Given a global confidence_threshold=0.85 and a carrier "ABC" override confidence_threshold=0.92 are configured and active When an image from carrier "ABC" is processed Then 0.92 is used to decide PhotoFact Flash triggering and entity acceptance And the applied threshold value (0.92) is recorded in the processing metadata And when an image from a non-ABC carrier is processed in the same environment, 0.85 is used

Workflow-Level Override Precedence

Given global confidence_threshold=0.85, carrier "ABC" confidence_threshold=0.90, and workflow "Auto-Physical-Damage" confidence_threshold=0.95 are configured When an ABC claim enters the "Auto-Physical-Damage" workflow Then the workflow-level threshold 0.95 is applied And when the claim moves to a workflow without an override, the carrier-level 0.90 is applied And processing metadata and the audit trail reflect precedence order: workflow > carrier > global

Burst Length and Fusion Guardrails

Given guardrails max_burst_length=12, max_fusion_strength=2.0, and processing_time_cap_ms=3000 are configured When an admin attempts to set burst_length=50 and fusion_strength=5.0 Then the change is blocked with validation errors indicating allowed ranges (burst_length <= 12, fusion_strength <= 2.0) And when the admin sets burst_length=8 and fusion_strength=1.5 Then PhotoFact Flash captures exactly 8 frames and applies fusion_strength=1.5 And the measured processing time per burst is <= 3000 ms and is logged in run metadata

Retry Limits and Alternate Target Ordering

Given alternate_target_order=[VIN_DoorJamb, VIN_Dashboard, LicensePlate_Lookup], retry_limit_per_target=2, and total_retry_limit=5 are configured When extraction fails for the current target Then the next target is attempted in the configured order And no target is attempted more than 2 times And the total number of retries across all targets does not exceed 5 And if retries are exhausted without success, the user is prompted for manual entry via a single clear CTA And the final data source and retry counts are recorded in metadata

On-Device vs Cloud Processing Policy

Given environment policies are configured: Field=OnDevice, Office=Cloud, with fallback=Allowed When the app runs in Field environment with network available Then burst fusion and extraction execute on-device and images are not uploaded And when the app runs in Office environment Then images are uploaded securely and processed in cloud And if the configured mode is unavailable (e.g., no secure enclave for on-device), the system falls back to the other mode, logs the fallback with reason, and continues processing

Versioned Rollout and Instant Rollback

Given configuration version v2 is created from v1 and a rollout target of 10% of Production users is defined When the rollout is started Then only the targeted 10% receive v2 within 60 seconds and all others remain on v1 And when a rollback to v1 is triggered Then 100% of users are reverted to v1 within 60 seconds and v2 is marked inactive And all rollout and rollback events include version_id, scope, actor, timestamp, and reason in the audit log

Configuration Audit Log Completeness and Integrity

Given an admin creates, updates, and rolls back settings across global, carrier "ABC", and workflow "Auto-Physical-Damage" scopes When the audit log is queried for a date/time range Then each entry contains user_id, timestamp (UTC), action (CREATE/UPDATE/ROLLBACK), scope, key, old_value, new_value, version_id, and reason And entries are returned in chronological order and are append-only (no edits or deletes allowed via UI or API) And any attempt to modify or delete an existing audit entry is rejected with HTTP 403 and no changes occur

Outcome Analytics and Audit Trail

"As an operations lead, I want visibility into Burst Boost outcomes and a complete audit trail so that I can demonstrate compliance and optimize performance over time."

Description

Captures granular metrics for Burst Boost including pre/post confidence, burst count, fusion success rate, re-extraction deltas, time-to-first-success, and manual corrections avoided. Writes an immutable, chronologically linked audit trail to each claim with source images, processing versions, and decisions. Streams summarized events to analytics dashboards and alerting to monitor impact, detect regressions, and drive continuous model and UX improvements while adhering to privacy and retention policies.

Acceptance Criteria

Burst Boost Metric Capture Completeness

Given a Burst Boost session is invoked for a claim When extraction runs pre-burst and post-fusion re-extraction completes Then the system records for the session: pre-extraction confidence per field, burst frame count, fusion success flag and score, post-extraction confidence per field, delta per field (post minus pre), time-to-first-success in milliseconds, and manual corrections avoided count And each metric is timestamped, associated to the claim ID, session ID, and processing version (model, fusion, ruleset) And metrics persistence succeeds with 99.5% completeness across sessions in a rolling 7-day window (no required field missing) And a failure to capture any required metric is logged with error code and retried up to 3 times with exponential backoff And an authenticated GET /claims/{claimId}/burst-boost/{sessionId}/metrics returns the full metric set in a documented schema with units and types And P95 metric write latency is <200 ms per session

Immutable, Chronological Audit Trail

Given any Burst Boost processing event (attempt, fusion, re-extraction, decision) occurs on a claim When the event is finalized Then an append-only audit entry is written with monotonic timestamp, actor (system/user), action, input references (content hashes of images), processing versions, outputs, and decision rationale, all linked to a claim and session correlation ID And each audit entry includes a cryptographic hash and previous-entry hash to form a verifiable chain; any mutation attempt is rejected and logged And source images are stored content-addressably; only their hashes and storage references appear in the audit entry And an authenticated GET /claims/{claimId}/audit supports chronological retrieval and export; exported chains validate hash continuity end-to-end And audit write availability is ≥99.99% monthly; P95 write latency <250 ms; retries with idempotency keys prevent duplicates

Real-time Analytics Event Streaming

Given a Burst Boost session completes (success or failure) When the summarization pipeline runs Then a summarized analytics event (schema v1) is published to the analytics bus with claimId, sessionId, timestamps, burstCount, fusionSuccess, confidenceLift, timeToFirstSuccessMs, manualCorrectionsAvoided, and errorCodes (if any) And events are redacted of PII and include only references (hashes/IDs) per policy And events are delivered to downstream consumers with at-least-once semantics and idempotent de-duplication via eventId for 24 hours And P95 producer-to-dashboard latency is ≤10 seconds; consumer lag P95 ≤10 seconds; end-to-end delivery success ≥99.9% daily And backfill can replay the last 30 days without schema drift; dashboards reflect replayed data without double counting

Regression Detection and Alerting

Given baselines for key KPIs (confidenceLift, fusionSuccessRate, timeToFirstSuccessMs) computed over the prior 7 days When the current rolling 60-minute window deviates beyond thresholds (confidenceLift drop >15% absolute, fusionSuccessRate drop >10% absolute, timeToFirstSuccessMs increase >30%) for any product segment (line of business, device type, geography) Then an alert is generated within 5 minutes containing metric, segment, baseline, current value, links to dashboards, and runbook And alerts are routed to Slack, Email, and PagerDuty with deduplication across channels and auto-resolve after 2 consecutive hours back within thresholds And synthetic regression tests can be triggered to verify alert creation and routing without impacting production metrics

Privacy, Access Control, and Retention Compliance

Given storage of metrics, events, and audit entries for Burst Boost When data is persisted and accessed Then all sensitive data is encrypted in transit (TLS 1.2+) and at rest (AES-256); access is restricted by role with least privilege and is fully audited And PII is minimized; images are referenced by content hash; dashboards show only aggregated, de-identified metrics And retention is enforced: raw images retained 180 days, derived metrics retained 3 years, audit trail retained 7 years, with configurable legal-hold exceptions And purge jobs run daily, produce tamper-evident reports, and remove personal data while retaining non-identifying aggregates; right-to-be-forgotten requests complete within 30 days And quarterly compliance tests pass with zero critical findings; access reviews show no violations

Manual Corrections Avoided Measurement

Given a defined baseline of manual correction rates without Burst Boost When Burst Boost runs for a claim Then the system computes manualCorrectionsAvoided by comparing accepted post-extraction values without user edits to the established baseline for the same field and context And the metric is stored per claim and aggregated per product segment; methodology and baseline version are recorded And an A/B toggle exists to validate attribution; analysis runs with minimum 500 sessions per variant and reports statistical significance (p<0.05) And dashboards display avoided corrections per 100 claims and cumulative hours saved, updated daily

Severity Map

Computer vision outlines damaged areas, classifies damage type (dent, crack, hail, rust), and estimates severity and affected parts. Auto-tags loss details and pre-fills structured fields, surfacing checklist tasks if critical views are missing. Accelerates triage, standardizes scoring, and reduces rework.

Requirements

Damage Segmentation & Type Classification

"As a claims adjuster, I want the system to automatically outline and label damage areas in photos so that I can quickly understand the type and extent without manual inspection."

Description

Implement a computer vision service that detects and outlines damaged regions in uploaded images, segments them with polygons, and classifies each region by damage type (e.g., dent, crack, hail, rust, scratch, corrosion). Support multiple regions per image, per-region confidence scores, and normalization of labels to ClaimFlow’s damage ontology. Accept common image formats (JPEG/PNG), handle varied lighting and angles, and process images individually or in batch. Produce overlay assets for the UI and a structured JSON payload for downstream systems via the platform’s event bus. Include model versioning, health checks, and graceful degradation paths when confidence is below configurable thresholds.

Acceptance Criteria

Single Image: Multi-Region Damage Detection & Segmentation

- Given a curated validation set of 500 images with annotated damaged regions, when the service processes each image at a max side of 1024 px, then: - Per-image damaged-region recall >= 0.85 and precision >= 0.85 at IoU >= 0.50. - Each returned region is a closed, non-self-intersecting polygon in pixel coordinates within [0,width) x [0,height). - Supports up to 100 regions per image without truncation. - Each region includes a unique regionId and a segmentationConfidence in [0.0,1.0].

Per-Region Damage Type Classification & Ontology Normalization

- Given the same validation set with ground-truth damage types {dent, crack, hail, rust, scratch, corrosion}, when the service classifies each detected region, then: - Macro-averaged F1 >= 0.75 across the six classes. - Each region includes a typePrediction {originalLabel, normalizedCode, confidence in [0,1]}. - normalizedCode maps to ClaimFlow ontology codes and never returns a value outside the allowed set; unknowns map to CF.DAMAGE.OTHER with reason "low-confidence" or "no-match". - If top-1 confidence < 0.65, include top-3 labels with confidences in descending order.

Confidence Thresholds & Graceful Degradation Behavior

- Given configurable thresholds T_seg (default 0.50) and T_cls (default 0.60), when results fall below thresholds, then: - Regions with segmentationConfidence < T_seg are excluded from auto-tagging and marked lowConfidence=true in the payload; overlays render them as dashed outlines. - Regions with type confidence < T_cls keep normalizedCode=CF.DAMAGE.OTHER and emit flag requiresHumanReview=true. - An event bus message with reason "LOW_CONFIDENCE" is published for the image; no blocking errors are thrown. - Thresholds are adjustable via settings and take effect without service restart within 60 seconds.

Single and Batch Processing Performance & Resilience

- Given a request with 1 image, then the response is returned within P95 <= 2.0s on the reference GPU profile and includes exactly one result object correlated by imageId. - Given a batch request with up to 100 images (mix of JPEG and PNG), then: - The service processes all images; one failure does not prevent others from returning results. - The order of results matches the input order; each result includes the original clientRequestId. - Overall throughput is >= 30 images/minute P95 on the reference GPU profile. - Per-image errors include machine-readable codes and messages; successful images are not omitted.

Overlay Asset Generation and Event Bus JSON Output

- Given any processed image, then: - A PNG overlay asset with alpha channel is generated with polygons color-coded by normalizedCode; filename pattern {imageId}_{modelVersion}_overlay.png. - The overlay and a thumbnail (max-side 512 px) are persisted to the configured object store with read URL and 24h TTL metadata. - A JSON payload is produced matching schema claimflow.cv.damage.v1 and includes imageId, modelVersion, processedAt (ISO-8601 UTC), regions[{regionId, polygon, bbox, areaPx, normalizedCode, originalLabel, confidences}], aggregate metrics, and lowConfidence flags. - The JSON is published to the platform event bus topic claimflow.cv.damage.v1 within 1s of processing completion and is acknowledged in the happy path.

Image Formats, Orientation Handling, and Robustness to Conditions

- Given JPEG or PNG inputs between 640 and 6000 px on the longest side, with or without EXIF orientation, when processed, then: - EXIF orientation is respected; output polygons align with visually upright image. - Non-supported formats (e.g., HEIC, GIF) return HTTP 415 with error code UNSUPPORTED_MEDIA_TYPE. - Images with low light, glare, or oblique angles still meet detection/classification metrics within 10% absolute of baseline metrics measured on the standard validation set. - If critical views are missing per platform rules, the payload includes missingCriticalViewsHint=true to trigger a checklist task downstream.

Model Versioning, Health Checks, and Observability

- Given the service is running, then: - GET /health returns 200 with {status:"up"} and includes {modelVersion, modelChecksum, uptimeSeconds}. - GET /ready returns 200 only after the model is loaded and warm; returns 503 otherwise. - Every response (API, payload, overlay metadata) includes modelVersion; version changes only via controlled deployment. - If the model fails to load, requests return 503 with error code MODEL_UNAVAILABLE and an event with reason "MODEL_UNAVAILABLE" is emitted; no partial results are produced. - Prometheus metrics expose per-image latency, batch size, and confidence distributions; logs include correlationId for all requests.

Severity Scoring & Thresholding

"As a triage manager, I want consistent severity scores with confidence so that I can route claims automatically and reduce subjective variance."

Description

Calculate a standardized severity score (0–100) for each detected region and roll up to image- and claim-level aggregates. Incorporate region size, density, and contextual cues to derive severity, and provide confidence intervals. Expose configurable thresholds per line of business to categorize Low/Medium/High severity and emit structured fields for triage and routing. Flag low-confidence or ambiguous results for review, persist scores with model version metadata, and ensure consistent outputs across retriggers and reprocessing.

Acceptance Criteria

Region-Level Severity Score 0–100 with Confidence Interval

Given a photo with at least one detected damage region and a fixed model version V and configuration C When severity scoring runs Then each region returns severity_score as an integer within [0,100] And each region returns ci_lower and ci_upper within [0,100] with ci_lower <= severity_score <= ci_upper And each region returns confidence within [0.0,1.0] And for a controlled test where region B has strictly greater area and damage density than region A in the same image, severity_score(B) > severity_score(A) And runtime completes within the SLO (p95 <= 500ms per image for scoring, excluding detection)

Image- and Claim-Level Severity Aggregation

Given an image with N >= 2 scored regions and their pixel areas When image-level aggregates are computed Then the image exposes aggregates: regions_count, severity_max, severity_mean, severity_area_weighted_mean And severity_mean = average of region severity_score rounded to 2 decimal places And severity_area_weighted_mean = sum(severity_score_i * area_i) / sum(area_i) rounded to 2 decimal places And the claim-level aggregates apply the same metrics across all images in the claim And all aggregate values are within [0,100] and are reproducible for identical inputs, model version, and configuration

LOB-Configurable Threshold Categorization (Low/Medium/High)

Given a Line of Business (LOB) L with thresholds defined in configuration C as ordered, non-overlapping ranges covering [0,100] When categorization runs for regions, images, and claims under L Then each entity receives a severity_category in {Low, Medium, High} based on its severity score and C And changing thresholds in C (without changing inputs or model version) changes categories accordingly on the next run And categorization decisions are logged with thresholds_set_id and effective range used

Structured Field Emission for Triage and Routing

Given scoring and categorization have completed for a claim When structured outputs are emitted via API and event stream Then per-region fields include: region_id, severity_score, ci_lower, ci_upper, confidence, severity_category, lob, thresholds_set_id And per-image fields include: image_id, regions_count, severity_max, severity_mean, severity_area_weighted_mean, severity_category, lob, thresholds_set_id And per-claim fields include: claim_id, images_count, severity_max, severity_mean, severity_area_weighted_mean, severity_category, lob, thresholds_set_id And all numeric fields have defined types and units, and enums use the canonical values {Low, Medium, High} And downstream routing rules can filter on severity_category and thresholds_set_id without additional transformation

Low-Confidence or Ambiguous Results Flagging for Review

Given configuration C defines min_confidence and max_ci_span and boundary_margin points around thresholds When a region's confidence < min_confidence OR (ci_upper - ci_lower) > max_ci_span Then region.review_required = true with reason code {LOW_CONFIDENCE or WIDE_CI} And when a region's severity_score lies within boundary_margin of any threshold, region.review_required = true with reason code AMBIGUOUS_BOUNDARY And if any region in an image is review_required, image.review_required = true; if any image is review_required, claim.review_required = true And all review flags and reason codes are present in emitted structured fields

Deterministic Outputs on Retriggers and Reprocessing

Given identical inputs (images and metadata), the same model version V, and the same configuration C When scoring is re-run (manual retrigger or automated reprocessing) Then all region scores, confidence intervals, aggregates, categories, and review flags are byte-for-byte identical to the prior run And the run records share the same determinism hash derived from inputs+V+C And if V or C changes, outputs may change but are associated to the new V or C and do not overwrite prior run outputs

Persistence with Model Version Metadata and Auditability

Given a claim has been scored When results are persisted Then each stored record (region, image, claim) includes: model_version, model_hash, config_version, thresholds_set_id, lob, run_id, scored_at (ISO 8601), and checksum And records are immutable; updates create a new run_id with a link to the prior run_id And retrieving by claim_id returns the latest run by default and supports filtering by run_id, model_version, and config_version And deletion policies (if any) do not allow partial removal that would break the audit trail

Parts Mapping & Affected Components Identification

"As a claims manager, I want the system to identify which parts are affected so that coverage and repair estimates can be initiated accurately."

Description

Map detected damage regions to affected parts using a maintained parts taxonomy and view/orientation classification. Support vehicle and property contexts, leveraging make/model metadata when available and falling back to generic component sets otherwise. Return a structured list of affected components with repairability indicators and coverage-relevant attributes to inform downstream estimating and policy checks.

Acceptance Criteria

Vehicle VIN-Based Mapping to Parts Taxonomy

Given a vehicle claim with VIN decoded to a supported make/model/year and photos showing a left-front bumper dent and a left headlamp crack When Severity Map processes the photos and orientation classification runs Then the response contains exactly 2 affected components mapped to the make/model-specific taxonomy with: - component 1 taxonomy_id for "Front bumper cover (left)" and orientation=front-left - component 2 taxonomy_id for "Headlamp assembly (left)" and orientation=front-left And for each component the fields are present: [taxonomy_id, component_name, orientation, parent_component_id, damage_types, severity_score (0–1), confidence (0–1), location_polygon, source_image_ids] And confidence >= 0.85 for each component And parent_component_id correctly links to the assembly parent And parts-mapping latency <= 2000 ms

Generic Fallback Without Make/Model Metadata

Given a vehicle claim where VIN is unavailable and make/model cannot be resolved, and photos show a right rear door dent When Severity Map processes the photos Then the response contains 1 affected component mapped to the generic taxonomy with: - taxonomy_source="generic" and taxonomy_version present - component_name="Rear door (right)" and orientation=rear-right - required fields present: [taxonomy_id, component_name, orientation, damage_types, severity_score, confidence, location_polygon, source_image_ids] And confidence >= 0.75 And the operation completes without error

Property Roof Hail Mapping and Quantification

Given a property claim with roof photos showing hail impacts on multiple slopes When Severity Map processes the photos and classifies roof orientations Then the response includes affected components for each slope with: - component_name="Roof covering" and orientation in {north, south, east, west} per slope detected - metrics present: area_in_sq_m, impact_density_per_sq_m, severity_score, confidence, location_polygon - repairability provided for each slope component And confidence >= 0.80 for each slope component And quantifications (area, density) are within ±5% of ground-truth for the test set

Repairability Indicator Rules Application

Given detected damages with computed measurements (dent_diameter_mm, crack_length_mm, hail_density_per_panel) and severity_score When the repairability engine evaluates each affected component Then the following rules are applied deterministically: - If dent_diameter_mm <= 30 and severity_score <= 0.50 then repairability="repair" - If crack_length_mm > 20 on headlamp or windshield then repairability="replace" - If hail_density_per_panel > 8 then repairability="replace" - Else if confidence < 0.60 then repairability="unknown" And each component includes repairability_reason_code from the defined enum And all components include the repairability field with one of {repair, replace, unknown}

Coverage-Relevant Attribute Tagging

Given components mapped for a claim including: a windshield crack near an ADAS camera, a front bumper crack impacting a radar bracket, and a cosmetic door dent When coverage-relevant attribute tagging runs Then attributes are set as follows: - Windshield component includes coverage_relevant_attributes containing ["glass", "ADAS", "safety-system-involved"] - Front bumper component includes coverage_relevant_attributes containing ["safety-system-involved", "sensor-mount"] - Door cosmetic dent includes coverage_relevant_attributes containing ["cosmetic-only", "exterior-body"] And each component in the response has at least 1 coverage_relevant_attribute from the controlled list

Uncertainty and Missing Views Task Surfacing

Given a claim lacking required critical views (e.g., no front-left quarter image) for a suspected left headlamp damage When parts mapping confidence for that component would be < 0.70 or orientation cannot be resolved Then the component entry includes needs_additional_view=true and missing_views containing ["Front-left quarter"] And the response includes checklist_tasks with an item titled "Request front-left quarter view" and priority="high" And no component with confidence < 0.70 is marked final=true

Aggregation and De-duplication Across Images

Given the same component damage is detected in multiple photos (overlapping location polygons and identical taxonomy_id) When aggregation runs Then the response contains a single component entry for that taxonomy_id with: - source_image_ids listing all contributing image IDs - damage_types de-duplicated (no repeated labels) - severity_score equal to the fused score and >= the maximum individual detection score And there are 0 duplicate component entries where location_overlap > 0.50 for the same taxonomy_id

Auto-Tagging & Structured Prefill

"As an intake specialist, I want fields to be pre-filled from photo analysis so that I spend less time on data entry and avoid errors."

Description

Automatically tag loss details and pre-fill structured claim intake fields (damage types, affected parts, severity categories, occurrence indicators) using outputs from the vision and scoring modules. Apply validation rules, support human-in-the-loop confirmations, and track changes with versioned write-backs to the ClaimFlow data model. Ensure idempotent updates, partial-field handling when some views are missing, and emit events/webhooks to trigger downstream workflows and checklists.

Acceptance Criteria

Map Vision Outputs to Structured Prefill

Given a claim with photos processed by the vision and scoring modules When auto-tagging executes Then damage_types, affected_parts, severity_category, and occurrence_indicator are prefilled per mapping rules from model outputs And each prefilled field stores source (cv|scoring), confidence (0.00–1.00), and timestamp And fields with confidence >= 0.85 are flagged review_required=false; fields with confidence < 0.85 are flagged review_required=true And only values from the controlled vocabulary are written to the data model

Validation Rules and Cross-Module Consistency

Given prefilled values are produced When validation runs Then values not in the controlled vocabulary are rejected with reason_code='invalid_code' and are not written And if severity from cv and scoring differ by more than 1 level, set status='conflict', suppress final write, and require review And if damage_types exist but affected_parts is empty, create warning reason_code='missing_affected_parts' and do not set severity_category And all validation outcomes are logged with correlation_id and rule_ids triggered

Human-in-the-Loop Confirmation and Edits

Given prefilled fields with review_required=true or status='conflict' When an adjuster confirms or edits values Then the system updates the fields, sets confirmed=true for each confirmed field, and clears review_required And the previous_value, new_value, user_id, and timestamp are recorded for each field And the system emits a 'prefill.updated' event including changed_fields and version

Versioned Write-Backs and Audit Trail

Given any automated prefill or human edit When data is written to the ClaimFlow model Then a new immutable version is created with sequential version_number and correlation_id And version metadata records actor (system|user), source (cv|scoring|human), and change_set And historical versions are retrievable and diffable by API for the claim

Idempotent Reprocessing Behavior

Given the same inputs (image set, model versions, mapping version) are processed again When auto-tagging is re-run Then no new version is created and no duplicate tags are written And values remain unchanged and a 'prefill.noop' event is emitted with idempotency_key And the operation completes within 2 seconds at P95 for idempotent runs

Partial-Field Handling and Missing Views Checklist

Given required photo views are configured (e.g., front, rear, roof) When one or more required views are missing Then only fields supported by available evidence are prefilled; unavailable fields remain null with reason='insufficient_evidence' And severity_category defaults to 'Unknown' until evidence is captured And a checklist task 'Capture Missing Views' is created and assigned to the claim

Webhook Emission for Downstream Triggers

Given a successful write-back or update occurs When the transaction commits Then a webhook is emitted within 5 seconds with event_type ('prefill.completed'|'prefill.updated'|'prefill.noop'), claim_id, version_number, changed_fields, and correlation_id And non-2xx responses are retried up to 3 times with exponential backoff (1s, 3s, 9s) And failed deliveries are recorded with last_attempt_at and next_attempt_at

Missing Views Detection & Capture Checklist

"As an adjuster, I want the system to tell me which required photos are missing or unusable so that I can request them early and prevent rework."

Description

Detect the presence and quality of critical photographic views (e.g., front, rear, left, right, interior, VIN) using a view/quality classifier. When required views are missing, low quality, or occluded, surface a dynamic capture checklist within ClaimFlow and initiate automated requests for additional images via existing communication channels. Apply configurable business rules to block or defer auto-triage until mandatory views are provided, and update status in real time as new photos arrive.

Acceptance Criteria

Real-time Required View Detection on Photo Upload

Given an auto claim with 1–30 uploaded photos, When processing completes, Then each photo is classified into {front, rear, left, right, interior, VIN, unknown} with a confidence score 0–1 and ≥90% top-1 precision on the validation set. Given required views are configured as {front, rear, left, right, interior, VIN}, When classification completes, Then a checklist marks each required view Complete if any photo for that view has confidence ≥0.80; otherwise Missing. Given a detected view has occlusion score ≥0.60 or coverage <70% of the target area, When assessed, Then the view state is Low Quality (not Complete). Given processing starts, When photos are uploaded, Then the initial checklist is generated within 3 seconds at the 95th percentile. Given additional photos are uploaded in the same claim, When they arrive, Then re-evaluation runs automatically without user action.

Image Quality Classification and Reason Codes

Given any uploaded photo, When evaluated, Then the system computes blur, glare, exposure, framing, and occlusion metrics (0–1) and an overall quality score (0–1). Given overall quality <0.75 or any metric threshold is breached (blur>0.60 OR glare>0.60 OR exposure∉[0.30,0.70] OR framing<0.70), When mapped to a required view, Then the view is labeled Low Quality and reason codes for each failing metric are attached. Given multiple photos map to the same view, When at least one photo meets all thresholds, Then the view is marked Complete and low-quality photos do not block triage. Given a Low Quality label is shown, When rendered in the checklist, Then user-visible reason codes and a short guidance string (≤120 chars) are displayed for that view.

Dynamic Capture Checklist UI Updates

Given the capture checklist is visible in ClaimFlow, When any new photo for the claim is uploaded by claimant or internal user, Then the checklist updates in real time within 5 seconds and each item transitions among {Missing, Low Quality, Complete} as appropriate. Given all mandatory checklist items are Complete, When the last item transitions to Complete, Then a status banner indicates All required views received within 2 seconds. Given optional views are configured, When they are Missing, Then they are listed under Optional and do not prevent auto-triage. Given any checklist state change, When it occurs, Then an event is logged with {claimId, viewType, priorState, newState, timestamp, actor} for auditability.

Automated Additional Image Requests via Existing Channels

Given one or more mandatory views are Missing or Low Quality, When the checklist state is evaluated, Then an outbound request is sent via the claimant’s preferred available channel (in-app chat if active; else SMS; else email) within 60 seconds. Given the outbound request is sent, When delivered, Then it enumerates missing/low-quality views, includes per-view capture guidance links, and provides a secure upload link bound to the claim with a token expiring in 7 days. Given a request was sent in the last 12 hours and no new missing items were added, When re-evaluated, Then no duplicate request is sent (de-duplication enforced). Given photos are submitted via the secure link, When received, Then they are auto-associated with the claim and trigger immediate re-evaluation without manual routing. Given any outbound request, When logged, Then an audit record is created with {channel, templateId, recipient, sendTimestamp, deliveryStatus, claimId}.

Business Rule Gate for Auto-Triage Based on Mandatory Views

Given business rules require mandatory views to be Complete, When any mandatory view is Missing or Low Quality, Then auto-triage is set to Deferred with reason Missing mandatory views and triage tasks do not advance. Given LOB/claim-type-specific required views are configured by an admin, When configuration changes are published, Then new claims use the updated set while existing claims retain a snapshot unless a supervisor manually refreshes configuration. Given all mandatory views become Complete, When re-evaluated, Then auto-triage automatically resumes within 5 seconds and downstream tasks are enqueued. Given a supervisor with Override permission, When an override is applied with reason code and comment, Then triage may proceed despite missing items and the override is recorded in the audit log with userId and timestamp.

VIN View Handling and Alternative Paths

Given auto physical damage claims where VIN is mandatory, When evaluating the checklist, Then VIN can be satisfied by a VIN photo or by manual VIN entry plus supporting photo if rules allow. Given a VIN photo is provided, When OCR quality ≥0.85 and check-digit validation passes, Then VIN is marked Complete and the VIN field is auto-filled; otherwise the view is Low Quality with reason OCR_LOW_CONFIDENCE or CHECKDIGIT_FAIL. Given manual VIN entry is permitted, When the user enters a VIN, Then a supporting photo is required and VIN is marked Complete only if the photo OCR ≥0.80 or a supervisor approves an override with a reason code. Given multiple VIN photos exist, When any meets thresholds, Then accept the best and suppress further VIN requests for this claim.

Reviewer Annotations, Overrides & Audit Trail

"As a senior adjuster, I want to correct the system’s annotations and document why so that final assessments are accurate and auditable."

Description

Provide an interactive UI overlay that displays model-drawn polygons, labels, affected parts, and severity scores. Enable reviewers to edit polygons, relabel damage types, and override severity with reason codes. Persist all edits with user identity, timestamps, and before/after diffs, and automatically reapply prefill and workflow triggers after changes. Store model-versus-human outcomes for QA, reporting, and future model retraining.

Acceptance Criteria

Polygon Editing and Geometry Controls

- Given an image with model-drawn damage polygons visible, when a reviewer selects a polygon, then its vertices become draggable and the shape can be adjusted with a snap tolerance of <=5px and a minimum vertex spacing of >=3px. - Given an active polygon, when the reviewer drags a vertex or edge and releases, then the new geometry renders within 100ms and remains within image bounds. - Given the need to add a new polygon, when the reviewer completes drawing, then the polygon auto-closes, validates as non-self-intersecting, and displays pixel-area and perimeter. - Given a polygon, when the reviewer deletes it, then a confirmation modal appears, and on confirm the polygon is removed and an audit entry is recorded with the original geometry. - Given ongoing edits, when no further changes occur for 2s, then the system autosaves the current state and persists it across page reload; an undo/redo history of at least 20 steps is available per image.

Damage Type Relabel With Reason Codes

- Given a selected polygon, when a reviewer changes the damage type, then they must choose a reason code from a controlled list [Misclassification, Ambiguous, Edge Case, Customer Info, Other] and may enter optional notes up to 300 characters. - Given a successful relabel, then the polygon's label and color update immediately, the item is marked "Reviewer-updated", and the change is saved within 2s. - Given the relabel action, then the audit trail captures {oldType, newType, reasonCode, notes, userId, timestamp} and the UI shows a toast confirming save. - Given an invalid type, when the reviewer attempts to submit a label outside the allowed set [dent, crack, hail, rust, other], then validation prevents save and displays an inline error.

Severity Override and Validation

- Given a polygon with a model severity score, when a reviewer overrides the score, then the input accepts integers 0–100 inclusive, requires a reason code, and optionally allows notes up to 300 characters. - Given a valid override submission, then the displayed severity updates, dependent UI (badges, heatmap) refreshes within 200ms, and the change is autosaved within 2s. - Given an override, then the system records {oldScore, newScore, reasonCode, notes, userId, timestamp} in the audit trail. - Given an invalid value (e.g., blank, >100, <0, non-integer), when submitted, then the system blocks save and displays a descriptive validation message.

Immutable Audit Trail With Before/After Diffs

- Given any change to polygons, labels, affected parts, or severity, when the change is saved, then the system writes an immutable audit entry containing before/after values, userId, userRole, sessionId, ipAddress, and ISO-8601 UTC timestamp. - Given a geometry change, then the audit entry stores vertex arrays for old and new polygons and a computed delta (added/removed/moved vertices count). - Given the audit log view, when a reviewer opens it, then entries display in reverse-chronological order with filters by user, field, action type, date range, and can be exported as CSV and JSON. - Given access control policies, when a user without Audit permissions attempts to view logs, then access is denied with a 403 message and no data is returned.

Reapply Prefill and Workflow Triggers After Edits

- Given a saved reviewer change, when the change is persisted, then the system recalculates prefilled structured fields and re-runs workflow rules within 3s, updating tasks accordingly. - Given existing tasks affected by changes, when rules are re-evaluated, then obsolete tasks are canceled, new required tasks are created, and updated tasks retain their IDs; no duplicate tasks are created. - Given rule reapplication, then a banner "Prefill and workflows updated" appears within 5s and the audit trail records a "Rules re-evaluated" entry with outcome summary. - Given errors during rule reapplication, then the UI shows a non-blocking error message, retries up to 3 times with exponential backoff, and preserves the user's edits.

Store Model vs Human Outcomes for QA and Retraining

- Given an image processed by the model, when a reviewer finalizes edits, then the system stores paired records of model predictions and final human-adjusted outcomes with modelVersion, dataVersion, and claimId. - Given stored outcomes, when a QA export is requested for a date range, then the system produces a file within 60s containing per-polygon fields {part, type, severity_model, severity_final, iou_geom, accepted:boolean} and aggregate metrics. - Given a retraining flag enabled, when outcomes are stored, then they are partitioned into a retraining dataset bucket with PII-free media references and checksum validation. - Given no reviewer changes, then the system still records an explicit "accepted as-is" outcome for each polygon.

Concurrent Edit Handling and Versioning

- Given two reviewers editing the same image, when non-conflicting fields are changed concurrently (e.g., different polygons), then changes merge automatically and both users see updates within 2s. - Given conflicting edits to the same polygon, when a save occurs, then the second saver is presented a conflict dialog showing a diff (before, theirs, others) and must choose to overwrite, merge, or cancel. - Given any conflict resolution, then no data is lost, an audit entry records both attempted changes and the chosen resolution, and a new versionId is assigned. - Given optimistic concurrency control, when a stale version is submitted without resolution, then the system rejects with 409 and provides an option to reload latest.

Proof Seal

Each capture is sealed with a cryptographic timestamp, GPS/geofence validation, and a tamper-evident hash embedded in EXIF and the claim record. Provides chain-of-custody transparency for auditors and SIU while respecting privacy controls. Delivers instant audit readiness and stronger fraud defense.

Requirements

Cryptographic Timestamp Seal

"As an SIU analyst, I want machine-verifiable capture timestamps so that I can prove exactly when evidence was created during investigations and audits."

Description

At the moment of media capture or message ingestion, generate a cryptographic timestamp using a trusted time source (e.g., RFC 3161 TSA or cloud KMS-signed time). Bind the timestamp to the media’s content hash and a client nonce, embed the timestamp token in EXIF/XMP and store it within the claim record. Support clock-skew attestation, replay protection, and server-side fallback sealing when offline (with explicit disclosure). Expose verification results in the UI, rules engine, and export packages so SIU and auditors can machine-verify when evidence was created.

Acceptance Criteria

Real-time TSA Timestamp at Capture

Given a connected device or ingestion service with network access When media is captured or a message is ingested Then the system requests a timestamp from a configured trusted time source (RFC 3161 TSA or cloud KMS-signed time) And returns a signed timestamp token within 3 seconds at P95 (10 seconds absolute timeout) And persists the token ID, source, and signing cert chain reference with the claim asset And logs failures with error codes and retries up to 2 times before surfacing a user-visible error

Binding Timestamp to Content Hash and Client Nonce

Given an immutable byte stream of the captured media or message content When sealing is initiated Then the system computes a SHA-256 content hash over the exact bytes stored And generates a 128-bit cryptographically secure random client nonce unique per asset And includes both the content hash and nonce in the data that is signed by the TSA/KMS And verification fails if any bit of content or the nonce changes (hash or nonce mismatch) And the claim record stores {hash_algorithm:"SHA-256", content_hash, client_nonce, time_source, signing_key_id}

Embedding Timestamp Token in EXIF/XMP and Claim Record

Given a successfully sealed image/video format supporting EXIF/XMP When the seal is produced Then the system embeds the timestamp token and related fields into metadata (EXIF/XMP) without altering pixel/audio data And the same token bytes are stored in the claim record And for formats without EXIF/XMP support, a sidecar XMP/JSON is created and linked in the claim record And a subsequent metadata read yields an identical token to the claim record value And any write attempt that would change pixels/audio after sealing results in a new hash and requires a new seal

Offline Capture with Server-side Fallback Sealing and Disclosure

Given a device is offline at the moment of capture When media is captured Then the client computes and stores SHA-256(content) and a 128-bit nonce locally with monotonic capture time And upon reconnect within 24 hours the server obtains a trusted timestamp and seals using the stored hash and nonce And the UI clearly discloses "Sealed after upload (offline)" on the asset And the claim record sets offline_seal=true and records offline_duration And rules and exports expose offline_seal so auditors can detect fallback cases

Clock-Skew Attestation and Drift Thresholds

Given device-reported capture time and a trusted time at sealing When the seal is produced Then the system computes clock_skew_seconds = trusted_time - device_time And marks status "Clock OK" if |skew| <= 120 seconds, else "Clock Skew" with the absolute skew value And persists clock_skew_seconds and status in the claim record and export payloads And the UI displays a visible badge reflecting the status And the rules engine can evaluate clock_skew_seconds and status for decisions

Replay Protection for Nonce and Token Reuse

Given a 128-bit client nonce must be unique per asset When an incoming seal request reuses an existing nonce within 30 days or a timestamp token appears in multiple claims/assets Then the system flags a replay event, prevents silent reuse, and records an audit entry with severity=high And the rules engine exposes a replay_detected=true signal And the UI displays a warning on affected assets And exports include a replay_detected flag

Verification Exposure in UI, Rules Engine, and Exports

Given a sealed asset in a claim When verification is executed on-demand or during nightly jobs Then cryptographic verification validates the TSA/KMS signature, cert chain, token integrity, and hash binding And the UI shows a tri-state status (Verified/Warning/Failed) with reason codes And the rules engine exposes fields: timestamp_verified, verification_reason_code, time_source, signing_key_id, clock_skew_seconds, offline_seal, replay_detected, content_hash And export packages include the raw token, verification report JSON, and the intermediate certs required for offline verification

Location Integrity & Geofence Validation

"As a claims manager, I want captures validated against the loss-site geofence so that I can trust the evidence was collected at the correct location."

Description

Capture high-fidelity location evidence (GNSS fix, accuracy radius, altitude, speed, satellite count, and provider) at the instant of capture. Validate position against claim-configured geofences (e.g., loss address radius) with configurable tolerances and time windows. Detect and flag spoofing signals (mock location, emulator, rooted/jailbroken devices, VPN anomalies) and record the validation verdict with reasons. Embed signed location payload in EXIF/XMP and the claim record. Provide privacy-safe modes (e.g., share externally with coarse location or detached proof) while maintaining verifiability.

Acceptance Criteria

Capture GNSS Metadata at Moment of Capture

Given a device with location permissions granted and GNSS available When a photo is captured in ClaimFlow Then the location fix is taken at most 300 ms from the shutter event And the payload includes latitude, longitude, horizontalAccuracy(m), altitude(m), speed(m/s), satelliteCount, provider, and captureTimestamp(UTC) And satelliteCount >= 4 and provider = "GNSS" for a GNSS-classified fix And if horizontalAccuracy > configured.maxAccuracyMeters, the record is saved and flagged with reason "LowAccuracy" And if provider != "GNSS", the record is saved and flagged with reason "NonGNSSProvider" And if no fix is available, the payload records provider = "Unavailable" and reason "NoFix"

Geofence Validation Against Loss Location

Given a claim geofence configured with center(lat,lon), radius R meters, tolerance T meters, and a time window W When a capture occurs with location fix L at timestamp t Then the system computes distance d from L to center And if t ∈ W and d <= R, verdict = "InsideGeofence" And if t ∈ W and R < d <= R + T, verdict = "BorderlineOutOfGeofence" And if t ∈ W and d > R + T, verdict = "OutsideGeofence" And if t ∉ W, verdict = "OutsideTimeWindow" And the computed d, R, T, W, and t are persisted with the verdict

Spoofing and Device Integrity Detection

Given device integrity and network anomaly checks are enabled When a capture occurs Then the system detects and flags any of: MockLocation, EmulatorSimulator, RootJailbreak, VPNCountryMismatch, OSAttestationFail And if any indicator is present, verdictSpoofing = "Suspected" and reasonCodes include the detected indicators And if no indicators are present, verdictSpoofing = "Clear" And spoofing verdict and reasonCodes are persisted in the claim record

Signed Location Payload Embedded in EXIF/XMP and Claim Record

Given a successful capture and validation When the photo file is written Then EXIF/XMP contains a signed location payload with fields: lat, lon, accuracy, altitude, speed, satelliteCount, provider, captureTimestamp, geofenceVerdict, spoofingVerdict, reasonCodes, payloadHash And the payload is signed (JWS/COSE) using the platform private key and includes the signing certificate chain identifier And verification with the corresponding public key succeeds and matches the payload stored in the claim record And any byte-level modification of EXIF/XMP causes signature verification to fail And the claim record stores payloadHash equal to the hash embedded in EXIF/XMP

Coarse Location Share Mode (Privacy-Safe)

Given an external share is initiated with Privacy Mode = "Coarse Location" and gridSize S meters (default 1000) When the share package is generated Then the shared coordinates are rounded to the nearest grid cell of size S and precise lat/lon are not exposed And the package includes a verifiable proof that the original precise location was within the configured geofence at capture time And external verification of the proof succeeds without access to the precise coordinates And the claim record logs that Privacy Mode = "Coarse Location" was applied with S

Detached Proof of Location Without Disclosing Coordinates

Given an external share is initiated with Privacy Mode = "Detached Proof" When the share package is generated Then the exported media contains no EXIF/XMP location coordinates And a separate proof object contains a signature over the canonical payload hash and geofence parameters And external verification confirms the geofence verdict and timestamp while revealing no precise coordinates And any tampering with either media or proof invalidates verification And the claim record associates the proof object ID with the capture

Content Hash & EXIF Embed

"As an auditor, I want a tamper-evident hash embedded in each file so that I can independently confirm the media has not been altered."

Description

Compute a SHA-256 content hash of each media asset at capture (and a frame chain for bursts/videos), then sign and embed the proof into EXIF/XMP while storing a canonical copy in the claim record. Reject or flag uploads whose recomputed hash does not match the sealed value. Present a tamper status indicator in ClaimFlow (Verified/Warning/Failed) and expose verification via API for downstream systems. Ensure transformations (thumbnails, transcodes) are recorded as derived artifacts with their own hashes linked to the original.

Acceptance Criteria

On-Capture SHA-256 Hashing and Signed EXIF/XMP Embed

Given a media asset is captured via the ClaimFlow capture SDK on a supported device When the asset is finalized prior to any transformation or metadata write Then the system computes a SHA-256 content hash over the original media bytes And generates an RFC 3339 UTC capture timestamp And signs the tuple {assetId, contentHash, timestamp, deviceId, appVersion} with the platform signing key (Ed25519) And embeds contentHash, signature, timestamp, and algorithm identifiers into EXIF/XMP fields And persists the same tuple in the claim record as the asset’s canonical proof And the operation succeeds only if both embed and claim record writes succeed atomically

Upload Verification and Tamper Status Indicator

Given an asset file is uploaded or re-ingested into ClaimFlow When the system recomputes the content hash using the same algorithm used at capture Then if the recomputed hash equals the sealed contentHash and the signature verifies, the asset status is set to Verified and stored And if the sealed proof is absent but a canonical proof exists in the claim record and the recomputed hash matches it, the status is set to Warning And if the recomputed hash does not match the sealed value or the signature verification fails, the status is set to Failed and the upload is rejected with HTTP 422 and a user-visible error And the UI tamper indicator displays Verified (green), Warning (amber), or Failed (red) consistently with the stored status

Derived Artifact Hashing and Provenance Linkage

Given the system generates a thumbnail or transcode from an original asset When the derived artifact is produced Then the system computes a SHA-256 hash for the derived artifact And records a derived artifact entry with {derivedId, originalAssetId, type, parameters, hash, createdAt} And links the derived artifact to the original asset in the claim record And the derived artifact’s creation does not alter the original asset’s tamper status And verification of a derived artifact returns its own status while referencing originalAssetId

Verification API for Downstream Systems

Given a downstream system calls the verification API When GET /api/v1/proof/verify?assetId={id} is invoked by an authorized client Then the API responds 200 within 300 ms p95 with JSON {assetId, status, contentHash, signatureValid, timestamp, hashAlgorithm, signatureAlgorithm, derivedOf} And when POST /api/v1/proof/verify with a media file is invoked, the API responds with the same schema and does not persist the file And unauthorized requests receive 401, unknown assets 404, and invalid payloads 400 And the API signatureValid field equals true only if the embedded signature verifies against the platform public key

Burst/Video Frame-Chain Hashing and Validation

Given a burst photo sequence or video is captured When frame hashing is performed Then the system computes per-frame hashes and a forward chain where Hn = SHA-256(Hn-1 || frameHash_n) with H0 initialized to all zeros And embeds chainHead and chainTail identifiers in XMP along with chainAlgorithm And persists the frame count and chainTail in the claim record And verification detects any dropped, inserted, or reordered frames by chain breakage and sets status to Failed

Canonical Proof Storage and Idempotency

Given an asset with an existing canonical proof is re-uploaded or referenced When the recomputed content hash matches the stored canonical hash Then the system does not create a new canonical proof record (idempotent) And returns HTTP 200 with the existing assetId and status Verified And any attempt to mutate canonical proof fields is rejected with HTTP 409 and no change persisted

Tamper Events, Rejection Policy, and Audit Logging

Given an upload results in status Failed or Warning When the result is stored Then an immutable audit event is written with {assetId, status, reason, detectedAt, actorId, sourceIp} And notifications are emitted to the claim’s activity feed and SIU webhook if status is Failed And the UI must surface a View Proof panel showing contentHash, timestamp, signatureValid, and any mismatch reasons

Immutable Chain-of-Custody Ledger

"As a compliance officer, I want an immutable, exportable chain of custody so that our organization can demonstrate end-to-end evidence integrity to regulators and auditors."

Description

Maintain an append-only, signed ledger of all custody events for sealed evidence (capture, upload, validations, transformations, views, shares, exports, and deletions with legal holds). Store event digests in write-once storage (e.g., S3 Object Lock/WORM) with periodic checkpointing. Link ledger entries to the claim, task, and user identity. Provide filtered audit views, exportable reports, and API access for SIU and compliance, ensuring traceability across automated workflows and external sharing.

Acceptance Criteria

Append-Only Ledger with WORM Enforcement

Given a sealed evidence item and custody event types {capture, upload, validation.gps_geofence, validation.timestamp, transformation, view, share.created, share.accessed, export.created, legal_hold.applied, legal_hold.released, deletion.requested, deletion.executed} When any custody event occurs Then the system appends a new ledger entry with fields {sequence_index, event_type, timestamp, actor_id, actor_type, claim_id, task_id (nullable), evidence_id, payload_digest, prev_entry_hash, entry_hash} And the entry is persisted to write-once storage with Object Lock retention equal to the configured retention policy And attempts to modify or delete any existing ledger entry are rejected with 409/Forbidden and a write.rejected audit event is recorded And retrieval of the entry returns the same entry_hash as persisted

Cryptographic Signing, Trusted Timestamps, and Identity Linkage

Given a ledger entry is created Then the entry is signed with Ed25519 using a managed platform key and includes signer_key_id And the RFC3339 timestamp is sourced from a trusted time authority with drift <= 2 seconds from system time And signature verification using the published public key succeeds for 100% of entries And actor_identity is resolved via IdP, recording {actor_type in [user, service], subject_id, tenant_id, mfa=true/false, client_id (if service)} And within a single claim, timestamps are non-decreasing and sequence_index increments by 1 And for capture/upload events, payload_digest equals the tamper-evident hash embedded in the sealed file’s EXIF; for transformations, parent_digest and child_digest are recorded And when an event occurs within a workflow task, task_id and workflow identifiers are populated

Periodic Checkpointing and End-to-End Chain Verification

Given ongoing ledger activity When either 100 entries have been appended since the last checkpoint or 15 minutes have elapsed (whichever comes first) Then a checkpoint is created with a Merkle root over entries since the previous checkpoint, signed, and stored in WORM with Object Lock And GET /ledger/checkpoints returns the latest checkpoint with {root, span_start, span_end, created_at, signature} And POST /ledger/verify over the full chain up to the latest checkpoint returns status=valid And verification of a chain of 10,000 entries completes in <= 3 seconds on the reference dataset

Audit Views: Filtering, Pagination, and Export

Given a user with role Audit.View When they filter by {claim_id, evidence_id, event_type, actor_id, date_range} Then results are correctly filtered and returned with cursor-based pagination and total_count And the first page returns in <= 2 seconds for up to 50,000 matching events And exporting the filtered result yields CSV and JSON within 60 seconds including all entries, a verification manifest (Merkle root and entry hashes), and a detached signature And PII fields are masked per tenant privacy policy unless the user has scope Audit.PII And all view and export actions are themselves recorded as ledger events

Ledger API: Endpoints, Access Control, and Performance

Given authenticated API clients with scopes {Ledger.Read, Ledger.Verify} When they call GET /claims/{id}/ledger, GET /evidence/{id}/ledger, GET /ledger?filters=..., GET /ledger/checkpoints, and POST /ledger/verify with supported filters and pagination Then responses conform to the OpenAPI schema and include ETag headers and Next-Cursor tokens where applicable And requests beyond 1,000/minute per token receive 429 with Retry-After And p95 latency <= 300 ms and p99 latency <= 800 ms at 1M total entries under nominal load And unauthorized or insufficient-scope access attempts return 401/403 and are recorded as ledger events

External Sharing Traceability

Given an evidence item is shared externally via link or integration When the share is created, accessed, revoked, or expires Then ledger events {share.created, share.accessed, share.revoked, share.expired} are recorded with fields {share_id, recipient_hint, token_fingerprint, ip, user_agent, timestamp, actor_id (if authenticated)} And access denials due to expiry or revocation record share.access_denied with reason And exports of the evidence include the complete external share event trail

Legal Hold and Deletion Semantics

Given an evidence item under legal hold When a deletion is requested Then the deletion is blocked, API returns 423 Locked, and legal_hold.blocked_deletion is recorded And when legal hold is released and retention policy permits, deletion.executed is recorded, the content is purged, and the ledger retains payload_digest and a deletion receipt And no ledger entries are removed; attempts to remove entries are rejected with 409/Forbidden and audited

Privacy Controls & Redacted Sharing

"As a privacy officer, I want to control what metadata is shared while keeping the proof intact so that we comply with privacy regulations without weakening fraud defenses."

Description

Offer tenant-level policies to minimize exposed metadata while preserving verifiability. Support configurable EXIF fields, detached signatures/sidecar proofs for external recipients, and automatic stripping of non-proof metadata on share. Provide redaction workflows (manual or automated) for PII-sensitive regions while keeping the original sealed copy under restricted access. Enforce retention schedules, consent flags, and access scopes; log all disclosures in the custody ledger.

Acceptance Criteria

Tenant Policy: Configurable EXIF Whitelist

Given a tenant admin configures an EXIF whitelist at the tenant level, When a new capture is sealed, Then only whitelisted EXIF fields are embedded alongside the tamper-evident proof and all non-whitelisted fields are excluded. Given a tenant admin updates the whitelist policy, When the policy is saved, Then subsequent captures conform to the new whitelist without altering any existing sealed originals. Given application defaults conflict with tenant policy, When both are present, Then the tenant policy overrides the defaults. Given invalid EXIF keys are submitted, When the admin attempts to save the whitelist, Then the system rejects the save and displays validation errors identifying unknown or unsupported keys.

External Share: Detached Sidecar Proof

Given a user initiates an external share, When share mode "Proof-only sidecar" is selected, Then the exported media has non-proof metadata stripped and a detached sidecar contains the cryptographic timestamp, GPS/geofence validation result, content hash, and signature. Given an external recipient downloads the files, When the sidecar is verified using the tenant public key, Then verification succeeds and matches the original seal recorded in the claim. Given separate shares to different recipients, When links are generated, Then recipient-specific access tokens do not change the media content hash or the sidecar signature. Given the share completes, When the files are delivered, Then the custody ledger records recipient identity (or alias), timestamp, scope, and sidecar checksum.

Auto-Strip Non-Proof Metadata on Share

Given a share to an external domain, When the file is prepared, Then all non-proof EXIF/IPTC/XMP fields are removed per tenant policy while preserving pixels and proof-required fields. Given an internal share within the same tenant, When policy allows full metadata internally, Then no stripping occurs and this decision is logged. Given a field is marked retain-for-legal in tenant policy, When exporting externally, Then that field is preserved and its retention basis is noted in the ledger. Given a file is exported and re-imported, When inspected, Then ClaimFlow has not added any new non-proof metadata fields.

Redaction Workflow: PII Regions with Sealed Original Retention

Given a reviewer enters redaction mode, When PII regions are auto-detected or manually selected, Then a redacted derivative is created and the sealed original remains unchanged under restricted access. Given the redacted derivative is saved, When persisted, Then the system stores a derivative hash and a link to the original seal/hash for provenance. Given a user without "View Original" permission requests the asset, When access is evaluated, Then only the redacted derivative is delivered and the access attempt to the original is logged. Given an SIU role requests temporary original access, When dual authorization is completed, Then time-bound access is granted and auto-revoked per policy.

Consent Flags and Access Scope Enforcement

Given a capture is flagged "consent required" and consent is pending, When an external share is attempted, Then the share is blocked with a reason code until consent is recorded or an approved override occurs. Given consent documentation is uploaded and verified, When the flag is updated, Then allowed share channels per consent scope are enabled and recorded. Given access scopes are defined on the claim, When a user outside scope attempts view or share, Then the action is denied with a 403 (API) or UI error and the attempt is logged with user, scope, and resource. Given an API token lacks required scopes, When protected endpoints are called, Then no data is returned and the response is 403 with a correlation ID.

Retention Schedule Enforcement and Purge

Given a tenant retention schedule exists, When a sealed capture exceeds its retention period and no legal hold applies, Then derivatives and share artifacts are purged, the original sealed proof is destroyed per policy, and a minimal tombstone remains. Given a legal hold is active on a claim, When the retention period elapses, Then no purge occurs and the hold is visible in admin reports with reason and requester. Given a scheduled purge job runs, When deletion completes, Then the custody ledger records purge events including asset identifiers, checksums, and timestamps. Given a retention policy is shortened, When saved, Then the change applies prospectively and items nearing purge are flagged in a dashboard with expected purge dates.

Custody Ledger: Comprehensive Disclosure Logging

Given any disclosure event (view, download, share, API retrieval) occurs, When it is processed, Then the ledger records actor, timestamp, action, recipient (or alias), claim ID, access scope, consent status, and checksums for media and sidecars. Given a ledger integrity check is run, When the hash chain is verified, Then no tampering is detected and any gaps or anomalies are reported with affected ranges. Given an auditor queries by claim and time range, When results are requested, Then the system returns complete results within 3 seconds for up to 10k events and supports CSV export. Given privacy export settings are enabled, When logs are exported, Then PII fields are minimized or tokenized per policy while preserving auditability of events.

Self-Serve Proof Verification

"As an external auditor or opposing counsel, I want to independently verify the authenticity of evidence so that I do not need privileged access to ClaimFlow to trust the proof."

Description

Provide a read-only verification portal and API where recipients can upload a file or enter a claim link to validate timestamps, hashes, signatures, and geofence results without accessing internal systems. Generate a human-readable verification report and a downloadable verification bundle (media, sidecar JSON, signatures, proofs). Embed a QR code or link in shared artifacts that resolves to the verification page. Expose SDKs for partners to integrate verification into their systems.

Acceptance Criteria

Public Verification Portal: File Upload Validation

Given an unauthenticated visitor on the public verification portal When they upload a sealed media file Then the system validates the cryptographic timestamp, tamper-evident hash, embedded signature, and geofence constraints And the portal displays per-check results as Pass or Fail with machine-readable error codes for any failures And no login or access to internal ClaimFlow systems is required to view results And no internal claim data beyond artifact-level verification metadata is exposed And the operation performs read-only processing with no mutation of internal records And p95 response time for files up to 50 MB is ≤ 5 seconds; for files > 50 MB and ≤ 200 MB is ≤ 30 seconds

Claim Link and QR Code Resolution

Given a recipient scans an embedded QR code or opens a verification link from a shared artifact When the token in the link is valid and not expired or revoked Then the verification page loads over HTTPS and displays the artifact’s verification results without requiring authentication And the page shows the artifact identifier, verification timestamp, and per-check outcomes And invalid, expired, or revoked tokens return a 410 Gone response with no artifact data And HSTS is enabled and all HTTP requests are redirected to HTTPS

Human-Readable Verification Report Generation

Given verification results exist for an artifact When the user requests a verification report Then a human-readable report is generated in HTML and PDF including: artifact identifier, verification transaction ID, ISO 8601 UTC verification timestamp, hash algorithm and digest, timestamp proof reference, signature verification outcome, and geofence evaluation outcome And the report contains a QR code/link back to the verification page and a SHA-256 checksum of the report file And the report download begins within 3 seconds at p95 for artifacts up to 50 MB

Verification Bundle Packaging and Integrity

Given verification results exist for an artifact When the user downloads the verification bundle Then the bundle contains: original media bytes, a sidecar JSON with normalized metadata and per-check outcomes, proof/signature files, and a manifest listing filenames with SHA-256 checksums And the bundle is signed or accompanied by a detached signature with a public key reference And the download URL is pre-signed, single-use, and expires within 24 hours And all files in the bundle match the manifest checksums upon re-verification

Verification API and Partner SDKs

Given a partner possesses a valid API key and submits an HMAC-signed request When they call the verification API with a file upload or artifact ID Then the API returns 202 Accepted with a job ID for asynchronous processing or 200 OK with results for synchronous validation, and appropriate 4xx/5xx with machine-readable error codes on failure And the JSON schema includes per-check status (Pass/Fail), failure reasons, algorithms used, and reproducible digests And idempotency is honored using an Idempotency-Key header with identical results for retries within 24 hours And official SDKs for JavaScript, Python, and Java expose methods to submit files, poll results, and download bundles with typed responses and working examples

Privacy, Read-Only Access, and Auditability

Given any portal or API verification action When verification is executed Then only verification metadata and outcomes are returned; no PII or internal claim notes are exposed And the verification workflow is strictly read-only with no changes to internal claim records And an audit log entry is recorded including timestamp, artifact identifier, requester IP, user agent, result summary, and correlation ID And rate limiting of 60 requests per minute per IP is enforced with 429 responses and a Retry-After header when exceeded

Tenant-Scoped Key Management

"As a security administrator, I want isolated, rotating signing keys per tenant so that compromise is contained and long-term validation remains possible."

Description

Use per-tenant keys for signing and sealing operations managed by cloud KMS/HSM with least-privilege access. Support key rotation, revocation, and BYOK, with auditable logs and alerting on key events. Enforce modern algorithms (e.g., ECDSA P-256/Ed25519) and store key identifiers with each proof to enable long-term validation and crypto agility. Provide break-glass procedures and migration tooling for re-sealing if keys are compromised.

Acceptance Criteria

Per-Tenant Signing Key Isolation

Given tenants A and B exist and both have active signing keys When Tenant A submits a sealing request Then the KMS operation uses a key scoped to Tenant A (key_id prefix/ARN contains Tenant A identifier) And any attempt by Tenant A to use Tenant B’s key returns 403/AccessDenied and is logged And across a batch of 1,000 parallel sign operations over 10 tenants, zero operations use a cross-tenant key And median signing latency <= 150 ms and p95 <= 400 ms per request

Key Rotation Without Service Interruption

Given Tenant A schedules a rotation from k_old to k_new When rotation is activated Then new seals begin using k_new within 60 seconds of activation And seals created before activation verify with k_old; seals after activation verify with k_new, based on embedded key_id And audit events KEY_ROTATION_INITIATED and KEY_ROTATION_COMPLETED are written with tenant_id, old_key_id, new_key_id, actor, timestamps And during the rotation window, sign failure rate <= 0.1% with up to 3 automatic retries per failure

Key Revocation and Seal Verification Behavior

Given a tenant key is revoked/disabled When a sealing request references the revoked key Then the operation is blocked, no seal is produced, and error code KEY_REVOKED is returned And existing seals created prior to revocation continue to verify successfully; verification returns status signature_valid=true and key_status=revoked And an audit event KEY_REVOKED is emitted with tenant_id, key_id, reason, actor, timestamp And an alert is sent to the tenant’s configured channel within 60 seconds of revocation

Bring-Your-Own-Key (BYOK) Onboarding and Control

Given a tenant initiates BYOK setup When a CSR/import is provided to the cloud KMS Then the system validates the algorithm is ECDSA P-256 or Ed25519 and rejects others with validation error And the key is created/imported as non-exportable in KMS/HSM; only the ClaimFlow service principal has Sign permission; tenant admins can revoke this permission And proof-of-possession is verified via challenge signature prior to activation And activation requires approval by two tenant admins (two-person control) And a test seal is generated and verified successfully; an AUDIT event BYOK_ACTIVATED is recorded with tenant_id and key_id

Algorithm Enforcement and Key Identifier Storage

Given a seal is produced When the seal is written to EXIF and claim record Then fields proof.alg and proof.key_id are populated and immutable thereafter And allowed algorithms are exactly [ECDSA_P256_SHA256, ED25519]; any other selection returns a 400 validation error And verification tooling uses embedded alg and key_id to fetch the correct public key and validate the signature both online and with the offline verifier And changing the tenant’s default algorithm only affects new seals; historical records remain unchanged and verifiable

Audit Logging and Alerting for Key Events

Given any key event occurs (create, rotate, revoke, permission change, failed sign) When the event is processed Then an immutable audit log entry is persisted within 2 seconds including timestamp (UTC ISO8601), tenant_id, key_id, event_type, actor, request_id, and outcome And logs are queryable by tenant_id and time range with RBAC-scoped access and retained for >= 7 years And alerts are generated for rotation, revocation, permission changes, and failed sign rate > 1% over 5 minutes; alerts are delivered to configured channels with MTTA < 5 minutes

Compromise Response: Break-Glass and Re-sealing Migration

Given suspected key compromise for a tenant When break-glass is initiated Then a time-bound (<= 2 hours) least-privilege role is granted only after two-person approval; all actions are audited And a new key is provisioned and activated; signing with the old key is immediately disabled; service p95 signing latency remains <= 500 ms during incident And the migration tool enumerates seals by old key_id and re-seals assets with the new key, preserving original timestamps and adding migration metadata (migrated=true, original_key_id, migration_time) And migrated items verify successfully with the new key and include a chain-of-custody link to the original seal And a migration report is produced with totals, success rate >= 99.5%, failures listed with reasons, and stored in audit

QuickQueue Offline

Performs lightweight on-device extraction for VINs, serials, and timestamps when connectivity is poor, queueing full-resolution processing for later. Shows a readiness badge and syncs automatically so adjusters can move on without waiting. Keeps the 2‑second experience consistent in the field.

Requirements

On-device Lite Extraction Engine

"As a field adjuster, I want captured photos to instantly yield VIN/serial/timestamp tags on my device so that I can move on within two seconds even when I have no signal."

Description

Provide a lightweight on-device computer vision/NLP module that detects and extracts Vehicle Identification Numbers (VINs), equipment serial numbers, and capture timestamps from photos and typed messages within ~2 seconds, even without connectivity. The module must validate VIN formats (including checksums for 17-character VINs), normalize serial patterns per OEM if known, and time-stamp assets using the device clock with timezone. It should operate within mobile CPU/GPU constraints, support offline language packs for alphanumeric recognition, and return structured tags (field, value, confidence, source) to the ClaimFlow intake flow. The engine must degrade gracefully when confidence is below threshold by flagging fields as 'needs review' without blocking user progress. Integration points include the media capture screen and the draft claim object, with an interface to pass results to the downstream server for reconciliation once full-resolution processing completes.

Acceptance Criteria

VIN Extraction and Validation (Offline, Media Capture)

Given the device has no connectivity and the adjuster captures a photo of a VIN plate on the media capture screen When the on-device lite extraction engine runs Then a 17-character VIN, if present, is detected and normalized to uppercase within 2 seconds (p95) from shutter tap And the VIN is validated against checksum rules; if validation fails, the VIN field is flagged Needs Review without blocking progress And a structured tag is emitted to the draft claim with field="VIN", value="<VIN>", confidence in [0,1], source="photo" And the user can proceed to the next step without waiting for server processing

Serial Number Extraction and OEM Normalization (Offline Photos and Messages)

Given the device is offline and an equipment serial number is present in either a captured photo or a typed message When the on-device lite extraction engine runs Then a serial number candidate is detected within 2 seconds (p95) from user action And known OEM normalization rules are applied; if the OEM is unknown, the original serial format is preserved And a structured tag is emitted to the draft claim with field="SerialNumber", value="<normalized or raw>", confidence in [0,1], source in {"photo","message"} And if confidence is below the configured threshold, the SerialNumber field is flagged Needs Review without blocking user progress

Capture Timestamp Tagging with Device Timezone

Given the user captures a photo or composes a typed message When the on-device lite extraction engine records capture time Then the timestamp is taken from the device clock at capture moment including timezone offset and formatted ISO 8601 (e.g., 2025-09-30T14:22:05-07:00) And a structured tag is emitted with field="CaptureTimestamp", value="<ISO8601 with timezone>", confidence=1.0, source in {"photo","message"} And if the device clock or timezone changes after capture, the stored tag remains the original capture time

Readiness Badge and Non-Blocking UX in Poor Connectivity

Given connectivity is poor or unavailable during capture When the on-device lite extraction completes Then a readiness badge is displayed on the media item within 2 seconds indicating local extraction is complete And the user can navigate forward without waiting for server-side processing And if any extracted field has low confidence, only a non-blocking Needs Review indicator is shown; no blocking error dialogs are presented

Queueing and Auto-Sync of Full-Resolution Processing

Given the device is offline at the time of capture and lite extraction has produced tags When connectivity is restored Then the app automatically uploads full-resolution media and sends lite results to the server with a correlation ID linking local and server processing And downstream reconciliation updates the draft claim by replacing or confirming fields without creating duplicates (idempotent updates) And the readiness badge transitions to a synced state upon server acknowledgment without user intervention

Structured Tag Schema and Intake Flow Interface

Given the lite extraction engine returns results When writing results to the draft claim object Then each tag includes exactly: field, value, confidence (0.0–1.0), source in {"photo","message"} And the tags are persisted and visible in the intake flow within 2 seconds of extraction completion And the intake flow exposes these tags to the downstream server for reconciliation via the defined interface

On-Device Performance and Offline Language Packs

Given the engine runs on a supported mid-range mobile device without connectivity When performing VIN and serial OCR/NLP Then processing completes entirely on-device with no network calls and within 2 seconds (p95) from user trigger And the English alphanumeric offline language pack is used; if an expected pack is unavailable, extraction still attempts and any affected fields are flagged Needs Review without blocking And the UI thread remains responsive during extraction (no ANR events and no visible freezes)

Offline Capture Queue & Auto Sync

"As a field adjuster, I want my captured evidence and tags to sync automatically when the connection returns so that I don’t have to wait around or re-upload anything."

Description

Implement a durable, encrypted local queue that stores original media, thumbnails, extracted metadata, and submission intents when the network is unavailable or slow. The queue must automatically attempt background synchronization when connectivity improves, using exponential backoff, jitter, and battery-aware constraints. Items should be FIFO by default with per-claim grouping to preserve ordering, support pause/resume, manual 'Sync now', and safe retry semantics (idempotent server APIs with client-generated UUIDs). The system must handle conflicts, duplicates, and partial uploads, resume from byte offsets for large videos/photos, and surface per-item sync status to the UI. A maximum queue size and eviction policy must be configurable, with user-safe prompts before any data is purged. Successful sync should transition items to server-side workflows without user intervention.

Acceptance Criteria

Durable Encrypted Offline Queue

Given network connectivity is unavailable or below the configured quality threshold When the user captures media and submits Then the app writes original media, thumbnails, extracted metadata, and submission intent into a local queue encrypted at rest And each queued item is assigned a client-generated UUID and timestamp And the queued data persists across app kill and device reboot without loss And attempting to read the queued payloads outside the app yields unreadable/encrypted content And if a queued payload is corrupt or missing on read, the item is marked Failed-Corrupt with a recoverable error and excluded from auto-sync until resolved

Auto Sync with Backoff, Jitter, and Battery-Aware Constraints

Given at least one item is Pending and the device transitions to an online or improved-connectivity state When the background sync runner starts Then retries use exponential backoff starting at the configured base interval and capped at the configured maximum, with ± configured jitter applied to each interval And sync attempts respect battery constraints (do not start when battery < configured threshold and not charging; do start when charging or above threshold) And sync proceeds while the app is in background, subject to OS background-execution limits And on successful sync of an item, the server transitions the item into the configured workflow and returns a server ID that the client records; the item is removed from the local queue

FIFO with Per-Claim Ordering Preservation

Given multiple queued items across multiple claims When auto-sync processes items Then items are dequeued FIFO by default across claims And items within the same claim are uploaded in the exact capture/queue order And a failure of an item in a claim blocks subsequent items of that claim until resolved, without blocking items from other claims

User Controls: Pause/Resume and Manual Sync Now with Status Visibility

Given the queue has active or pending items When the user taps Pause Then all in-flight uploads are gracefully paused within the configured timeout and all items show status Paused When the user taps Resume Then syncing resumes from the last known offsets and statuses update to Syncing with progress When the user taps Sync now on a specific item Then that item is attempted within 1 second, bypassing the current backoff window while honoring battery constraints And per-item status reflects Pending, Syncing (with progress %), Retrying in [ETA], Paused, Failed (with error code), or Synced, and updates in real time And after status Synced, no further user action is required and the item is removed from the queue list

Resumable Large Uploads and Partial Upload Recovery

Given a queued media file larger than the configured large-file threshold When the network drops mid-transfer Then upon reconnection the upload resumes from the last acknowledged byte offset without restarting And the final server-side object checksum matches the local checksum And multiple network flaps during a single upload still result in a single server object with no duplicates And partial server artifacts left by interrupted uploads are cleaned up or finalized automatically after success

Idempotent Retries, Duplicate Detection, and Conflict Handling

Given each queued item has a client-generated UUID When the same item is retried due to timeout or 5xx Then the server responds idempotently (returns the same resource or 409/200 with existing ID) and the client marks the item Synced once the server resource exists When the server reports a duplicate submission for the same UUID Then no additional server record is created and the client does not re-upload media bytes When the server returns a conflict (e.g., 409) due to updated server-side metadata Then the client fetches the server version, applies safe merge for non-conflicting fields, marks the item Needs Attention if manual resolution is required, and continues syncing other claims/items And the system never creates more than one server record per client UUID

Queue Size Limit, Configurable Eviction, and Safe Prompts

Given the queue approaches or exceeds the configured maximum (by count or storage size) When additional items are added Then the app displays a user-safe prompt describing the impact and options before any purge occurs When the user approves eviction Then the configured eviction policy is applied exactly (e.g., delete oldest derived artifacts first) and originals or unsynced items are not purged unless explicitly confirmed by the user When the user declines or cancels the prompt Then no data is purged and the queue contents remain unchanged And current queue usage (count and storage) is visible in the UI

Readiness Badge & Offline UX States

"As a field adjuster, I want a clear indicator that extraction succeeded and my items are queued so that I can confidently move to the next task without waiting."

Description

Add clear UI affordances that confirm when on-device extraction is complete and the user can proceed, including a 'Ready' badge on captured items, an offline banner when in QuickQueue mode, and concise toasts that confirm queued submission. The experience must remain tap-to-capture with no blocking spinners, provide accessible color/contrast and screen reader labels, support localizations, and expose per-item states: 'Extracting', 'Ready', 'Queued', 'Synced', 'Needs review'. Provide a one-tap 'Move on' CTA after readiness to preserve the two-second field experience. The design must be consistent across iOS and Android and resilient to app backgrounding and process death by restoring UI state from the queue on relaunch.

Acceptance Criteria

Per-Item Status Badges

Given the user captures an item under poor connectivity and QuickQueue is active When on-device extraction starts Then the item shows an 'Extracting' badge within 200 ms and the capture control remains enabled Given on-device extraction completes successfully When the extraction result is committed to the local queue Then the item shows a 'Ready' badge within 2 seconds of the original capture time and exposes the 'Move on' CTA Given the user submits while offline or degraded When the item is enqueued for server processing Then its badge changes to 'Queued' within 200 ms and displays a queued timestamp Given the device reconnects and the server confirms receipt When the app syncs the item Then its badge updates to 'Synced' within 1 second of confirmation Given extraction confidence is below the configured threshold or a parse error occurs When local processing completes Then the item badge shows 'Needs review' and exposes an info affordance Rule: Exactly one state badge is visible per item at any time; state is persisted to local storage and survives app restart; behavior is identical on iOS and Android.

Offline QuickQueue Banner

Given the user is on the capture screen When network is unavailable or round-trip latency exceeds 1500 ms for 3 consecutive checks Then a non-blocking banner labeled 'Offline — QuickQueue active' appears within 500 ms Given connectivity is restored and all queued items are synced When the next health check passes Then the banner auto-hides within 1 second Rule: The banner does not block capture interactions, meets a minimum 4.5:1 text contrast ratio, is announced by screen readers as a status update, and behaves identically on iOS and Android.

Queued Submission Toast

Given an item transitions to 'Queued' When the queue write succeeds Then a toast appears within 300 ms with concise localized copy and auto-dismisses after 2–3 seconds Rule: Exactly one toast is shown per item enqueue event; the toast does not block input; it is announced by screen readers (polite), and behavior is identical on iOS and Android.

Move On CTA and Two-Second Flow

Given an item's state becomes 'Ready' When the readiness is displayed Then a 'Move on' CTA is shown within 100 ms and is accessible to screen readers without stealing camera focus Given the user taps 'Move on' When the action is processed Then the app returns focus to the capture surface and the user can initiate the next capture within 2 seconds of the original capture, with no blocking spinners or overlays Rule: Capture controls remain enabled during extraction; additional captures can proceed in parallel; behavior is identical on iOS and Android.

Accessibility Compliance for States and Controls

Rule: All badge text meets WCAG 2.1 AA contrast (>= 4.5:1); badge icons meet >= 3:1 contrast; all controls and labels support Dynamic Type without truncation; VoiceOver/TalkBack labels for badges announce 'Status: <state>'; 'Move on' reads as 'Move on, advances to next capture'; state changes trigger polite live announcements; focus order is readiness -> action -> next capture; behavior is identical on iOS and Android.

Localization and Internationalization

Rule: All user-visible strings (badges, banner, toasts, CTA) are externalized and localized for at least English, Spanish, and French with verified translations; unsupported locales fall back to English; pseudo-localization with +30% text expansion shows no truncation or overlap; RTL locales mirror layouts appropriately while preserving badge semantics; date/time and numeral formats follow device locale; behavior is identical on iOS and Android.

State Persistence and Recovery

Given the app is backgrounded or terminated by the OS during 'Extracting', 'Queued', or 'Synced' When the user relaunches the app Then the capture screen reconstructs per-item states from the local queue within 1 second, without duplicate items or regressions Given the device reconnects after being offline When sync resumes Then items in 'Queued' transition to 'Synced' and the UI updates within 1 second per item Rule: Recovery errors surface 'Needs review' with a retry action; a telemetry event is logged for each restore and sync outcome; behavior is identical on iOS and Android.

Server Reconciliation & Conflict Merge

"As a claims manager, I want server-quality results to automatically reconcile with the quick offline tags so that accuracy improves without creating duplicate work."

Description

Create a reconciliation service that merges server-side full-resolution extraction results with previously stored on-device tags. The service must compare fields by type and value, apply confidence-based precedence rules, surface discrepancies for human review when thresholds are crossed, and maintain an audit trail of changes with timestamps and origins (device vs server). Merging must be idempotent, preserve user edits, and trigger workflow rules (e.g., routing, validations) only after reconciliation is finalized. Provide a diff view API for the client to display any adjusted fields and a notification hook to update the claim record without duplicating tasks.

Acceptance Criteria

Type- and Confidence-Aware Merge Precedence

Given a claim has on-device tags and server full-resolution results for the same field types (VIN, serial, timestamp) with confidence scores When reconciliation executes Then values are normalized type-safely (VIN uppercased 17 chars; serial trimmed; timestamps converted to UTC ISO-8601) And the server value replaces the device value only if (server_confidence - device_confidence) >= configured_delta and server_confidence >= min_confidence And if normalized values are equal, a single value is kept with provenance {device, server} and confidence = max(device_confidence, server_confidence) And if the above replacement condition is not met, the device value is retained

Idempotent Merge on Repeat Inputs

Given a claim at reconciled version V and the identical server payload is received again When reconciliation runs Then the persisted claim data remains unchanged And no new audit entries are created And no additional notifications or workflow triggers occur

User Edits Take Precedence Over Automated Values

Given a user has edited a field after initial capture (edit_timestamp > extraction_timestamps) and marked as user-edited When reconciliation proposes a different server value Then the user-edited value is retained And a discrepancy record is created with the proposed server value and confidence And the field is excluded from auto-overwrite until a human explicitly accepts a change

Discrepancy Threshold Creates Human Review Task and Holds Finalization

Given a field has conflicting normalized values between device and server and the confidence gap is below configured_delta or both confidences are below min_confidence When reconciliation completes evaluation Then a single human-review task is created containing field_name, values, confidences, origins, and reason And reconciliation status is set to Pending Review And no routing/validation workflow rules are triggered until the task is resolved

Comprehensive Audit Trail for Reconciliation Changes

Given reconciliation applies any field change When the change is committed Then an immutable audit entry is recorded with field_name, previous_value, new_value, previous_origin, new_origin, previous_confidence, new_confidence, changed_at (UTC), and reconciler_id And the audit log is retrievable via API by claim_id and time range And entries are ordered chronologically with a monotonically increasing version number

Diff View API Returns Adjusted Fields Only

Given a client requests the diff view for claim_id with an optional since_version When the API is called Then the response includes only fields adjusted by reconciliation since since_version (or since last finalized version) And each item includes old_value, new_value, origin_before, origin_after, confidence_before, confidence_after, changed_at, and version And results are deterministically ordered by changed_at then field_name And the API returns 200 with an ETag and supports If-None-Match to return 304 when unchanged

Finalization Notification Hook Is Exactly Once and De-duplicated

Given reconciliation transitions a claim from In Progress or Pending Review to Finalized When emitting the notification Then exactly one notification is published per finalized version with a unique deduplication_id And downstream systems update the claim record once with no duplicate task creations And retries using the same deduplication_id do not create additional updates

Network Health Detection & Mode Switching

"As a field adjuster, I want the app to automatically switch to offline mode when the signal is weak so that my capture flow stays fast and predictable."

Description

Implement a network health monitor that evaluates connectivity quality (latency, bandwidth, packet loss) and determines when to enter or exit QuickQueue Offline mode. Define thresholds and hysteresis to prevent flapping, detect captive portals and airplane mode, and expose a lightweight 'preflight' check before attempting real-time extraction. When poor conditions are detected, the client should immediately route captures to the offline queue and enable the offline banner; when conditions improve, the client should resume live flows and trigger background sync.

Acceptance Criteria

Auto-enter Offline Mode under Poor Network Conditions

Given the app is in online mode and network health is probed every 2 seconds And median RTT over the last 5 probes > 800 ms OR effective downstream bandwidth < 128 kbps OR packet loss ≥ 20% across the last 10 probes When these poor conditions persist for 2 consecutive probe cycles (≥ 4 seconds) Then the client switches to QuickQueue Offline mode within 1 second And subsequent captures are routed to the offline queue immediately And the offline banner becomes visible within 500 ms And no live extraction requests are initiated while in offline mode

Auto-exit Offline Mode and Resume Live Flows on Recovery

Given the app is in QuickQueue Offline mode And good network conditions are detected: median RTT < 400 ms, bandwidth ≥ 512 kbps, packet loss < 5% for 10 consecutive seconds When the recovery threshold is met Then the client exits offline mode and resumes live flows within 2 seconds And queued items begin background sync within 2 seconds of exit And the offline banner is dismissed and the readiness badge reflects syncing within 1 second And at least one queued item is uploaded successfully when the server is reachable

Captive Portal Detection and Handling

Given the device is connected to Wi‑Fi without internet access behind a captive portal And the connectivity check GET to the configured endpoint responds with HTTP status ≠ 204 or contains a login HTML page When the check result is received Then the client classifies the state as "Captive Portal" And enters QuickQueue Offline mode within 1 second And displays an offline banner indicating sign‑in required And suppresses live extraction attempts until a successful 204 check is observed for 10 seconds

Airplane Mode Detection and Handling

Given the OS reports airplane mode enabled or no network interfaces available When the app evaluates network health Then the client enters QuickQueue Offline mode immediately (< 200 ms) And network probe routines are suspended And captures are routed to the offline queue And preflight returns "offline" without performing network I/O in < 50 ms

Preflight Network Check Before Real-time Extraction

Given a user initiates real‑time extraction When the preflight network check runs Then it completes within 300 ms And returns a decision of "online" only if median RTT < 400 ms, bandwidth ≥ 512 kbps, packet loss < 5%, and last successful connectivity probe was within 2 seconds And if the decision is "offline" no network extraction request is sent and the capture is queued And the decision and metrics are recorded with a timestamp

Immediate Offline Queue Routing and UI Indicators

Given poor network conditions have triggered offline mode When the user captures a photo or scans a VIN/serial Then the asset is written to the offline queue in < 100 ms And the UI shows a readiness badge within 500 ms And the total capture-to-ready time is ≤ 2.0 seconds at the 95th percentile measured over 100 events And no blocking spinner appears longer than 300 ms

Hysteresis to Prevent Mode Flapping

Given network conditions oscillate around the defined thresholds When poor–good–poor transitions occur within short intervals Then the client requires at least 5 seconds of confirmed poor conditions (≥ 2 consecutive probes) before entering offline after being online And requires at least 10 seconds of confirmed good conditions before exiting offline And the mode does not toggle more than once within any 15‑second window during a 3‑minute oscillation test

Admin Controls & Remote Model Updates

"As a product admin, I want to configure offline extraction behavior and safely roll out model updates so that each carrier’s needs and device constraints are respected."

Description

Provide an admin console and remote config to enable or disable QuickQueue Offline per carrier, line of business, or environment; configure which fields are extracted on-device; and set queue size, sync policies, and confidence thresholds. Support over-the-air updates of the lightweight extraction model and language packs with version pinning, phased rollouts, and rollback capability. Device capability checks must prevent incompatible downloads, and all changes must be auditable with who/when metadata.

Acceptance Criteria

Tenant-Level Toggle by Carrier/LOB/Environment

- Given an admin with Config:QuickQueue permission and a device enrolled to Carrier A, LOB=Auto, Env=Prod, When the admin disables QuickQueue Offline at scope Carrier A/Auto/Prod, Then the device hides the Offline extraction UI and blocks on-device extraction within 5 minutes of connectivity or on next app launch. - Given conflicting policies across scopes, When policies are evaluated, Then the most specific scope (environment > LOB > carrier) takes precedence and is applied consistently on API responses and on-device behavior. - Given the feature is disabled for a device scope, When the adjuster captures while offline, Then a non-blocking notice "Offline extraction disabled by admin" is shown and no on-device extraction artifacts are queued.

On-Device Field Configuration Enforcement

- Given admin enables on-device fields {VIN, SerialNumber, Timestamp} and disables others, When the device performs QuickQueue extraction, Then only the enabled fields are emitted, stored, and displayed; disabled fields are omitted and not persisted. - Given a new field configuration is published, When the device fetches config vN+1, Then captures taken after fetch honor vN+1 and captures taken under vN remain unchanged; diagnostics show config version applied. - Given a capture, When on-device extraction runs, Then the end-to-end on-device step completes within 2 seconds at p95 for enabled fields on supported devices.

Queue Size and Sync Policy Configuration

- Given queue size is set to 50 with policy "reject-new", When the 51st extraction is attempted offline, Then the capture succeeds but the extraction is skipped with a user notice and the queue remains ≤ 50. - Given queue size is set to 50 with policy "drop-oldest", When the 51st extraction is added, Then the oldest queued item is evicted, the new item is queued, and an eviction event is logged locally. - Given sync policy is "Wi‑Fi only" and "battery ≥ 20%", When the device is on Wi‑Fi with battery at 30%, Then queued items begin syncing automatically within 60 seconds; When on cellular or battery 15%, Then no sync occurs and items remain queued. - Given intermittent connectivity during sync, Then retries use exponential backoff capped at 15 minutes and items are uploaded in capture-timestamp order without duplication.

Confidence Thresholds and Readiness Badge Behavior

- Given per-field thresholds (e.g., VIN ≥ 0.92, SerialNumber ≥ 0.85), When an extraction meets or exceeds its threshold, Then the readiness badge shows "Ready" within 2 seconds and the value is included in the queued payload; otherwise the badge shows "Needs review" and the field is excluded from auto-population. - Given thresholds are updated by admin, When the device fetches the new config, Then new captures use updated thresholds; previously queued items retain their original local decision but may be re-evaluated during server processing after sync. - Given an extraction without a computable confidence, Then the badge shows "Pending" and the item is queued for full-resolution server processing.

OTA Update, Version Pinning, and Compatibility Gating

- Given admin pins the lightweight model to v1.3.2 for Carrier A/Prod, When model v1.4.0 is published, Then devices in that scope continue using v1.3.2 and do not download v1.4.0. - Given admin targets model v1.4.0, When the device passes capability checks (OS ≥ required, CPU supports required instruction set, free storage ≥ 150 MB), Then the package downloads, signature and checksum validate, and the model swaps atomically without user action; on any failed check, the download is skipped and the current model remains active. - Given a download or activation failure, Then the device reverts to the last working model, records failure telemetry with error code, and retries with exponential backoff.

Phased Rollout and Rollback Controls

- Given a phased rollout plan (10% → 50% → 100%) with a pause function, When the rollout starts, Then only the targeted cohorts receive the update in the defined window and telemetry shows version adoption by scope with ≤ 15-minute granularity. - Given admin triggers a rollback, When devices in the affected scope are online, Then each reverts to the previous working version within 2 hours and blocks further downloads of the rolled-back version. - Given a device has not checked in for > 7 days, When it next connects, Then it evaluates current rollout state and installs the latest allowed version for its cohort.

Comprehensive Audit Trail for Admin Changes

- Given any admin change (enable/disable, field list, thresholds, queue/sync policies, model publish/pin/unpin, rollout/rollback), Then an immutable audit entry is created with user ID, UTC timestamp, scope (carrier/LOB/env), change set (before→after), request ID, and outcome (success/failure). - Given audit logs, When queried by date range, scope, or user, Then up to 10,000 matching records return within 3 seconds and can be exported to CSV. - Given access control, When a user lacks Audit:Read permission, Then audit log access is denied with HTTP 403 and no data is returned.

On-device Security, Privacy, and Retention

"As a compliance officer, I want offline data to be encrypted, controlled, and auditable so that we meet regulatory obligations even when devices are used without connectivity."

Description

Ensure all queued content and metadata are encrypted at rest using platform-secure storage (e.g., iOS Keychain-protected keys, Android Keystore), encrypted in transit upon sync, and inaccessible to other apps. Enforce configurable retention policies (e.g., TTL after successful sync or logout), immediate wipe on device deregistration, and redaction of PII from logs and notifications. Support MDM policies for corporate devices, including blocking screenshots on sensitive screens and remote wipe triggers. Provide audit logs for access and deletion events to meet SOC 2/GDPR obligations.

Acceptance Criteria

Encrypt Queued Content At Rest Using Platform Secure Storage

Given the app queues media and metadata offline When data is written to local storage Then each item is encrypted with AES-256-GCM using a per-installation key protected by iOS Keychain/Android Keystore (hardware-backed when available) And each ciphertext uses a unique nonce and authenticated tag; decryption fails on tamper And files reside only in app-private storage with no-backup flags (iOS excluded from backup + NSFileProtectionComplete; Android MODE_PRIVATE + noBackup) And no content is written to shared/external storage or exposed via content providers And encryption keys are rotated on reinstall and invalidated immediately on logout or deregistration

Encrypt In-Transit Sync with Certificate Pinning

Given the device initiates sync When establishing a connection to the API Then TLS 1.2+ is required with strong ciphers and certificate pinning to a known public key/leaf; connections fail closed on pin mismatch And all uploads use HTTPS; HTTP and redirect downgrades are blocked And authentication uses short-lived tokens; secrets are not sent in query parameters And a pin-mismatch test results in a logged security event and no data transmission

Configurable Retention and TTL After Sync or Logout

Given an admin-configured retention policy with TTLs for pending and synced items When an item is successfully acknowledged by the server Then the local full-resolution copy is deleted within 5 seconds unless policy explicitly retains it And pending items exceeding their TTL are purged automatically on app launch and at least every 15 minutes while running And on user logout, all queued content, derived metadata, caches, and encryption keys are wiped immediately And policy defaults and changes are versioned and auditable

Immediate Wipe on Device Deregistration and Remote Wipe

Given the device receives a verified deregistration or MDM remote wipe command When the command is processed Then the app halts operations, invalidates keystore keys, securely deletes queued content/caches/logs, and locks access until re-enrollment And online wipes complete within 10 seconds of receipt; if offline, the wipe executes on next app resume prior to any UI access And a wipe completion event is recorded and, if possible, confirmed to the server And reinstallation does not restore previous data (fresh keys and empty storage)

Redact PII from Logs and Notifications

Given the app emits logs or user/device notifications When content could include PII (e.g., VINs, serials, names, emails, phone numbers, addresses, GPS) Then PII is masked or tokenized before persistence or display And production notifications contain no media thumbnails or message snippets with PII; they use generic text And production logging excludes raw payloads by default; debug logging (when enabled) still applies redaction And automated scans of log outputs find no unredacted PII

MDM Policies: Block Screenshots on Sensitive Screens

Given an active MDM policy or app setting to block capture When a user views sensitive screens (offline queue, media viewer, extracted data) Then screen capture and recording are blocked using OS mechanisms (Android FLAG_SECURE; iOS screen capture detection and content obscuring) And if the OS cannot block capture, sensitive content is blurred/obscured And policy changes take effect without app restart And all block/attempt events are logged without PII

Audit Logging for Access and Deletion Events (SOC 2/GDPR)

Given any access or lifecycle operation on queued content or keys When an action occurs (view, decrypt, sync, purge, wipe) Then an immutable audit event is recorded with: user/device ID, anonymized object ID, action, timestamp (UTC), app version, device model, network IP, reason code, and outcome And audit entries are encrypted at rest locally and synced to the server within 60 seconds with retry and ordering guarantees And audit logs are retained per policy (minimum 1 year configurable) and are exportable for compliance And tamper signals (clock skew beyond threshold, storage corruption) are detected and flagged

Tap Verify

Overlays tappable boxes on extracted elements; tap to view the value and confidence, correct in-line, or trigger a guided recapture. Makes extraction transparent and editable at the source, speeding QA and training. Results in fewer errors and faster approvals.

Requirements

Interactive Tap Overlay

"As an adjuster, I want tappable boxes over extracted fields on a claim asset so that I can quickly locate the data the system found and act on it without hunting through the image."

Description

Render tappable bounding boxes over all extracted elements in photos, PDFs, and message attachments. Boxes must align with model-provided coordinates, handle multi-line/grouped fields, and avoid overlap using collision/priority rules. On tap/click, the element is visually highlighted and brought into focus, anchoring an action surface without shifting the underlying asset. The overlay must be responsive, support pinch-zoom and pan, and remain performant at scale (≤200ms overlay paint on assets up to 20MP, ≤60ms on subsequent pans/zooms). Accessibility includes full keyboard navigation, focus states, and screen reader labels describing field name, value, and confidence. Integrates with ClaimFlow’s Extraction Service (reads coordinates/labels) and Event Bus (emits tap, view, and action telemetry). Supports web and mobile SDKs with a unified component API.

Acceptance Criteria

High-resolution overlay alignment and initial paint budget

Given a 20MP image or PDF page and a valid extraction payload containing bounding boxes and labels When the overlay component is mounted Then 100% of boxes render aligned to model coordinates within the greater of ±2px or ±0.2% of the asset dimension And the initial overlay paint completes within ≤200ms from mount time And no visual layout shift of the underlying asset occurs during or after overlay paint

Responsive pan/zoom with performant subsequent paints

Given the overlay is rendered over an asset When the user pans or pinch-zooms between 25% and 400% Then boxes translate and scale in lockstep with the asset with no jitter or drift (≤1px at 1x equivalent) And each subsequent overlay repaint completes within ≤60ms And tappable hit areas maintain a minimum effective size of 44x44 CSS px at 100% zoom (scaling proportionally)

Collision handling and priority resolution for overlapping elements

Given two or more bounding boxes overlap by ≥10% area When the overlay computes layout Then the element with higher priority (from payload) is stacked above lower-priority elements And smart nudging/offsets are applied so that no interactive hit areas overlap by more than 2px in the hit-test layer And z-index ordering is deterministic (priority, then reading order) And each element remains individually tappable without ambiguity (distinct hit-test regions)

Tap/click focus, highlight, and anchored action surface without asset shift

Given a user taps or clicks a visible overlay element Then the tapped element receives a visible focus/highlight state and is brought into view if partially occluded And the action surface anchors adjacent to the element without shifting or reflowing the underlying asset or changing zoom level And only one element is in the active state at a time, and previous highlights are cleared And tap and view telemetry events are emitted with elementId, assetId, coordinates, and ISO-8601 timestamp

Multi-line and grouped field rendering and interaction

Given an extracted field composed of multiple line spans or grouped child elements in the payload When the overlay renders the field Then a single grouped tappable region represents the field with visual cues linking all constituent spans And tapping any span activates the group, highlighting all spans as one selection And hit-testing on any constituent span resolves to the groupId And the displayed value concatenates spans in reading order for accessibility and telemetry

Accessibility: keyboard navigation and screen reader labeling

Given keyboard-only navigation and a screen reader are enabled When navigating from the asset container into the overlay Then every tappable element is reachable via Tab/Shift+Tab (or Arrow keys when in spatial mode) in a deterministic order And focus states are visible with WCAG 2.2 non-text contrast ≥3:1 And each element exposes an accessible name including field name, value, and confidence (e.g., “Field Claim Number, value ABC123, confidence 96%”) And Enter/Space activates the element and emits the same telemetry as a tap And Escape returns focus to the asset container

Integration with Extraction Service, Event Bus, and unified SDK API

Given a valid Extraction Service response with coordinates (absolute px or normalized) and labels When the component initializes on web and mobile SDKs Then coordinates are correctly interpreted (including DPI/scale normalization) and boxes render in the correct positions And the component connects to the Event Bus and publishes tap and view events to the specified topics without runtime errors And the web and mobile SDKs expose a unified API (props: asset, extractionPayload, eventBus, config; callbacks: onTap, onView, onRender) with identical signatures And rendering parity across web and mobile yields ≤1% pixel-diff at 1x scale on reference assets

Confidence and Metadata Popover

"As a claims manager, I want to see the confidence and origin of an extracted value so that I can decide whether to accept it or take corrective action."

Description

Display an anchored popover on tap that shows the extracted value, confidence score (numeric and color-coded), extraction source (asset ID and region), model version, and timestamp. Provide configurable confidence thresholds to flag low-confidence values and surface warnings. Support deep link to the full asset preview at the exact region and to the claim record field. Popover content must be localized, accessible, and dismissible with ESC/tap-away. Integrate with Tenant Settings for threshold configuration and with the Audit Log to record views and actions taken from the popover.

Acceptance Criteria

Popover Anchoring and Content Display on Tap

Given a tappable Tap Verify element is visible When the user taps the element Then an anchored popover opens adjacent to the element within 300 ms and no other popovers remain open And the popover displays: extracted value (plain text), confidence score (two decimals), a color-coded indicator, extraction source assetId and region (x,y,width,height normalized 0–1), model version, and extraction timestamp in ISO 8601 adjusted to the user’s timezone And only one popover can be open at a time and a subsequent tap on another element closes the previous popover And the popover repositions to remain fully within the viewport if the anchor is near an edge

Confidence Score Rendering and Threshold Warnings

Given tenant confidence thresholds are low=0.70 and medium=0.90 When a confidence score of 0.65 is displayed Then the indicator shows "0.65" with red status and a low-confidence warning is shown When a confidence score of 0.85 is displayed Then the indicator shows amber status and no warning banner When a confidence score of 0.95 is displayed Then the indicator shows green status and no warnings Then boundary behavior is: score < low -> red; low ≤ score < medium -> amber; score ≥ medium -> green And color indicators and any text meet WCAG 2.1 AA contrast requirements

Tenant Settings: Configurable Thresholds

Given an admin updates Tenant Settings thresholds to low=0.60 and medium=0.80 and saves When a user opens any popover after save Then the new thresholds are applied within 60 seconds without redeploy When invalid thresholds are entered (low >= medium or values outside 0–1) Then the settings cannot be saved and validation errors are shown When no tenant-specific thresholds exist Then defaults of low=0.70 and medium=0.90 are used And thresholds persist across sessions and apply to all Tap Verify popovers for the tenant

Deep Links to Asset Region and Claim Field

Given a popover is open for a specific element with assetId and region When the user selects "Open asset" Then the asset preview opens centered on the exact region with a visible highlight within 1 second When the user selects "Go to field" Then the claim record opens with the corresponding field in view and focused within 1 second And deep links include claimId, assetId, elementId, and region parameters to maintain context When the asset or field cannot be resolved Then navigation is prevented and a descriptive error is shown

Localization, Accessibility, and Dismissal

Given the user locale is es-ES When a popover opens Then all labels, messages, dates, and numbers are localized to es-ES; missing translations fall back to en-US And the popover has role="dialog", aria-labelledby a visible title, is keyboard-focusable, and traps focus while open And the ESC key and tap-away dismiss the popover and focus returns to the invoking element And color indicators are accompanied by text/icons and meet WCAG 2.1 AA contrast And keyboard behavior supports Tab/Shift+Tab to navigate and Enter/Space to activate actions

Audit Logging of Popover Views and Actions

Given a popover is opened Then an Audit Log entry "popover.view" is recorded with tenantId, userId, claimId, assetId, elementId, timestamp, and confidence score When the user clicks "Open asset" or "Go to field" Then an Audit Log entry "popover.action" is recorded with action type and the same context metadata When a low-confidence warning is shown Then an Audit Log entry "popover.warningShown" is recorded with thresholds and score When the popover closes Then an Audit Log entry "popover.close" is recorded with reason (esc, tap-away, close-button, navigation) And all Audit Log entries are available via the Audit Log UI/API within 15 seconds and are immutable

Inline Edit with Validation and Write-back

"As an adjuster, I want to correct extracted values in-line so that the claim record is accurate without leaving my review flow."

Description

Enable in-place editing of extracted values directly within the popover, with field-type-specific inputs (date picker, currency, VIN mask, address autocomplete). Apply synchronous validation (format, range, cross-field rules) and asynchronous business-rule checks (policy coverage, deductible caps). On save, write back to the canonical claim record, mark the field as ‘verified,’ and propagate updates to downstream workflow steps and notifications. Maintain complete audit history (before/after, who, when, reason), support undo within session, and resolve concurrent edits via optimistic locking and merge prompts. Ensure PII protection, role checks, and autosave with failure recovery.

Acceptance Criteria

Role-Based Inline Edit Popover With Field-Type Inputs and PII Masking

Given a user with edit permission taps a tappable box When the popover opens Then render a field-type-specific input: Date uses a date picker, Currency enforces locale currency with 2 decimals, VIN enforces 17 uppercase alphanumerics excluding I/O/Q with checksum, Address uses autocomplete with at least 5 suggestions within 500ms Given a user without edit permission taps a tappable box When the popover opens Then show the value read-only and keep Save disabled Given a field is flagged as PII and the user lacks PII view permission When the popover opens Then mask the value and prevent unmask and edit; log the access attempt in audit Given a field is flagged as PII and the user has PII view permission When the user clicks Unmask Then reveal the value and auto-remask after 5 minutes of inactivity Given the popover is open When it renders Then display the current extracted value and confidence percentage

Synchronous Field Validation: Format, Range, and Cross-Field Rules

Given a field input is changed When the value fails format validation Then show an inline error within 300ms and disable Save Given Date of Loss is entered When the date is in the future relative to system UTC date Then show error "Date cannot be in the future" and block Save Given Claim Amount is entered When the value is < 0 or > 1000000 or has > 2 decimal places Then normalize decimals where possible or show error and block Save Given a VIN is entered When length != 17 or contains I/O/Q or checksum fails Then show error and block Save Given Coverage Limit and Claim Amount exist When Claim Amount > Coverage Limit Then show a cross-field error on both fields and block Save Given all synchronous validations pass When Save is clicked Then proceed to asynchronous business-rule checks

Asynchronous Business-Rule Checks and Result Handling

Given local validations pass When Save is clicked Then invoke asynchronous checks for policy coverage, deductible caps, and special handling flags Given asynchronous checks return within 5 seconds When results include a blocking failure (e.g., policy lapsed) Then prevent commit and display a blocking message with a View Details link Given asynchronous checks return warnings (e.g., near deductible cap) When user confirms override per role policy Then allow commit and record the override in audit Given asynchronous checks time out after 5 seconds or network error occurs When retry is offered Then display a retry option and keep Save disabled until checks complete or user cancels Given the app is offline When Save is clicked Then queue checks and mark the field status as "Pending verification" until connectivity is restored and checks pass

Save/Write-Back: Verification Flag, Propagation, Autosave, and Recovery

Given validations and required checks pass or an allowed override is confirmed When the user clicks Save Then write the updated value to the canonical claim record within 1 second and return a new version identifier Given write-back succeeds When the response is received Then mark the field as Verified with timestamp and editor user id and display a verified badge Given the field value changes When write-back completes Then emit a FieldUpdated event to the workflow engine; dependent tasks recalculate and notifications dispatch within 10 seconds Given the user edits a value without clicking Save When 10 seconds elapse or the input loses focus Then autosave a draft to the server if online or to local storage if offline; do not mark as Verified until explicit Save Given Save or autosave fails due to network error When 3 retries with exponential backoff (1s, 2s, 4s) fail Then persist the draft locally, show a recovery banner, and auto-retry on reconnect; no data loss occurs Given the app is reloaded after a failure When the user returns within 7 days Then restore the unsaved draft and prompt to resume or discard

Audit Trail: Before/After, Who/When/Reason, Exportability

Given a field value is changed and saved When write-back succeeds Then append an immutable audit record with claim id, field id, previous value, new value, editor user id, ISO 8601 UTC timestamp, source (inline edit/autosave), and client id Given Save is initiated When the user confirms Then require a reason (code or free text min 5 chars) and include it in the audit record; disable Save until provided Given an audit record exists When queried via the audit API Then return it within 1 second and allow export as CSV and JSON Given an undo or redo results in a saved change When recorded Then create a new audit entry linked to the prior change id

Undo/Redo Within Session

Given a user has made changes to a field in the current session When the user clicks Undo Then revert the field to the previous value without page reload and update the Verified badge accordingly Given up to the last 10 changes are stored per field in session When the user clicks Undo repeatedly Then step back through prior states; Redo moves forward until the latest state Given an undo reverts to the original extracted value When applied Then clear local validation errors and re-run asynchronous checks if required before allowing Save Given the session ends (logout, 30 minutes inactivity, or browser close) When the session is restored Then the undo/redo stack is cleared

Concurrent Edit Resolution via Optimistic Locking and Merge Prompt

Given another user saved a newer version of the same field When the current user attempts to Save Then detect a version conflict via ETag/version and block silent overwrite Given a version conflict is detected When the merge prompt is displayed Then show Mine vs Theirs values and diffs within 500ms and offer actions: Keep Mine, Keep Theirs, Merge Given the user selects Keep Mine When Save proceeds Then overwrite the canonical value, increment version, and create audit entries for the conflict and resolution Given the user selects Keep Theirs When applied Then discard the local change, refresh the value, and close the popover without write-back Given the user selects Merge When editing the combined value Then re-run validations and checks before saving the merged result Given the merge prompt remains unanswered for 2 minutes When timeout occurs Then cancel the Save and retain the draft locally

Guided Recapture Flow

"As a field adjuster, I want a guided recapture when a field is unclear so that I can quickly obtain a usable image and move the claim forward."

Description

Provide an action from the popover to initiate guided recapture when confidence is low or data is unreadable. On mobile, open an in-app camera with real-time guidance (edge detection, glare/perspective hints, stability gate) and automatic capture; on desktop, offer drag-and-drop/upload with quality checks. Link recaptured assets to the original claim and targeted field, re-run extraction on the new asset, and update overlays and values automatically. Handle failures with fallback to manual entry and clear error messaging. Preserve chain-of-custody metadata, support offline capture with queued sync, and respect storage/retention policies.

Acceptance Criteria

Mobile Guided Recapture Initiation from Low-Confidence Field

- Given a field extracted with confidence below the configured threshold (default 0.80) or marked unreadable, When the user taps "Recapture" in the Tap Verify popover on mobile, Then the in-app camera opens within 1 second and the targeted field name is displayed in the capture UI. - Given camera permissions are not yet granted, When the user taps "Recapture", Then the OS permission prompt is shown once, and if permanently denied, Then the app displays a non-blocking error and offers Manual Entry. - Given the camera opens, When the session starts, Then the app records claim ID, field ID, initiating user ID, and recapture session ID for audit. - Given a recapture is initiated, When the event occurs, Then an analytics event "recapture_initiated" with source=TapVerify and platform=mobile is emitted.

Desktop Recapture via Upload with Quality Checks

- Given a field with low confidence on desktop, When the user selects "Recapture" from the popover, Then a modal opens supporting drag-and-drop and file browse with allowed types: JPG, PNG, HEIC, PDF (single page). - Given a file is dropped/selected, When pre-checks run, Then files larger than 25 MB or with dimensions under 1000 px shortest edge are rejected with an actionable message. - Given a raster image, When quality checks run, Then the image passes if Laplacian variance >= 100, glare coverage <= 15%, and skew <= 5 degrees; otherwise, Then the user sees specific guidance and may retry. - Given an upload starts, When progress is available, Then a progress indicator is shown and the user can cancel before 100% without affecting existing claim data. - Given a successful upload, Then the asset is linked to the claim and targeted field and queued for extraction.

Real-time Mobile Guidance and Auto-Capture

- Given the in-app camera is open, When a document/area is in frame, Then edge detection outlines appear within 250 ms and guidance hints show for glare and perspective. - Given the device is moving, When stability is detected for >= 500 ms and quality checks pass (glare <= 15%, skew <= 5 degrees, focus metric >= threshold), Then the shutter auto-captures; otherwise, Then auto-capture is suppressed. - Given auto-capture is available, Then a manual capture button is also available and functions identically. - Given a capture occurs, Then the user can review, retake, or accept; accepting proceeds to extraction.

Re-extraction and Overlay Update after Recapture

- Given a new asset is captured/uploaded, When the extraction job starts, Then a loading state is shown and completes within 8 seconds p95. - Given extraction succeeds, Then the targeted field value is updated in the UI, Tap Verify overlays refresh to reflect the new bounding boxes, and the new confidence score is displayed in the popover. - Given the value changes, Then the prior value, new value, user, timestamp, and asset ID are recorded in the audit log and accessible from the field history. - Given other fields are affected by the new asset, Then their overlays and values are updated consistently and flagged as "updated via recapture" in the audit trail.

Failure Handling and Manual Entry Fallback

- Given extraction fails due to timeout, parsing error, or repeated quality failure (>= 2 attempts), When the user completes the recapture attempt, Then the app displays a clear error message with next steps and opens Manual Entry for the targeted field. - Given the user switches to Manual Entry, When a value is submitted, Then validation rules are applied and the value is saved with a "manual" source tag linked to the failed recapture session. - Given a failure occurs, Then the recaptured asset is still attached to the claim and field for reference, and the user may retry later. - Given the user cancels the flow, Then no existing claim values are overwritten.

Chain-of-Custody Metadata Preservation

- Given any recapture (mobile or desktop), When an asset is created/attached, Then the system records immutable metadata: claim ID, field ID, user ID, session ID, capture/upload timestamp (UTC), device or browser info, app version, IP (desktop), GPS (if permission granted), file name, file size, mime type, and SHA-256 hash. - Given the asset is processed, Then a provenance link is maintained from the field value to the specific asset and extraction model version used. - Given a value is updated, Then an audit entry with before/after values, reason=recapture, and reference to the metadata is created and cannot be edited by end users. - Given an admin views the claim audit, Then chain-of-custody details are viewable and exportable as JSON.

Offline Capture with Queued Sync and Retention Compliance

- Given the mobile device is offline, When the user initiates recapture, Then capture is allowed, the asset and metadata are stored locally encrypted, and the field is marked "Pending Sync" without overwriting the current value. - Given connectivity is restored, When sync runs, Then pending assets upload automatically with exponential backoff (up to 5 retries), and on success the extraction flow runs as if captured online. - Given a conflict where the targeted field was updated on the server after the offline capture, Then the system does not auto-overwrite; it presents a resolution prompt to the user and flags the conflict in the audit log. - Given local temporary assets, When upload succeeds, Then local copies are deleted within 1 hour; if still offline, Then local copies are purged after 24 hours unless retention policy requires longer. - Given organizational storage/retention policies are configured, When an asset is stored server-side, Then the correct retention tag is applied and the asset is subject to scheduled deletion per policy, with deletion events logged.

Training Feedback Capture for Model Improvement

"As a product owner, I want verified corrections to feed our training pipeline so that extraction accuracy improves over time with real-world data."

Description

Capture user confirmations and corrections as labeled feedback tied to asset regions, field types, and final accepted values. De-duplicate identical corrections, anonymize/scrub PII per tenant policy, and batch to a secure ML feedback store. Provide configuration to opt-in per tenant, with controls for sampling rate and data retention. Expose metrics (accept rate, correction rate by field, confidence vs. accuracy) to inform model retraining and threshold tuning. Integrate with the ML pipeline via export jobs and with Feature Flags to enable/disable per environment.

Acceptance Criteria

Inline Tap Verify Feedback Capture

- Given Tap Verify overlays extracted fields for a claims document and a user confirms or corrects a field value - When the user saves the change or navigates away from the field - Then a feedback record is created with tenantId, documentId, fieldType, assetRegion {x,y,width,height}, extractedValue, finalAcceptedValue, modelConfidence, actionType (confirm|correct), timestamp, sessionId, and actorId hashed per tenant policy - And the feedback record is persisted within 500 ms and assigned a globally unique id - And the document state reflects the finalAcceptedValue immediately and on reload

Feedback De-duplication and Idempotency

- Given multiple identical corrections to the same documentId+fieldType+assetRegion within the same session or within a 30-minute window - When batching to the ML feedback store - Then only one canonical correction is stored and an occurrences count is incremented accordingly - And writes use an idempotency key derived from tenantId+documentId+fieldType+assetRegion+finalAcceptedValue to prevent duplicates on retries - And non-identical subsequent changes are stored as new versions with an incremented version number

Tenant Opt-In, Sampling, and Retention Controls

- Given a tenant with feedbackCapture disabled - When users perform confirmations or corrections - Then no feedback records are stored or exported and metrics exclude this tenant - Given feedbackCapture is enabled with samplingRate=20% (random) - When 10,000 eligible events occur - Then between 18% and 22% are captured and the remainder are dropped by design - And setting retentionDays=90 results in automatic purge of feedback older than 90 days via a daily job - And configuration changes via Admin API take effect within 5 minutes and are audit logged with actor, before/after, and timestamp

PII Scrubbing per Tenant Policy

- Given tenant policy marks specific fields or patterns as PII - When feedback is stored, exported, or logged - Then PII values are tokenized or masked according to policy and raw PII is not present in payloads, logs, or metrics - And assetRegion geometry and non-PII metadata remain intact for model training utility - And outbound exports pass an automated PII scan with 0 critical findings

Secure Batching to ML Feedback Store

- Given feedback events are queued for batching - When batch size reaches 500 records or 60 seconds elapse, whichever comes first - Then the batch is written to the ML feedback store with encryption in transit (TLS 1.2+) and at rest (AES-256) - And access to the store is restricted by service identity with least privilege and audited - And transient failures are retried with exponential backoff up to 5 attempts; on persistent failure, alerts are emitted and the queue persists up to 50,000 events with oldest-first drop when full

Metrics Exposure and Accuracy vs Confidence

- Given captured feedback for a tenant over a selected date range - When querying the Metrics API or viewing the dashboard - Then accept rate and correction rate are reported overall and by fieldType, with filters for environment and date - And calibration curves (confidence vs accuracy) are available by fieldType and exportable as CSV - And metrics are updated at least every 60 minutes and reconcile to within 1% of raw feedback counts

Export Jobs and Feature Flags by Environment

- Given Feature Flags are set per environment (dev, staging, prod) - When the export flag is disabled in an environment - Then no export jobs run and no feedback leaves that environment - Given the export flag is enabled - When the scheduled daily export runs - Then a per-tenant export is produced in the agreed schema with a manifest, transferred to the ML pipeline destination, and success is audit logged - And failures trigger alerts and are retried on the next schedule without data loss

Role-Based Controls and QA Review Queue

"As a QA lead, I want a permissioned review queue for flagged fields so that my team can efficiently verify and correct data before approval."

Description

Enforce role-based permissions for view, edit, and recapture actions. Provide a QA mode that aggregates low-confidence or changed fields into a review queue with assignment, due dates/SLAs, and bulk accept/correct actions. Surface status badges (Unverified, Corrected, Verified) on overlays and in the claim record. Record reviewer decisions in the audit log and emit metrics for throughput and aging. Integrate with ClaimFlow’s task engine for routing and notifications, and expose APIs to fetch and update review items programmatically.

Acceptance Criteria

RBAC: Overlay View/Edit/Recapture Permissions

Given a user with role Adjuster lacking Edit permission, when opening Tap Verify overlay, then value and confidence are visible but inline edit controls are disabled and recapture is hidden Given a user with role QA Reviewer with Edit permission, when tapping a field, then inline edit and guided recapture controls are enabled Given a user without Recapture permission, when attempting to invoke recapture via API, then the API returns 403 RBAC_DENIED and no recapture session is created Given role permissions are updated by Admin, when a user next loads the overlay or calls the API, then the new permissions are enforced within 60 seconds Given an unauthorized attempt to edit or recapture, when the attempt occurs, then an audit log entry is created with actor, action, fieldId, and RBAC_DENIED outcome

QA Mode: Queue Population for Low-Confidence and Changed Fields

Given QA mode is enabled with threshold 0.80, when a claim has extracted fields, then all fields with confidence < 0.80 are added to the QA review queue exactly once Given a user edits any extracted field value, when QA mode is active or later activated, then that field is added to the QA review queue marked as Changed Given additional edits occur to a queued field, when viewing the queue, then the item remains a single entry with latest proposed value and retains change history link Given new extraction results arrive (reprocess) during QA mode, when confidence rises above threshold and no user change exists, then the item is removed from the queue; otherwise it remains Given the queue is loaded, when viewing an item, then metadata includes fieldId, label, source (photo/message), current value, previous value, confidence, and reason (LowConfidence or Changed)

QA Review: Assignment, Due Dates, SLA Alerts, and Notifications

Given a new QA queue item is created, when routing rules assign it to a team or user, then the item shows Assignee and DueDate computed from SLA policy Given an assignee change occurs, when the assignment is saved, then a task is created/updated in the task engine and a notification is sent to the new assignee within 60 seconds Given an item approaches SLA (<= 2 hours remaining), when viewed in the queue, then it displays a Yellow SLA badge; if overdue, a Red SLA badge and an OVERDUE event is emitted Given SLA policy is updated, when recalculation runs, then existing open items have DueDate recomputed and badges/alerts updated accordingly Given an item is unassigned, when viewed, then it is filterable as Unassigned and appears in the team work queue

QA Review: Bulk Accept and Bulk Correct Actions

Given multiple items are selected in the QA queue, when Bulk Accept is executed, then all selected items transition to Verified and their values remain unchanged Given one or more selected items fail validation during Bulk Accept, when the operation completes, then successful items are committed and failures are reported with item-level error codes Given multiple items of the same field type are selected, when Bulk Correct is executed with specified corrections per item, then each item is updated to its provided value and transitions to Corrected Given Bulk operations complete, when checking the audit log, then each item has a decision entry linked by a common bulkOperationId with actor and timestamp Given a bulk action is triggered, when processing, then user feedback shows progress and final success/failure counts within the session

Status Badges: Unverified, Corrected, Verified on Overlay and Claim Record

Given an extracted field is in the QA queue, when displayed on the overlay, then it shows an Unverified badge; once accepted without change, it shows Verified Given a field is changed and approved during QA, when displayed, then it shows Corrected with a tooltip indicating previous value and reviewer Given a field’s QA decision is reverted (re-opened), when displayed, then the badge returns to Unverified and the claim-level QA status updates counts accordingly Given field badges update, when viewing the claim record, then aggregate counts of Unverified, Corrected, Verified are displayed and match the overlay Given a status change occurs, when queried via API, then the field status reflects the latest value within 1 second and persists across sessions

Audit Logging and Metrics for Reviewer Decisions

Given a reviewer accepts or corrects a field, when the decision is saved, then an immutable audit entry records actor, timestamp, fieldId, oldValue, newValue, rationale (optional), and SLA state Given an item is reassigned or deferred, when the action is taken, then the audit log records the transition with from/to assignee and due date changes Given decisions are made, when metrics are computed hourly, then throughput (items/hour per reviewer), average cycle time, and aging buckets (0–4h, 4–24h, >24h) are emitted Given metrics are emitted, when querying the metrics endpoint, then values are available within 5 minutes and align with audit log counts (±1% tolerance) Given audit data is requested, when filtering by claimId, reviewer, date range, and outcome, then results return within 2 seconds for up to 10k entries

APIs and Task Engine Integration for Review Items

Given a client calls GET /review-items with filters (claimId, status, assignee, reason), when valid, then the API returns a paginated list with totalCount and ETag headers Given a client updates a decision via PATCH /review-items/{id} with If-Match ETag, when the tag matches, then the decision is applied and the item status updates; when it does not, API returns 409 CONFLICT Given a review item is created, assigned, decided, or overdue, when events occur, then webhooks ITEM.CREATED, ITEM.ASSIGNED, ITEM.DECIDED, ITEM.OVERDUE are emitted to subscribed endpoints Given the task engine integration is enabled, when an item is created or reassigned, then a corresponding task is created/updated with deep link to the Tap Verify view Given API usage exceeds 600 requests/min per tenant, when additional requests arrive, then the API responds with 429 TOO_MANY_REQUESTS and Retry-After header

Live Factline

Stream transcribe voice notes as they’re recorded, auto-extracting policy numbers, VINs, parties, locations, causes of loss, and timestamps in real time. Shows live checkmarks when required fields are captured and instantly creates structured fields and triage tasks. Cuts rework and speeds submission for adjusters while giving intake leads immediate, high‑quality data to route without waiting.

Requirements

Streaming Transcription Engine

"As an adjuster, I want my voice notes transcribed in real time so that I can see what the system heard and avoid re‑recording or manual typing."

Description

Provide low‑latency, streaming speech‑to‑text for voice notes inside ClaimFlow, transcribing as the adjuster speaks with insurance‑domain vocabulary support (policy numbers, VINs, loss terms), punctuation, and token timecodes. Must operate on web and mobile recorders, sustain <500 ms end‑to‑end latency, handle brief network interruptions with seamless resume, and support optional local buffering for poor connectivity. Audio is processed securely, with configurable retention (transient or retained per compliance policy). Exposes a stable streaming API that emits interim and finalized transcript segments for downstream extraction.

Acceptance Criteria

Real-time transcription latency on web and mobile

Given an adjuster uses the in-app recorder on supported web and mobile clients and speaks continuously for up to 15 minutes When audio frames are streamed to the transcription engine under normal network conditions Then interim transcript updates are emitted with end-to-end latency <= 500 ms at the 95th percentile measured from frame arrival to interim text emission And average latency <= 300 ms And finalization of a spoken phrase occurs within 800 ms after detecting 500 ms of silence And these latency thresholds are met on latest Chrome (desktop), Safari (iOS), and Chrome (Android)

Domain vocabulary recognition accuracy

Given voice notes include policy numbers, VINs, names of parties, locations, causes of loss, and common insurance terms When the notes are transcribed in real time Then exact-match accuracy for policy numbers >= 95% in clear speech and >= 90% with moderate noise (SNR >= 10 dB) And exact-match accuracy for VINs >= 95% in clear speech and >= 90% with moderate noise, and recognized VINs pass ISO 3779 character rules (no I, O, Q) and length = 17 And recognized policy numbers match configured carrier regex patterns with precision >= 95% and recall >= 95% in clear speech And recall and precision for predefined loss-domain terms each >= 92% in clear speech

Token timecodes and punctuation correctness

Given finalized transcript segments are produced When inspecting their token lists and punctuation Then each token contains start and end timestamps aligned to the audio timebase with median absolute error <= 50 ms and 95th percentile error <= 100 ms against ground truth And token timestamps are monotonically non-decreasing without overlaps and cover the tokenized text And sentence-ending punctuation (period, question mark) F1 >= 0.90 and comma F1 >= 0.85 in clear speech And finalized segments are immutable; once emitted, their text, punctuation, and token timecodes never change

Seamless resume after brief network interruptions with local buffering

Given an active transcription stream with local buffering enabled And the client experiences a network interruption of up to 5 seconds When connectivity is restored Then the stream resumes automatically within 1 second without user action And no transcript loss or duplication occurs; buffered audio is sent and processed in order And interim-to-final segment continuity is preserved (no gaps in segment IDs or time ranges) And with local buffering disabled, interruptions up to 2 seconds still resume without data loss And the client can buffer at least 60 seconds of audio locally and backfill upon reconnect

Streaming API emits interim and final segments with stable contract

Given a client connects and streams audio via the transcription streaming API When messages are received Then the API emits distinct interim and final transcript segment messages with a stable, versioned schema And each final segment message includes: segment identifier, text, isFinal=true, token list with per-token timecodes, and segment timing metadata And segment identifiers are monotonically increasing within a stream And interim segments may be superseded by a final segment covering the same range; once a final segment is sent, it is never retracted or modified And the API allows graceful stream closure and delivers a completion message

Secure processing and configurable retention

Given retention policy is set to transient for a tenant When a transcription stream completes Then no audio or transcript content is persisted server-side and none is retrievable after stream closure And data are encrypted in transit using TLS 1.2+ during processing Given retention policy is set to retained:30d for a tenant When a transcription stream completes Then audio and transcripts are stored encrypted at rest and automatically deleted after 30 days per policy And retention configuration is enforced per stream and recorded in audit metadata without storing raw content

Real‑time Fact Extraction

"As an intake lead, I want key claim facts identified as I speak or listen so that I can act on reliable, structured data without waiting for a finished recording."

Description

Continuously extract and normalize key facts from the live transcript—policy numbers, VINs, parties, locations, causes of loss, and timestamps—updating outputs as transcript hypotheses change. Apply domain validations (VIN length and check digit, policy number formats by carrier, date/time normalization to UTC, location geocoding) and map to ClaimFlow’s standard taxonomy. Emit incremental structured fact events with confidence scores for consumption by the UI, field population, and workflow engines.

Acceptance Criteria

Real-time policy number extraction with carrier validation

- Given a live transcript stream containing a policy number utterance and detected carrier context, When the engine identifies a policy number candidate, Then it validates against the active carrier’s pattern repository and emits a PolicyNumber.created event with normalized_value, carrier_code, confidence >= 0.85 within 500 ms of the final token. - Given a candidate fails validation, When no carrier-specific pattern matches, Then no PolicyNumber event is emitted and a Validation.rejected diagnostic is recorded without blocking other extractions. - Given a previously emitted policy number is impacted by an ASR hypothesis change, When the token sequence changes, Then a PolicyNumber.updated event with updated normalized_value and incremented version is emitted and the prior version is marked superseded.

VIN extraction with length and check digit validation

- Given the live transcript includes a 17-character VIN candidate, When the engine detects it, Then it validates length=17 and ISO 3779 check digit, uppercases, removes spaces, and emits a Vin.created event with normalized VIN and confidence >= 0.90 within 500 ms of the final token. - Given a VIN candidate fails check digit or contains invalid characters (I, O, Q), When detected, Then no Vin event is emitted and a Validation.rejected diagnostic is recorded. - Given a VIN was previously emitted, When an ASR hypothesis update changes any VIN character, Then a Vin.updated event is emitted with incremented version and the prior version is marked superseded.

Party entities identification and normalization

- Given the transcript mentions an individual and a role (e.g., insured, claimant, witness), When detected, Then a Party.created event is emitted with fields role (from ClaimFlow taxonomy), full_name, optional contact info, and confidence >= 0.75 within 700 ms of mention. - Given multiple mentions refer to the same person, When coreference resolution determines equivalence, Then subsequent events reuse the same party_id and a Party.merged event is emitted consolidating attributes. - Given a previously emitted party becomes unsupported due to hypothesis updates, When confidence remains < 0.50 for 2 consecutive seconds, Then a Party.retracted event is emitted and the UI checkmark for Parties is cleared via the event flag.

Location extraction with geocoding

- Given the transcript mentions an address or place name, When detected, Then a Location.created event is emitted with formatted_address, lat, lon, geocode_provider, and confidence >= 0.80 within 1 second of the mention. - Given multiple geocode candidates are viable, When ambiguity exists, Then the top candidate is emitted and an ambiguity flag is set with n_candidates and alternative candidates included for disambiguation. - Given an ASR update changes the address tokens, When the resolved coordinates shift by >200 meters, Then a Location.updated event is emitted with incremented version and the prior is marked superseded.

Cause of loss classification mapped to taxonomy

- Given the transcript contains a description of the loss, When a cause can be inferred, Then a CauseOfLoss.created event is emitted with ClaimFlow taxonomy code (e.g., FIRE, WATER, THEFT, WEATHER, COLLISION, LIABILITY, OTHER) and confidence >= 0.70 within 500 ms of detection. - Given the classification confidence is < 0.70, When ambiguity persists, Then a CauseOfLoss.candidate event is emitted (not created) with confidence for UI hinting until confidence threshold is met. - Given subsequent transcript clarifies the cause, When confidence >= 0.70, Then the candidate is promoted via CauseOfLoss.created with the same cause_id and updated fields.

Timestamp extraction and UTC normalization

- Given the transcript mentions a date/time (absolute or relative) and a local timezone context is known, When detected, Then a Timestamp.created event is emitted with normalized_value in ISO 8601 UTC (Z), original_time_zone, and confidence >= 0.80 within 500 ms of detection. - Given no explicit timezone is mentioned, When adjuster profile provides a default timezone, Then normalization uses the profile timezone and source=profile is recorded. - Given a hypothesis update changes the date/time interpretation, When the normalized value differs, Then a Timestamp.updated event is emitted with incremented version and prior version marked superseded.

Incremental fact events and hypothesis reconciliation

- Given streaming transcript partials and finals, When new facts are extracted or existing facts change, Then events are emitted incrementally with schema fields: event_type, fact_type, fact_id, version, normalized_value, raw_span[start_ms,end_ms], confidence, created_at, correlation_id. - Given multiple updates occur to the same fact, When versions increment, Then ordering is strictly increasing and reprocessing the same fact_id+version is idempotent. - Given tokens are retracted by ASR, When a prior fact is invalidated, Then a Fact.retracted event is emitted within 300 ms and subscribers (UI and workflow engine) receive the event within 200 ms and 500 ms respectively, reflected by cleared UI checkmarks where applicable.

Live Completion Checkmarks

"As an adjuster, I want live checkmarks for required fields so that I know exactly what’s already captured and what remains while I’m recording."

Description

Display a real‑time checklist that reflects required fields captured from the ongoing transcription, showing clear states (missing, partial, complete) and a progress indicator. Each checkmark links to the source transcript snippet and confidence, highlights conflicts, and surfaces what to say next to fulfill unmet requirements. Works within the claim intake UI with accessibility and keyboard/voice controls.

Acceptance Criteria

Real-time Checkmark State Updates

Given live transcription emits an entity for a required field, when the entity is recognized or updated, then the corresponding checkmark state updates within 300 ms. Rule: State definitions — Missing: no candidate value with confidence >= 0.50; Partial: candidate exists but fails validation OR confidence in [0.50, 0.89]; Complete: normalized value passes validation rules and confidence >= 0.90. Rule: State transitions are Missing -> Partial -> Complete; regression only occurs if a conflict is detected. Rule: Visual indicator for each state includes icon, label text, and color with contrast >= 4.5:1; information is not conveyed by color alone. Given network latency adds 200 ms and events arrive out of order, when processing completes, then the final state reflects the highest-confidence validated value.

Progress Indicator Accuracy

Given a configuration with M required fields, when C fields are in Complete state, then the progress displays "C of M Complete" and percent = round((C/M)*100) bounded [0,100]. Rule: Partial fields do not increment C. Rule: Progress updates within 300 ms of any field state change. Rule: aria-label or tooltip announces "C of M required fields complete" for assistive tech. Given M = 0, then the progress shows "0 of 0 Complete" and 100%.

Source Snippet and Confidence Linking

Given a user activates a checkmark item, when the source is opened, then the transcript scrolls to and highlights the exact tokens used, showing timestamp (mm:ss) and confidence as a numeric value with two decimals. Rule: If multiple snippets contributed, a list shows each snippet with timestamp and individual confidence; the highest-confidence snippet is preselected. Rule: Keyboard support — Enter/Space opens source, Tab navigates snippets, Esc closes and returns focus to the originating checkmark. Rule: Voice commands "Open source" and "Close source" perform the same actions. Performance: 95th percentile time to open source view <= 400 ms.

Conflict Detection and State Handling

Given two or more distinct candidate values for the same field with confidence difference <= 0.05 and both >= 0.70, when they are detected, then the field is marked Conflict and its state is Partial. Rule: A conflict badge and message list all candidates with timestamp and confidence, and guidance includes "Please confirm correct [field]". Rule: Conflicted fields are excluded from the Completed count until resolved. Rule: When a validated value with confidence >= 0.90 is extracted that matches one candidate or the user confirms a candidate, then the conflict clears and the state becomes Complete within 300 ms. Rule: Conflict events are logged with time, values, and confidences for audit.

Next-Utterance Guidance

Given at least one required field is Missing or Partial, when the checklist updates, then the "What to say next" panel displays up to three prompts referencing unmet fields by name. Rule: Prioritization — Missing over Partial; then by workflow dependency; tie-breaker by time since last attempt. Rule: Guidance updates within 500 ms of any state change. Rule: Each prompt is <= 120 characters, plain language, and supports localization. Rule: Voice command "Read next prompt" reads the top suggestion; keyboard shortcut Alt+N focuses it.

Accessibility and Controls Compliance

Rule: Component conforms to WCAG 2.2 AA; focus indicators contrast >= 3:1, logical tab order, ARIA roles (list, listitem, status), and aria-live announcements on state change. Rule: Screen reader announcement format on update — "[Field label], [state], confidence [0.00–1.00 with two decimals]". Rule: Keyboard navigation — Up/Down move between checkmarks; Enter/Space toggles details; Alt+N triggers guidance; no pointer required. Rule: Voice control recognizes "Next requirement", "Previous requirement", "Open source", "Close source", "Read next prompt" with >= 95% accuracy on the curated test set. Performance: aria-live announcements occur within 500 ms of state changes.

Auto‑Populate Structured Fields

"As a claims manager, I want extracted facts to fill the form automatically so that submissions are complete faster and require fewer manual corrections."

Description

Automatically populate claim intake fields with extracted facts in real time, preserving user editability and providing conflict resolution. If a user edits a field, auto‑updates pause for that field with the option to re‑enable. Maintain version history with timestamps, source (voice vs. manual), and confidence, with a one‑click revert to prior values. Synchronize changes across the claim record and downstream systems without duplicate updates.

Acceptance Criteria

Real-time Auto-Populate on Voice Extraction

Given a live voice note is being transcribed and the NLP extracts a Policy Number with confidence >= 0.85 When the extraction completes Then the Policy Number field is auto-populated within 500 ms with the extracted value, tagged with source=voice, confidence, and timestamp Given a field is empty and multiple values are extracted sequentially When a higher-confidence value (increase >= 0.05) arrives before the user edits the field Then the field updates to the new value and the version history logs both versions Given the NLP emits a value that fails the field’s validation rules (e.g., invalid VIN checksum, non-numeric policy) When validation fails Then the field is not populated and a non-blocking warning is logged to telemetry

Edit Pause on Manual Override

Given a field was auto-populated from voice When the user edits the field Then auto-updates for that field pause immediately, an “Auto-update paused” indicator appears, and subsequent voice extractions do not change the field Given auto-updates are paused for a field When the user clicks “Re-enable auto-updates” Then the pause state clears and the next incoming valid extraction for that field is applied only if its timestamp is newer than the current value Given auto-updates are paused When the voice stream ends without further extractions Then the paused state persists until the user re-enables or the claim is submitted

Conflict Resolution Between Voice and Manual Values

Given a field has a manual value and a new conflicting voice value arrives When the conflict is detected Then a conflict banner appears showing both values with sources, confidence scores, and timestamps, and no automatic replacement occurs Given a conflict banner is displayed When the user selects “Keep Manual” or “Replace with Voice” Then the chosen value is committed, the other is archived in history, and the conflict is cleared within 300 ms Given the user takes no action for 60 seconds after conflict and routing rules require a value When the system proceeds Then the currently committed value (manual) is used for routing and an unresolved-conflict event is logged without blocking

Version History and One-Click Revert

Given a field has received at least one voice and one manual update When the user opens Version History Then entries show value, timestamp (UTC ISO-8601), source (voice/manual), user id (if manual), and confidence (if voice) Given a prior version is selected When the user clicks “Revert” Then the field value changes to that version within 300 ms, an audit entry is created, and the auto-update pause state remains unchanged Given a revert occurs When downstream synchronization runs Then only a single consolidated update is emitted to subscribers with the reverted value

Idempotent Synchronization to Downstream Systems

Given a field value changes via auto-populate, manual edit, or revert When synchronization is triggered Then a message is sent with a stable correlation id for the change and a deduplication key so downstream systems can ignore duplicates Given the same value is set multiple times due to retries When downstream receives repeated messages with the same deduplication key Then only one update is applied and others are acknowledged without side effects Given a downstream system is temporarily unavailable When retries occur Then exponential backoff with jitter is used up to 6 attempts over 15 minutes, the UI shows “Sync pending,” and upon success the status flips to “Synced” without creating duplicate updates

Required Fields Checkmarks and Triage Triggering

Given required fields are configured for intake When all required fields reach a valid committed state (pass validation, no unresolved conflicts) Then a green checkmark appears for each and triage tasks are created within 1 second Given a required field later becomes invalid due to user edit or revert When validation fails or a conflict arises Then its checkmark is removed, dependent triage tasks are paused or rescinded per configuration, and the UI indicates which requirement is unmet Given a required field is valid but auto-updates are paused When a new higher-confidence voice value arrives Then the field does not change and no checkmark toggling occurs unless the user re-enables updates

Instant Triage Task Routing

"As an intake lead, I want tasks created and routed as soon as key facts are captured so that I can move claims forward immediately."

Description

Create and route triage tasks the moment the minimal data set is satisfied, using configurable workflow rules (LOB, severity, geography, availability). Ensure idempotency and de‑duplication when facts are updated, and update task metadata as higher‑confidence values arrive. Push tasks to the appropriate queues in real time and notify assignees, enabling routing without waiting for the recording to finish.

Acceptance Criteria

Minimal Data Set Trigger

Given a live transcription session is in progress and the minimal dataset is configured as Policy Number, Loss Location, Loss Timestamp, Cause of Loss, and LOB with minimum confidence 0.80 When the final required field is captured meeting the confidence threshold Then a triage task is created within 2 seconds And the task includes the captured fields and an initial Severity bucket per rules And no user action is required to create the task And the task status is set to "New" and the creation event is logged with timestamp and source "Live Factline"

Rule-Based Queue Routing

Given active workflow rules consider LOB, Severity, Geography, and Adjuster Availability with defined priority ordering When a triage task is created Then the task is routed to the queue defined by the highest-priority matching rule And if multiple rules match, the rule with the highest priority value is applied deterministically And if no adjuster is available in the target queue, the task is placed in the configured fallback queue and a routing alert is logged And the routing decision includes an attached rules evaluation trace on the task

Idempotent Task Creation

Given a triage task has already been created for the current intake session When additional or corrected facts arrive that do not invalidate the minimal dataset Then no additional triage task is created for the same session and claim context And the system maintains exactly one open triage task per deduplication key defined as Policy Number + Loss Timestamp (±15 minutes) + Loss Location And repeated detections within 5 minutes update the existing task only

Metadata Update on Confidence Upgrade

Given a triage task exists with preliminary extracted values When a higher-confidence value (> previous by at least 0.05) or a corrected normalized value for a field (e.g., VIN, address) arrives Then the task's corresponding fields are updated within 1 second and the previous value is retained in an immutable audit log And routing rules are re-evaluated; if the target queue changes, the task is moved and the previous assignee is unassigned And the current assignee (if any) receives a single consolidated update notification per 60-second window

Cross-Source De-duplication

Given facts may arrive concurrently from voice transcription and message input tied to the same intake ID When the minimal dataset is satisfied by any combination of sources Then exactly one triage task is created And subsequent minimal-dataset events referencing the same deduplication key are merged into the existing task without creating duplicates And merge actions are logged with source attribution

Real-Time Queue Push and Notification

Given a triage task is created or reassigned due to routing evaluation When routing completes Then the task appears in the destination queue within 2 seconds And the assigned user or queue subscribers receive an in-app notification within 2 seconds and an email within 30 seconds And notification delivery success or failure is logged with correlation to the routing event And failed notifications are retried up to 3 times with exponential backoff

Confidence & Clarification Prompts

"As an adjuster, I want the system to ask me to confirm uncertain details so that the data is accurate without slowing me down."

Description

Implement confidence thresholds and ambiguity handling that trigger inline prompts when values are uncertain or conflicting. Provide concise confirmation choices (e.g., policy number A or B), dictate‑back confirmation, and quick correction via voice or tap. Unresolved items are clearly flagged and excluded from auto‑routing rules until confirmed, with a seamless fallback to manual entry.

Acceptance Criteria

Low-Confidence Inline Prompt for Required Field

Given Live Factline is transcribing a voice note and a candidate value for a required field (e.g., policy number) is extracted with confidence below the configured prompt threshold When the extraction event is emitted Then an inline clarification prompt appears within 500 ms showing the top 2–3 candidates and a "Manual entry" option And when the user confirms a candidate by tap or voice Then the structured field is set, marked confirmed, and the field’s live checkmark turns green within 300 ms And the unresolved flag and prompt are cleared for that field And the action is audit-logged with before/after value and confidence score

Conflict Prompt for Multiple Sources

Given two different candidate values are extracted for the same field from different sources (e.g., audio vs. photo OCR) and both exceed the candidate floor When the conflict is detected Then a prompt lists both values with their sources and confidence percentages, plus "None of these" and "Manual entry" And when the user selects one or says "Use first/second" Then the selected value is set and marked confirmed, the alternative is archived as rejected, and a "conflict_resolved" event is logged And if "None of these" is chosen, the field remains unresolved and flagged

Dictate-Back Confirmation Mid-Confidence

Given a field is extracted with confidence between the prompt threshold and the auto-confirm threshold When dictate-back is triggered Then the system reads back or displays the value and asks for "Confirm" or "Correct to <value>" And on "Confirm" (voice or tap) the field is marked confirmed and checkmarked within 300 ms And on "Correct to <value>" the system updates the field to the spoken/typed value, marks it confirmed, and displays the updated checkmark within 500 ms And if speech is not recognized after 2 attempts or the user says "I'll type", focus moves to manual entry input for that field

Exclude Unconfirmed Fields from Auto-Routing

Given one or more required fields are unresolved When auto-routing rules evaluate the intake Then the case is excluded from auto-routing and labeled with a "Needs confirmation" badge And triage tasks dependent on unresolved fields are not created And when all required fields are confirmed Then routing proceeds on the next evaluation cycle (<= 1 second) without page refresh

Prompt Dismissal Behavior and Re-prompt Strategy

Given a clarification prompt is shown for a field When the user dismisses the prompt without confirming a value Then the field remains unresolved with a visible red flag and no checkmark And the unresolved count increases in the outstanding items panel And the same prompt will not reappear for at least 60 seconds unless a higher-confidence candidate or a new candidate is extracted

Clarification Audit Trail and Telemetry

Given any clarification interaction occurs (prompt shown, confirmation, correction, manual entry, timeout) When the event completes Then an audit record is saved with timestamp, user id, session id, field name, old value, new value, confidence scores, source(s), and end-to-end latency And 99% of audit records persist within 2 seconds, with a missing-log rate below 0.1% And PII fields are masked in logs according to policy

Per-Field Confidence Threshold Configuration

Given an admin has set per-field values for auto-confirm threshold and prompt threshold (0.0–1.0, with prompt threshold <= auto-confirm) When a field is extracted with confidence >= auto-confirm threshold Then it is auto-confirmed without any prompt And when confidence is between prompt threshold (inclusive) and auto-confirm threshold (exclusive) Then dictate-back confirmation is used And when confidence < prompt threshold Then a candidate selection prompt is shown And threshold changes take effect for new extractions within 10 seconds of saving

Admin Rules & Required Fields

"As an operations admin, I want to configure required fields and routing rules so that Live Factline adapts to our lines of business and compliance needs."

Description

Offer an admin console to configure required fields by line of business and jurisdiction, define extraction patterns and synonyms, set validation thresholds, and author routing rules. Support versioned rule sets with effective dates, test mode with sample audio/transcripts, and export/import for change control. Changes propagate instantly to Live Factline without redeploying code.

Acceptance Criteria

Configure Required Fields by Line of Business and Jurisdiction

- Given I am an Admin in the Rules console, When I select Line of Business "Auto" and Jurisdiction "CA", Then I can add, remove, and order required fields including Policy Number, VIN, Loss Location, Parties, and Cause of Loss. - Given a required field set is saved and published for "Auto-CA", When a new Live Factline session starts with LOB=Auto and jurisdiction=CA, Then the session reflects the configured required fields and shows live completion indicators for each. - Given the required set includes "VIN", When Live Factline cannot extract a VIN or the extracted VIN fails validation, Then the field is flagged as Required-Missing and routing rules that depend on it do not execute. - Given field dependencies are configured (e.g., "Injury Details" required if "Injury Indicated" = true), When the dependency condition evaluates to true, Then the dependent field becomes required; otherwise it remains optional. - Given conflicting required field definitions exist across scopes, When both a global and jurisdiction-specific rule apply, Then the most specific scope overrides the broader one and the system logs the resolution.

Define Extraction Patterns and Synonyms

- Given I author a Policy Number pattern "POL-[0-9]{8}" and synonyms ["policy #","policy no.","policy number"], When test mode runs on a transcript containing "policy # POL-12345678", Then the Policy Number field is extracted as "POL-12345678" with the synonym recognized. - Given patterns include capture groups and normalization, When extraction occurs, Then the stored value is normalized using configured rules (e.g., strip spaces, uppercase) and the raw match is retained in metadata. - Given multiple patterns could match the same field, When extraction runs, Then the highest-scoring match is selected and other candidates are stored as alternates with scores. - Given a synonym maps to multiple fields, When ambiguity occurs, Then the engine applies disambiguation priority as configured and logs the resolution; if unresolved, it flags for manual review in test mode.

Set Validation Confidence Thresholds

- Given I set Policy Number confidence threshold to 0.92 and VIN to 0.95, When extraction scores are below their thresholds, Then the fields remain Unverified and do not earn a required-field checkmark in Live Factline. - Given an extraction score meets or exceeds the threshold, When the field value is set, Then the field is marked Verified and the checkmark displays in Live Factline in under 1 second. - Given multiple candidates exceed threshold, When tie-breaking is needed, Then the engine selects the candidate with the highest score; on equal scores it applies recency in transcript as tie-breaker. - Given thresholds are updated and published, When a new session begins, Then the new thresholds are applied without requiring application redeploy.

Author Routing Rules by Conditions

- Given I create a routing rule "If LOB=Auto AND Jurisdiction=CA AND text contains 'injury' OR InjuryScore >= 0.7 Then create task 'Bodily Injury Triage' priority High and assign queue 'BI-CA'", When a transcript meets these conditions, Then the task is created with the configured attributes and appears in the target queue. - Given a routing rule references a required field that is missing or Unverified, When evaluation runs, Then the rule is skipped or delayed per configuration and an audit log entry explains the reason. - Given rule execution order is configured, When multiple rules could fire, Then they execute in the specified order and respect stop/continue behaviors. - Given I disable a rule and publish, When new sessions start, Then the rule no longer fires and the change is recorded in the audit log.

Versioned Rule Sets with Effective Dates and Preview

- Given rule set v1 is active and I create v2 with Effective From 2025-10-15T00:00:00Z, When I preview using a time-travel selector set to 2025-10-16, Then the engine evaluates test inputs against v2 and shows differences from v1. - Given v2 is published with a future effective date, When the current time is before the Effective From, Then v1 continues to serve production sessions and v2 remains scheduled. - Given the Effective From time is reached, When a new Live Factline session starts, Then v2 is applied within 5 seconds without code redeploy and an audit event records the activation. - Given a session started before activation, When it continues after activation, Then it remains pinned to its start-version to avoid mid-session rule changes.

Test Mode with Sample Audio/Transcripts

- Given I upload sample audio and/or paste a transcript in test mode, When I run the test against a selected rule set version, Then the system displays extracted fields, per-field scores vs thresholds, required-field status, and routing rule outcomes. - Given I run the same test against multiple versions (v1 vs v2), When I view results, Then the system highlights differences in extractions, verifications, and routing decisions. - Given test mode is used, When tests are executed, Then they do not impact production sessions, queues, or tasks, and results are retained in a test log with timestamps and the executing user. - Given a test case is saved, When another admin loads it, Then inputs and expected assertions can be re-run and pass/fail status is indicated.

Export/Import for Change Control

- Given I click Export for a rule set version, When the file is generated, Then it downloads as signed JSON including version metadata, effective dates, checksums, and a ruleset ID. - Given I import a ruleset file, When validation runs, Then the system verifies schema, signatures, and referential integrity; on success it creates a new draft version; on failure it blocks import and lists errors. - Given a draft created via import, When I preview and publish it, Then the audit log records who imported, who published, timestamps, and an optional change reason. - Given I need to roll back, When I select a prior version and publish it with immediate effect, Then it becomes active within 5 seconds and the change is logged.

Gap Coach

Guides adjusters mid‑note with subtle prompts when essential details are missing based on claim type, carrier rules, and SLA requirements. Offers one‑tap scripts (e.g., “Please confirm policy number and contact phone”) and logs completions automatically. Reduces follow‑ups, boosts first‑pass completeness, and standardizes notes across teams.

Requirements

Real-time Note Parsing & Gap Detection

"As a claims adjuster, I want real-time guidance while writing notes so that I capture all required details the first time and avoid follow-ups."

Description

Perform low-latency, on-the-fly NLP analysis of in-progress notes (typing or dictation) to detect missing required details based on claim type, carrier rules, jurisdiction, and SLA checklists, and surface context-aware inline prompts with confidence thresholds. Support multi-channel inputs (free-text notes, voice transcripts, photo captions, and message threads) and maintain sub-200 ms p95 prompt latency with graceful degradation offline by queuing checks. Integrate with ClaimFlow’s claim schema, existing rules engine, and event bus to synchronize detected gaps as structured fields and emit events for downstream automation.

Acceptance Criteria

Inline Prompting While Typing — p95 <200 ms

Given an adjuster is actively entering text in the claim note editor with network connectivity and a claim context (type, carrier, jurisdiction, SLA) loaded When the system detects a missing required detail Then an inline prompt is rendered within 200 ms at p95 and within 350 ms at p99, measured over a rolling window of at least 1,000 prompt events per environment And the prompt references the specific missing field (by schema field label) and offers a one-tap script when available for that field And new prompts are rate-limited to a maximum of one new prompt every 2 seconds per user to avoid flicker

Confidence-Thresholded Prompting

Given a detection score is below the configured rule threshold When evaluating whether to show a prompt Then the prompt is suppressed and a suppression audit record is written with rule_id, score, threshold, and timestamp Given a detection score is equal to or above the configured rule threshold When evaluating whether to show a prompt Then the prompt is displayed and includes the reason (rule label) and a confidence category derived from score bands And when content changes cause the score to cross the threshold Then the prompt is shown or withdrawn within 100 ms of the score change And the system does not show duplicate prompts for the same field and rule within a session unless the prior prompt was resolved or dismissed

Multi-Channel Input Aggregation and Gap Detection

Given the claim has inputs from free-text notes, streaming voice transcripts, photo captions, and message threads within the current session When new content arrives on any channel Then it is processed and added to the analysis context within 300 ms at p95 And if the required detail is present in any channel Then no prompt for that field is displayed And streaming voice partial hypotheses do not trigger prompt resolution; only finalized transcript segments can resolve or dismiss a gap And detected gaps consider all channels in aggregate so that providing a detail in any channel suppresses the corresponding prompt within 200 ms at p95

Offline Degradation with Queued Checks and Retry

Given the device loses connectivity or the NLP/rules services are unavailable When the user continues typing Then checks are enqueued locally and a non-blocking offline indicator is shown within 100 ms And the queue persists across app restarts and preserves FIFO order for at least 500 pending items When connectivity is restored Then queued checks are replayed and corresponding prompts are delivered within 2 seconds at p95, and the offline indicator is cleared And if a queued check becomes obsolete due to subsequent content changes Then it is dropped with an audit record noting the drop reason and superseding content hash

Rules-Driven Detection Mapped to Claim Schema

Given a claim context with type, carrier, jurisdiction, and SLA checklist available When a gap is detected Then the applied rule_id and rule_version are recorded and the gap maps to a ClaimFlow schema field_id and path And 100% of emitted gaps include field_id, rule_id, confidence_score, and source_channel in structured storage And when the mapped field becomes populated by user input or integration Then the corresponding gap state transitions to resolved automatically within 200 ms at p95

Gap Events Emitted on Event Bus

Given a gap is created, updated, resolved, or dismissed When the gap state changes Then an event is published on the event bus within 150 ms at p95 with payload containing event_type, claim_id, field_id, rule_id, confidence_score, state, actor_id (if any), source_channel, and ISO 8601 timestamp And events are delivered at-least-once with an idempotency key for de-duplication by consumers And if publishing fails Then the system retries with exponential backoff up to 5 attempts and moves the message to a dead-letter topic on final failure

Prompt Action Logging and Dismissal Handling

Given a prompt is shown When the user taps a one-tap script Then the script text is inserted into the note at the cursor position and the gap transitions to pending verification until the required detail is present, after which it is resolved automatically When the user provides the missing detail in any channel Then the completion is logged automatically with claim_id, field_id, actor_id, method=auto, and timestamp When the user dismisses a prompt as Not Applicable or Known Deferred Then a reason selection is required and the gap state becomes dismissed, and it does not reappear unless claim context changes make the rule applicable again And if content changes remove a previously provided value Then the associated gap reopens within 200 ms at p95

One-tap Script Suggestions

"As a claims adjuster, I want one-tap suggested scripts for missing details so that I can ask for the right information quickly and consistently."

Description

Offer curated, carrier- and claim-type-specific one-tap scripts for common missing items (e.g., policy number, contact phone, loss location) that can be inserted into notes or outbound messages with a single action. Support token substitution (claim ID, policyholder name), localization, and accessibility, and allow team-level curation with versioned templates. Track script performance to rank most effective prompts and ensure consistent, compliant language across teams.

Acceptance Criteria

Targeted Script Suggestions Based on Carrier and Claim Type

Given an adjuster is composing a claim note for carrier Y with claim type T and required details (e.g., policy number, contact phone, loss location) are missing per rules When the system detects one or more missing details Then display up to 3 relevant one‑tap scripts mapped to carrier Y and claim type T within 300 ms of keystroke And do not display scripts not mapped to carrier Y or claim type T And remove a suggestion immediately when its corresponding detail is captured in the note or claim record And show a short reason tag indicating the detected gap (e.g., "Missing: Policy Number")

One‑Tap Insertion into Notes and Outbound Messages

Given targeted suggestions are visible in the editor When the user selects a script via click, tap, or Enter Then the fully rendered text inserts at the current cursor location in the note editor in under 100 ms And an Undo action is available for at least 5 seconds to revert the insertion And insertion is idempotent (a single selection inserts a single instance) And the user can choose Send via channel submenu (SMS, email, in‑app) to insert into the selected outbound composer with a single action

Token Substitution with Fallbacks and Preview

Given a template that includes tokens such as {claim_id}, {policyholder_first_name}, {adjuster_name}, {loss_date:MMM d, yyyy} When the script is previewed or inserted Then all tokens resolve from the active claim context using specified formats and time zone And unresolved optional tokens are replaced by configured fallbacks (e.g., {policyholder_first_name} → "Policyholder") and visibly flagged in preview And if a required token lacks a value and no fallback exists, block insertion and display an actionable error within 100 ms And token rendering respects locale-specific formats for dates and names

Localization and Language Routing

Given the user's UI locale is L and the policyholder's preferred language is P When a suggestion is displayed Then the suggestion label and preview appear in locale L And when inserted for outbound to the policyholder, the script content uses language P if a P translation exists; otherwise it falls back to English with an "EN" badge And right‑to‑left languages render correctly (text direction, cursor behavior, punctuation) And phone numbers, dates, and currency inside tokens format per the active locale And ≥95% of published templates for a team have translations for all configured locales

Accessibility and Keyboard‑Only Operation

Given a keyboard‑only or screen reader user is interacting with suggestions When suggestions appear Then all suggestion items are reachable via Tab/Shift+Tab with visible focus indicators And items are announced with appropriate ARIA roles and names including gap type and the first 50 characters And actions are operable via Enter/Escape/Arrow keys without a mouse And color contrast for text and controls is ≥ 4.5:1 and touch targets are ≥ 44×44 px And automated accessibility checks (axe‑core) report 0 critical violations for suggestion and insertion flows

Team Curation and Versioned Templates with Compliance Locks

Given a team admin with Template Manager role is managing templates When creating or editing a template Then they can set target carriers, claim types, locales, channels, and an allowed token list, and define compliance‑locked segments And upon Publish, a new semantic version (MAJOR.MINOR.PATCH) is assigned with changelog and approver recorded And published changes propagate to end users within 5 minutes And rollback to any prior version is possible with full audit trail And non‑admin users cannot edit locked segments or publish templates And template visibility is scoped to the team with optional carrier‑specific overrides

Performance Tracking, Ranking, and Auto‑Logging

Given scripts are being suggested and used across claims When a script is shown, inserted, sent, and the missing field is subsequently completed Then the system records impression, insertion, send, response, and completion events with timestamps within ≤5 seconds And computes an effectiveness score daily per carrier/claim type: 50% weight completion within 24h, 30% response within 24h, 20% insertion rate And ranks scripts accordingly, surfacing the top 3 by default in suggestions And a dashboard exposes metrics filterable by date range, carrier, claim type, and team, with CSV and API export that excludes PII And all usage events are appended to the claim audit log including user, script version, channel, and outcome

Rule & SLA Configuration Console

"As an operations admin, I want to configure required fields and SLAs by carrier and claim type so that Gap Coach prompts align with our rules and compliance needs."

Description

Provide an admin UI to define, prioritize, and version required data elements per carrier, line of business, jurisdiction, and claim phase, with effective dates and exceptions. Map rules to prompt templates and severity (blocking vs. advisory), simulate outcomes on sample claims, and publish changes safely. Enforce RBAC, audit every change, and integrate with the existing ClaimFlow rules engine to avoid duplicating logic.

Acceptance Criteria

Define and prioritize multi‑dimension rules with effective dates and exceptions

Given a Rules Admin is creating a rule with required data elements and context (carrier, line of business, jurisdiction, claim phase) When they enter a start date (and optional end date), set a numeric priority, and add zero or more exceptions based on additional attributes Then the rule saves with a unique ID, required fields validated, end date >= start date, and is associated to the specified context. Given overlapping rules exist for the same required element and context When the system resolves applicability Then the rule with higher numeric priority applies; if priorities tie, the rule with more specific context applies; if still tied, the most recently Published version applies. Given a rule would duplicate an identical active rule (same required element, same context, same date range) When attempting to save Then the system blocks the save with a descriptive error listing the conflicting rule IDs. Given a date range is expired or not yet effective When retrieving effective rules for a claim context at a timestamp Then only rules active at that timestamp are returned.

Map rules to prompt templates and severity (blocking vs advisory)

Given a rule exists When a Rules Admin maps one or more prompt templates (with locale codes) and selects a severity of Blocking or Advisory Then the mapping saves and each template passes variable interpolation validation (all placeholders resolve to available claim fields). Given a rule has severity Blocking When a simulation or evaluation is performed Then the output marks the prompt as blocking; for Advisory, the output marks it as advisory. Given a template is missing for the default locale When attempting to publish Then the system blocks publishing and lists the missing locale(s).

Versioning, publish scheduling, and rollback workflow

Given a ruleset is in Draft When a Rules Admin submits it for Review and a Reviewer approves Then the state becomes Ready to Publish and all items are immutable until published or sent back to Draft with comments. Given a ruleset is Ready to Publish When a Publisher schedules an effective start timestamp (with timezone) and confirms Then a Published version is created that becomes active at that timestamp and only one active version per scope (carrier + line of business + jurisdiction + claim phase) is allowed. Given a Published version is active When a rollback is initiated to a prior Published version with a reason Then the prior version is re-published with a new effective timestamp, the current version is superseded, and both events are recorded in the audit trail. Given an attempt is made to edit a Published version When saving Then the system blocks the edit and offers “Create New Version” that clones to Draft.

Simulate rules on sample claims across versions

Given a Draft ruleset and a set of sample claims (uploaded or selected by ID) When the user runs a simulation Then the system returns for each claim: the list of missing required elements, associated prompt templates, severity, triggering rule IDs, and a summary count of Blocking vs Advisory. Given both Current Production and Draft versions exist When a differential simulation is run Then the output highlights adds/removes/changes in prompts and severities per claim and provides a downloadable CSV. Given invalid sample claim data is provided When the simulation runs Then the system reports validation errors per claim without blocking valid claims from running.

RBAC enforcement for configuration console and APIs

Given roles System Admin, Rules Admin, Reviewer, Publisher, and Viewer When each role accesses features Then permissions are enforced as follows: System Admin (all), Rules Admin (create/edit rules and run simulations), Reviewer (approve/reject), Publisher (schedule/publish/rollback), Viewer (read-only). Given an unauthorized user attempts a restricted action When the action is invoked via UI or API Then the system returns 403 Forbidden with an error code, no data is changed, and the attempt is logged in the audit trail. Given a user session is established When the UI renders Then only features available to the role are visible and actionable; hidden actions cannot be reached via direct URL.

Audit trail for all rule changes

Given any create/update/delete, state transition, publish, rollback, or simulation action When the action completes Then an immutable audit entry is recorded containing actor ID, timestamp (UTC), action type, entity IDs, before/after diffs, reason/comment (if provided), and source (UI/API). Given an auditor queries the audit log When filtering by date range, actor, carrier, ruleset ID, or action type Then matching entries are returned sortable and exportable to CSV. Given an attempt is made to alter or delete an audit entry When performed by any role Then the system blocks the action and records the attempted tamper in the audit log.

Integration with existing ClaimFlow rules engine (no logic duplication)

Given a ruleset is Published When the publish job completes Then the existing ClaimFlow rules engine is updated via its configuration API, and a version hash and engine config ID are stored with the ruleset. Given the simulation feature runs When evaluating rules Then it invokes the same rules engine evaluation API used at runtime and returns identical results for a reference set of claims as a direct engine call. Given the rules engine API is unavailable at publish time When a publish is attempted Then the system aborts publishing, reverts to the prior state, surfaces a clear error, and records the failure in the audit trail.

Auto-Completion Logging & Audit Trail

"As a compliance officer, I want a complete audit trail of prompts and their outcomes so that we can demonstrate adherence to carrier rules and SLAs."

Description

Automatically record every prompt event (shown, accepted, edited, dismissed, resolved) with timestamp, user, claim ID, prompt ID, and resulting data captured. Write outcomes back to the claim record as structured tags and note annotations, expose an audit view for compliance, and provide export to the data warehouse. Support configurable retention and privacy controls for PII with encrypted storage and access logging.

Acceptance Criteria

Prompt Event Lifecycle Logging

- Given a Gap Coach prompt is shown within a claim, When the prompt is displayed to the user, Then an event with type "shown" is recorded with timestamp (UTC ISO 8601 with ms), user_id, claim_id, and prompt_id. - Given a user accepts a prompt, When acceptance occurs, Then an event with type "accepted" is recorded with the same identifiers and resulting_data_captured stored as structured JSON. - Given a user edits a prompt response or one‑tap script, When the edit is saved, Then an event with type "edited" is recorded including a before/after diff of resulting_data_captured. - Given a user dismisses a prompt, When dismissal occurs, Then an event with type "dismissed" is recorded and includes an optional reason if provided. - Given a prompt’s required details are completed, When the prompt is resolved, Then an event with type "resolved" is recorded. - Given transient storage failure, When an event write fails, Then the system retries until success and persists the event within 60 seconds without creating duplicate records (idempotent by event key). - Given high‑volume logging at ≥100 events/second per tenant, When events are written, Then p50 write latency ≤200 ms and p99 ≤1 s.

Write Outcomes to Claim Record as Structured Data and Note Annotations

- Given a prompt outcome of type accepted or resolved with resulting_data_captured, When write‑back occurs, Then structured tags are created/updated on the claim record to mirror each data field and value. - Given write‑back occurs, When the claim note is updated, Then a note annotation is appended containing prompt_id, event_type, user_id, and timestamp, and is viewable in the claim timeline. - Given a subsequent edit to the same prompt outcome, When changes are saved, Then tags are updated in place and a new annotation is added, preserving prior values in the audit log. - Given duplicate acceptance events, When processed, Then tags and annotations remain idempotent (no duplicate tags/notes; latest value retained). - Given UI and API consumers, When they query the claim record, Then updated tags and the latest annotation are visible within 1 second of the event p95.

Audit View for Compliance

- Given a user with the ComplianceViewer role, When they open the Audit Trail for a claim, Then all related prompt events are listed chronologically with columns event_type, timestamp, user_id, prompt_id, and a flag indicating presence of resulting_data_captured. - Given filter inputs (date range, user_id, prompt_id), When filters are applied, Then the grid updates correctly within 2 seconds p95 and reflects only matching events. - Given an event row is selected, When details are opened, Then the full event payload is viewable subject to PII masking rules and role permissions. - Given a user without Audit access, When they request the Audit Trail, Then access is denied with a 403 and the attempt is access‑logged. - Given the Export action, When the user exports from the Audit View, Then a CSV containing the visible (filtered) events is generated and downloaded, respecting PII masking settings.

Data Warehouse Export

- Given new events exist, When the scheduled export runs hourly or an on‑demand export is triggered, Then all events since the last successful watermark are delivered to the configured warehouse destination with fields: event_type, timestamp, user_id, claim_id, prompt_id, resulting_data_captured. - Given a completed export window, When reconciliation runs, Then row count parity between source and destination for that window is 100% within 1 hour; otherwise an alert is raised and retry/backfill occurs automatically. - Given a backfill request for a historical date range, When executed, Then all events in range are exported exactly once (no duplicates) and watermarks advance correctly. - Given PII export policy configuration, When exporting, Then PII fields are included only when the destination is authorized for PII; otherwise those fields are redacted or omitted consistently.

Configurable Retention and Legal Holds

- Given a tenant‑level retention policy in days, When events exceed the configured retention period, Then the system purges or anonymizes those events per policy and creates a deletion audit log entry. - Given a legal hold is applied to a tenant, claim, or prompt_id, When retention would otherwise purge events, Then those events are retained until the legal hold is cleared. - Given a retention policy change, When a stricter policy is saved, Then subsequent purge cycles honor the new policy and do not resurrect previously deleted data. - Given routine purge execution, When the purge job runs, Then it processes at least 10,000 events per minute without referential integrity violations or partial deletes.

PII Encryption, Masking, and Access Logging

- Given fields labeled as PII in resulting_data_captured or note annotations, When stored, Then they are encrypted at rest and transmitted only over TLS 1.2+. - Given a user without PII permission, When viewing the Audit View or claim annotations, Then PII values are masked (e.g., last 4 only) while non‑PII remains fully visible. - Given any successful or failed read of PII, When it occurs, Then an immutable access log entry is created with user_id, timestamp, claim_id, action, and purpose code, retained per policy. - Given cryptographic key rotation, When keys are rotated, Then previously stored PII remains decryptable by authorized services with zero data loss and rotation is recorded in system audit logs. - Given a data subject erasure request, When executed, Then PII fields within events and annotations are purged or irreversibly anonymized within 30 days while preserving non‑PII audit context.

Prompt Throttling & User Controls

"As a claims adjuster, I want controls that minimize distracting prompts so that I can stay focused while still getting help when it matters."

Description

Reduce noise by batching related suggestions, suppressing duplicates, and rate-limiting prompts within a session based on context and user behavior. Provide user controls to snooze, mute, or mark prompts as not applicable with a reason, and learn from these signals to refine future suggestions. Ensure prompts are keyboard-first, non-blocking, and never obstruct text entry or screen readers.

Acceptance Criteria

Batch related suggestions within 5 seconds into a single prompt

Given three or more suggestion triggers occur within a 5-second window for the same claim and note context, When Gap Coach generates prompts, Then exactly one batch prompt card is displayed with a badge showing the total count, And the card groups suggestions by category (e.g., policy, contact, coverage), And no additional standalone prompt cards are shown for those triggers, And a single notification is emitted, And telemetry records a batch_id with item_count equal to the number of triggers.

Suppress duplicate prompts in-session using context hash

Given a prompt with normalized_key K and context_hash H has been shown in the current user session, When a prompt with the same normalized_key K and context_hash H would fire again, Then it is suppressed and not shown again in this session, And the suppression is logged with reason "duplicate_in_session", And if the context_hash changes, Then the prompt may be shown once with an "updated" badge.

Adaptive rate-limiting based on user engagement

Given Gap Coach is active in a note-taking session, When prompts are eligible to be shown, Then no more than one prompt (or batch card) is shown every 30 seconds (baseline), And no more than five prompts total are shown in any rolling 15-minute window, And if the user dismisses or ignores two prompts consecutively, Then the interval increases to 90 seconds until a prompt is accepted, And if the user manually opens the suggestions panel, Then the next eligible prompt may bypass the interval once, And prompts older than 2 minutes are dropped or merged into the next batch.

User controls: snooze, mute, and not applicable with reason

Given a prompt card is focused, When the user selects Snooze, Then they can choose 5 minutes, end of session, or until claim stage changes, And the prompt does not reappear until the selected snooze condition is met. Given the user selects Mute, Then the user can choose scope: this claim or this claim type for this user, And prompts with the same normalized_key do not appear within the selected scope thereafter. Given the user selects Not Applicable, Then a required reason must be captured from a controlled list with optional free-text (min 5 chars), And the prompt is dismissed and recorded with reason. For all actions, Then events are logged with user_id, claim_id, normalized_key, action, scope_or_reason, and timestamp.

Keyboard-first, non-blocking prompt experience

Given the adjuster is typing in the note field, When a prompt is generated, Then focus remains in the note field and no modal is shown, And the prompt appears in a side panel or inline area that does not overlap the text caret region, And 95th percentile keystroke-to-render latency remains under 50 ms with prompts active, And the following shortcuts work: Ctrl+Shift+S (Snooze), Ctrl+Shift+M (Mute), Ctrl+Shift+N (Not Applicable), Ctrl+Shift+P (Open prompts panel), And pressing Esc dismisses the active prompt card without affecting the note content, And tab order proceeds Note Field -> Latest Prompt Card -> Prompt Actions in logical sequence.

Accessibility: screen reader compatible and unobstructed text entry

Given a prompt is generated, When a screen reader is active (NVDA or JAWS on Windows, VoiceOver on macOS), Then the prompt is announced via an ARIA live region with politeness "polite" without stealing focus, And all controls expose accessible names, roles, and states, And color contrast for text and interactive elements is at least 4.5:1, And all actions are operable via keyboard only, And no prompt overlaps or obstructs the note entry region at 1280x800 and 1920x1080 resolutions, And automated accessibility audits (axe-core) report zero critical violations on the prompts UI.

Learning from feedback signals to refine future suggestions

Given a user marks a prompt with normalized_key K as Not Applicable with reason R three times across separate sessions for the same claim type, When the user starts subsequent sessions for that claim type, Then the display frequency of prompts with key K is reduced by at least 80% for that user over the next five sessions, And if a prompt is muted for a claim, Then prompts with key K do not reappear for that claim in any session, And nightly model updates incorporate snooze/mute/NA signals into suppression rules, And an audit report is available per user and claim type showing pre/post display rates and the effective date of rule updates.

Workflow Handoff for Unresolved Gaps

"As a claims manager, I want unresolved gaps to generate actionable tasks so that no claim progresses without critical information."

Description

When notes are finalized with unresolved blocking gaps, automatically create tasks in ClaimFlow workflows with assignees, due dates, and SLA timers, and attach the most relevant scripts for outreach. Prevent stage advancement when required details are missing (based on rule severity), notify stakeholders, and expose APIs/webhooks for downstream systems to consume unresolved gap lists.

Acceptance Criteria

Auto-create workflow tasks for unresolved blocking gaps on note finalization

Given a claim note is finalized and at least one gap with severity=Blocking remains unresolved When the note is saved as Final Then the system creates one workflow task per unresolved blocking gap within the claim’s active workflow And each task includes: gap ID, gap type, gap description, required fields, originating note ID, claim ID, and rule severity And duplicate tasks for the same active unresolved gap are not created on subsequent note edits; existing open tasks are referenced And task creation occurs within 2 seconds of finalization And an audit log entry is recorded with timestamp, actor, and created task IDs

Assign task owners and due dates based on carrier rules and claim attributes

Given routing rules configured by carrier and claim attributes (LOB, state, complexity, queue) When tasks are created for unresolved gaps Then the system assigns an owner per routing rule, or assigns to a default queue if no match And the due date is calculated from rule-defined SLA (e.g., 24 business hours) using the claim’s timezone and business calendar And tasks display owner, due date, and priority computed from severity and SLA proximity And if the assigned owner is marked unavailable (OOO), assign to the rule-defined backup; otherwise place in the target queue and notify queue members And changes to routing rules affect subsequent task creations without altering existing tasks unless a manual re-evaluation action is triggered

Start and track SLA timers for unresolved gap tasks

Given a task is created for an unresolved blocking gap When the task is created Then an SLA timer starts with duration defined by the applicable rule And the timer pauses when task status is set to Waiting on Insured/Third Party and resumes when moved back to In Progress And remaining time and predicted breach timestamp are displayed on the task and claim header And a warning event is emitted at 75% elapsed and an escalation to the supervisor is sent at breach And SLA outcomes (met/missed), total elapsed work time, and total pause time are recorded for reporting

Block stage advancement when required details are missing per severity rules

Given a claim is in a stage that requires all blocking gaps to be resolved And one or more unresolved gaps have severity=Blocking When a user attempts to advance the claim to the next stage Then the system blocks advancement and displays a list of blocking gaps with the required fields to resolve each And the Advance action remains disabled until all blocking gaps are resolved or downgraded by rule update And users with role=Admin may perform a one-time override with a mandatory reason; the override is captured in the audit log and flagged in SLA reports And gaps with severity=Non-Blocking do not prevent advancement

Attach most relevant outreach scripts to created tasks

Given a task is created for an unresolved gap When selecting scripts for the task Then the system attaches the top 1–3 scripts ranked by relevance to the gap type, claim type, and carrier preferences And scripts provide one-tap text/email/call prompts with placeholders (e.g., policy number, contact phone) auto-filled from claim data And sending a script logs the outreach on the task with timestamp, channel, recipient, and template ID And users may choose an alternative recommended script; the selected script ID is recorded

Notify stakeholders upon creation and updates of unresolved gap tasks

Given notification rules per carrier/team are configured When unresolved gap tasks are created, reassigned, nearing SLA breach (≥75% elapsed), breached, or resolved Then the system sends notifications to stakeholders via configured channels (in-app, email, Slack/MS Teams) And notifications are deduplicated per task-recipient within a rolling 10-minute window And each notification contains claim ID, gap summary, assignee, due date/SLA status, and a deep link to the task And delivery outcomes are logged; failures retry with exponential backoff up to 3 attempts

Expose APIs and webhooks for unresolved gap lists to downstream systems

Given authenticated partners with OAuth 2.0 client credentials When calling GET /claims/{id}/gaps?status=unresolved or GET /gaps?severity=blocking&carrierId={cid} Then the API returns paginated results within 500 ms p95 including gap IDs, severity, required fields, linked task IDs, assignee, due date, and SLA status And webhooks fire on gap task created, updated, resolved, and SLA breached events, delivering JSON payloads with event type, claim ID, gap IDs, and task metadata And webhooks use HMAC-signed headers, 10-second timeout, and retries with exponential backoff up to 5 times; failed deliveries are recorded to a dead-letter queue And APIs enforce tenant-scoped RBAC; unauthorized calls receive 403; rate limits of 100 rpm per client return 429 on exceed

Insights & Continuous Improvement Loop

"As an operations leader, I want visibility into prompt effectiveness and completeness metrics so that we can optimize rules and reduce rework over time."

Description

Provide analytics on first-pass completeness, prompt acceptance/ignore rates, time saved, unresolved gap frequency by carrier/LOB, and rule effectiveness. Enable A/B testing of prompt variants, collect structured feedback from users, and feed labeled examples into the model training pipeline to improve detection accuracy. Surface recommendations to retire low-value prompts and elevate high-impact ones.

Acceptance Criteria

First-Pass Completeness Analytics

Given a user with Analytics access selects a date range and filters (carrier, LOB, claim type, adjuster) When they open the Insights dashboard Then the First-Pass Completeness (FPC) rate is shown as numerator/denominator and percentage, computed as notes with zero unresolved gaps at submission divided by total notes in scope And FPC values match a validated offline calculation for the same scope within <=1.0% absolute difference And clicking the FPC widget reveals daily trend, a drill-down list of underlying notes, and a CSV export with claim_id, note_id, carrier, LOB, adjuster, submission_timestamp, unresolved_gap_count And metrics are updated at least every 2 hours; data freshness timestamp is displayed And p95 dashboard load time for the last 30 days scope is <=2.5 seconds

Prompt Acceptance/Ignore Metrics

Given event tracking is enabled When viewing Prompt Engagement for a selected prompt and scope Then acceptance rate, ignore rate, and dismiss rate are displayed with counts and percentages And metrics are segmented by carrier, LOB, claim type, and adjuster and respond to filters And events are deduplicated per prompt per note session and captured within 5 minutes of occurrence And end-to-end event capture success rate is >=99.5% over the selected period, with gaps flagged And users can export a CSV with prompt_id, variant_id, claim_id, note_id, user_id, event_type, timestamp

Time Saved Reporting

Given a baseline cohort configuration is set (e.g., no-prompt sessions over a prior 30-day window) When comparing assisted vs baseline for a selected scope Then median and p95 time saved per note (seconds) and per claim are displayed with sample sizes And a methodology tooltip describes the calculation method and baseline period used And results exclude the top/bottom 1% duration outliers And displayed medians match an offline recomputation within <=1.0% relative difference And a CSV export includes scope filters, sample sizes, median, p95, method, and baseline definition

Unresolved Gap Frequency by Carrier/LOB

Given a user selects a date range When viewing the Unresolved Gap Frequency report Then a table shows, for each carrier and LOB, the percentage and count of notes submitted with >=1 unresolved gap And rows can be sorted by percentage or count and filtered by claim type and adjuster And clicking a row shows the most common unresolved gap categories with counts And CSV export includes carrier, LOB, note_count, unresolved_gap_count, unresolved_gap_rate

Rule Effectiveness & Recommendations

Given rules/prompts generate suggestions during note-taking When viewing Rule Effectiveness Then each rule displays trigger rate, acceptance rate, lift in FPC versus unexposed cohort, and confidence intervals where N>=100 And rules with acceptance rate <10% over the last 30 days and FPC lift <0.5 percentage points are flagged with a Retire recommendation And rules with acceptance rate >=40% and FPC lift >=1.5 percentage points are flagged with an Elevate recommendation And clicking Apply Recommendation requires confirmation and records an audit entry with actor, timestamp, previous state, new state, and reason And a rollback option restores the prior configuration with a separate audit entry

A/B Testing of Prompt Variants

Given two or more prompt variants are configured When a test is started Then assignment is deterministic by claim_id with a 50/50 split ±2% and persists across a claim And the platform computes outcome metrics (acceptance rate, FPC lift, time saved) per variant and a two-sided significance test; tests with p<0.05 and N>=500 per arm are marked Significant And a winner can be promoted with one click, automatically pausing losing variants and recording an audit log And users can download a CSV of assignment and outcomes at the note level

Structured Feedback & Training Pipeline

Given a prompt is shown When the adjuster optionally rates usefulness (1–5) and selects a reason code or enters a comment Then the feedback is saved with prompt_id, variant_id, claim_id, note_id, user_id, timestamp And a Feedback report shows response rate, average rating, and top reason codes by scope, and is exportable to CSV And labeled examples from accepted/ignored prompts and feedback are queued daily into the model training pipeline with schema validation and PII scrubbing; failures are surfaced with error details And each training run records model version, dataset snapshot ID, number of examples, and validation metrics, visible in an audit trail

Hazard Sentinel

Detects safety risks mentioned in the recording—like downed lines, gas odors, standing water, biohazards, or structural instability—and time‑stamps each mention. Auto-generates a safety checklist and alerts the user with prioritized do/don’t guidance before they proceed. Improves field safety, documents duty‑of‑care, and provides auditable risk flags for QA and compliance.

Requirements

Hazard NLP Extraction

"As a field adjuster, I want the system to automatically detect safety hazards mentioned in my recording so that I’m warned about risks without manual review."

Description

Automatically transcribes user recordings and analyzes the transcript to detect safety hazards (e.g., downed power lines, gas odors, standing water, biohazards, structural instability). Uses a domain-tuned NLP model with synonym handling and confidence scoring to classify hazard type and severity. Outputs normalized hazard events into ClaimFlow’s data model for downstream modules (checklist generation, guidance, workflow gating). Processes securely with encryption, queues offline captures for later processing, and exposes detections via internal APIs for UI and workflow integration.

Acceptance Criteria

Online Recording: Hazard Detection and Timestamping

Given a user uploads a recording up to 10 minutes with network connectivity When transcription and NLP processing is initiated Then processing completes in ≤ 2x audio duration at p95 and ≤ 3x at p99 And each hazard mention is detected and time-stamped within ±1.0 second of its spoken position And detected hazards are limited to {downed power lines, gas odors, standing water, biohazards, structural instability} And each detected mention is associated to a normalized hazard event including {hazard_type, severity, confidence, mention_timestamp, mention_text} And no more than 1 false-positive hazard event is produced per 10 minutes of non-hazard speech in a clean-audio test set

Offline Capture: Queue and Retry Processing

Given the device is offline at the time of recording upload When the user saves the recording in ClaimFlow Then the recording is queued with status "Pending Sync" and an immutable checksum And the queued recording is encrypted at rest And sync retries with exponential backoff until connectivity is restored or a max delay of 24 hours is reached And upon connectivity restoration, the recording is transmitted and processed automatically without user action And at-most-once processing is guaranteed per checksum (no duplicate jobs created) And user-visible status transitions are available via internal API (Queued → Syncing → Processing → Complete/Failed)

Synonym Handling and Hazard Normalization

Given a transcript containing synonyms or colloquialisms for supported hazards (e.g., "downed wire", "sparking line", "smells like gas", "ankle-deep water", "sewage", "mold", "structure is shaky") When the NLP model processes the transcript Then each synonym maps to the correct canonical hazard_type in the ClaimFlow taxonomy {DOWNED_POWER_LINES, GAS_ODOR, STANDING_WATER, BIOHAZARD, STRUCTURAL_INSTABILITY} And on a curated synonym evaluation set (≥ 200 utterances per hazard), mapping accuracy ≥ 95% overall and ≥ 90% per hazard And non-hazard mentions yield no hazard event when confidence < T (default T=0.6)

Confidence Scoring and Severity Classification

Given labeled validation data for the five supported hazard types with severity annotations When the model is evaluated at threshold T = 0.6 Then hazard-type macro F1-score ≥ 0.85 and per-hazard F1 ≥ 0.80 And severity classification macro accuracy ≥ 0.80 And each hazard event includes confidence ∈ [0,1] with ≥ 2 decimal precision and severity ∈ {Low, Moderate, High, Critical} And events with confidence < 0.6 are flagged need_review = true and are excluded from default downstream gating and checklist generation

Internal API: Hazard Event Exposure

Given an authenticated internal client with service token scope "hazards:read" When it calls GET /internal/hazards?claim_id={id}&min_confidence=0.6 Then the API returns 200 with a paginated list of hazard events conforming to schema HazardEvent v1 (id, claim_id, hazard_type, severity, confidence, timestamps, source_id) And p95 response time ≤ 500 ms for result sets ≤ 100 records And filtering by time_range and hazard_type returns only matching events And unauthorized calls return 401; insufficient scope returns 403; invalid params return 422 with error details And OpenAPI documentation exists and matches actual responses

Security: Encryption and Data Handling

Given audio recordings, transcripts, and hazard events stored by the system Then data at rest is encrypted with AES-256 and keys are managed via KMS with rotation ≤ 90 days And data in transit uses TLS 1.2+ with modern cipher suites And raw audio and transcripts are not written to application logs; PII redaction is applied to error traces And access to storage and APIs is audited with immutable logs retained ≥ 1 year And CI security scans (SAST/Dependency) report zero High/Critical findings before release

Data Model Integration and Downstream Triggers

Given a claim with extracted hazards When hazards are persisted Then records are stored per ClaimFlow HazardEvent v1 schema and linked to claim_id And the safety checklist generator receives events with confidence ≥ 0.6 and produces checklist items aligned to hazard_type within 2 seconds of persistence (p95) And the do/don’t guidance module is invoked with the highest-severity hazard and returns prioritized guidance text And workflow gating blocks "Proceed to On-Site Task" when any Critical severity hazard exists until the checklist is acknowledged via API

Time-Stamped Hazard Mentions

"As a QA reviewer, I want each hazard mention time-stamped and linked to the audio so that I can quickly verify context and accuracy."

Description

Aligns each detected hazard to precise transcript timecodes and stores start/end timestamps with links to the corresponding audio snippet. Deduplicates repeat mentions, tracks first-seen and last-seen times, and highlights mentions in the transcript UI for quick verification. Persists timestamps with the claim for auditability and exposes them via API for QA and reporting.

Acceptance Criteria

Precise Timecodes for Hazard Mentions

Given an audio recording containing a spoken hazard phrase at known ground-truth times When Hazard Sentinel processes the file and aligns transcript tokens Then each detected hazard mention is stored with start_time and end_time aligned to the transcript with absolute error ≤250ms versus ground truth. And Then timestamps are ISO 8601 UTC with millisecond precision, and end_time >= start_time for every mention. And Then for overlapping mentions, all mentions are stored independently without dropped or merged timecodes. And Then the stored timecodes round-trip via persistence and API without loss of precision.

Audio Snippet Linking for Mentions

Given a hazard mention with stored start_time and end_time When a user clicks the mention highlight in the transcript UI Then the audio player starts within 250ms of start_time and stops within 250ms of end_time. And Then a deep link control opens the player to the same segment when shared. And Then the API field audio_snippet_url for the mention returns 200 OK with Content-Type audio/* and stream duration within ±500ms of (end_time − start_time). And Then authorization is enforced (401 for unauthenticated requests, 403 for unauthorized claims).

Deduplicate Repeat Mentions and Track First/Last Seen

Given multiple mentions of the same hazard_type occur within a single recording When processing completes Then the system emits one hazard aggregate per hazard_type with fields first_seen = earliest mention.start_time and last_seen = latest mention.end_time. And Then the aggregate includes mention_count equal to the number of mentions and a list of mention IDs with their individual start_time and end_time. And Then no duplicate aggregates of the same hazard_type exist for the same recording. And Then if only one mention exists, first_seen equals that mention's start_time and last_seen equals that mention's end_time.

Transcript UI Highlights Mentions

Given the transcript view is open for a processed recording When hazard mentions exist Then the exact token spans for each mention are highlighted with a distinct style per hazard_type. And Then clicking a highlight selects it, scrolls it into view, and syncs the audio player to the mention's start_time. And Then keyboard navigation (e.g., Alt+Up/Down) moves to previous/next mention. And Then highlight colors meet WCAG 2.1 AA contrast. And Then a "Hazard highlights" toggle shows or hides all highlights without reloading the page.

Persist Timestamps with Claim for Audit

Given a claim with extracted hazard mentions is saved When the claim is reopened after application/service restart Then all mentions, aggregates, timestamps, and audio links are present and unchanged. And Then an audit log entry records creation and any subsequent update with actor, UTC timestamp, and change summary. And Then records are retained according to retention_policy_days (default 2555 days) and purged only per policy. And Then database constraints enforce non-null start_time, end_time, hazard_type, and referential integrity to claim_id and recording_id.

API Returns Hazard Mentions with Timestamps

Given an authenticated client with scope claim.read and a valid claim_id When it calls GET /api/v1/claims/{claim_id}/hazards Then the response is 200 OK and returns a JSON array with fields: hazard_type, first_seen, last_seen, mention_count, and mentions[n]{id, start_time, end_time, transcript_span, audio_snippet_url}. And Then all timestamps are ISO 8601 UTC with millisecond precision. And Then query parameters type, start_after, end_before, and limit filter the results; invalid parameters return 400. And Then performance: for up to 200 mentions, median latency ≤800ms and p95 ≤1200ms in staging. And Then access control: 401 for unauthenticated, 403 for unauthorized, 404 for unknown claim_id.

Auto Safety Checklist Generation

"As a field adjuster, I want an auto-generated safety checklist tailored to the hazards so that I can take the right precautions before proceeding."

Description

Maps detected hazards to a configurable library of safety checklists and generates a tailored checklist per claim. Merges items across multiple hazards, orders tasks by severity and dependencies, and pre-fills dynamic steps (e.g., notify utility, establish perimeter) using available claim context. Publishes the checklist as a sub-task set in ClaimFlow, supports offline execution with later sync, and updates in real time if new hazards are detected.

Acceptance Criteria

Checklist Generation for Multiple Hazards in One Claim

Given a claim with detected hazards "downed power lines", "standing water", and "gas odor" And a configured library mapping each hazard to checklist items with severity scores and dependency rules When the system generates the safety checklist Then a single checklist is produced that contains the union of all mapped items And duplicate items across hazards are merged into one item with aggregated source-hazard tags And the checklist contains no items that are not mapped from the detected hazards or global safety prerequisites And checklist generation completes within 3 seconds for claims with <= 10 detected hazards

Severity- and Dependency-Ordered Tasks

Given the library defines numeric severity levels (e.g., Critical=4, High=3, Medium=2, Low=1) and explicit dependencies between items When the checklist is generated Then every item appears after all of its dependencies And items are primarily sorted by descending severity and secondarily by dependency topological order And for items with equal severity and no dependency relation, the order matches the library default order And the generated order is deterministic for the same inputs

Dynamic Pre-fill of Checklist Items Using Claim Context

Given claim context provides fields (e.g., policy carrier, utility provider, insured contact, loss location, occupancy status) And a checklist item includes dynamic placeholders (e.g., {utility_provider.name}, {utility_provider.phone}) When the checklist is generated Then placeholders are resolved using available claim context and populated into item fields And unresolved placeholders are surfaced as "Needs input" without blocking checklist publication And all prefilled values record their source and timestamp in the audit log And no item contains an unresolved placeholder token string at runtime

Publish Checklist as Sub-Tasks in ClaimFlow

Given a generated checklist for a claim When it is published to ClaimFlow Then a sub-task set named "Safety Checklist" is created under the claim And each checklist item is created as an individual sub-task with preserved order, severity, hazard tags, and dependencies And dependent sub-tasks cannot be marked complete until all prerequisite sub-tasks are complete And the publishing operation completes successfully and is visible to the assignee within 5 seconds

Offline Execution and Deferred Sync

Given the device is offline and the "Safety Checklist" sub-tasks are cached locally When the user records progress (complete/incomplete), notes, and photos on checklist items Then all actions are stored locally with timestamps and item IDs And upon connectivity restoration, all offline changes sync to the server within 60 seconds And attachments are uploaded and linked to their respective sub-tasks without loss And conflicts are resolved per field using last-write-wins with an audit entry and user notification when a local change is overwritten

Real-time Update on New Hazard Detection

Given the user has the checklist open and a new hazard is detected for the same claim When the system processes the new hazard Then new checklist items are added and existing items updated within 3 seconds And the updated checklist maintains dependency and severity ordering And already completed items remain completed; if new dependencies are introduced, they are added as pending with a change notice And the user is notified with a banner summarizing what changed and a link to the change log

Prioritized Do/Don’t Guidance

"As a field adjuster, I want prioritized do/don’t guidance so that I know the most critical actions to take immediately."

Description

Generates concise, prioritized do/don’t guidance for each detected hazard, emphasizing immediate life-safety actions. Presents guidance in an at-a-glance card with severity cues, icons, and links to SOP/OSHA references. Supports localization and text-to-speech for hands-free operation. Surfaces as a pre-proceed banner in the ClaimFlow UI and remains accessible throughout the claim workflow.

Acceptance Criteria

Immediate Life‑Safety Prioritization across Hazards

Given at least one hazard is detected, When guidance is generated, Then the first bullet on each hazard card addresses immediate life‑safety mitigation for that hazard type. Given multiple hazards are detected, When the list is rendered, Then hazards are ordered by severity descending (Critical, High, Medium, Low); ties are resolved by most recent mention timestamp (newest first). Given a Critical hazard is present, When the banner is displayed, Then that hazard appears at the top of the list and is prefixed with a critical attention cue.

Concise Do/Don’t Guidance Format per Hazard Card

Given a hazard card is generated, When its bullets are rendered, Then the card contains 2–4 total bullets with at least one "Do" and at least one "Don't". Given bullets are shown, When measured, Then no bullet exceeds 120 characters. Given text is analyzed, When readability is computed (Flesch–Kincaid), Then grade level is <= 8.0. Given localization is applied, When switching languages, Then bullet counts and order remain unchanged.

Guidance Card UI: Severity Cues, Icons, Timestamp

Given a hazard card is displayed, Then it includes: hazard name, severity label, color‑coded severity cue, hazard‑type icon, and latest mention timestamp (hh:mm:ss local). Given WCAG evaluation, Then the color contrast between text and background is >= 4.5:1 for normal text and >= 3:1 for large text/icons. Given the hazard type is unknown, Then a generic safety icon is used and no UI break occurs. Given viewport width from 320px to 1440px, Then the card layout remains readable without horizontal scroll.

SOP/OSHA Reference Linking and Availability

Given a hazard card is displayed, When references are available, Then the card shows at least one internal SOP link and one external OSHA reference link relevant to the hazard. Given a reference link is tapped, Then it opens in a new tab/window and returns HTTP 200 within 2 seconds on a 4G connection. Given the device is offline, When a reference is tapped, Then an offline notice is shown and a cached summary (>= 200 characters) is displayed if available. Given analytics is enabled, When a reference is tapped, Then a click event is logged with hashed user ID, hazard ID, and timestamp.

Localization of Guidance and UI (EN/ES) with Fallback

Given the app locale is EN‑US or ES‑US, When guidance is generated, Then all visible strings on the banner and cards appear in the selected locale. Given locale resources are missing for a string, Then the English string is shown and an error is logged. Given language is switched by the user, When on the banner or card, Then text updates within 500 ms without full‑screen reload. Given Spanish is selected, Then units and examples use locale‑appropriate terms.

Hands‑Free Text‑to‑Speech Playback

Given a hazard card or the pre‑proceed banner is visible, Then a TTS control (play/pause/stop) is present and focusable via keyboard. Given the user taps Play, Then guidance for the visible card is spoken in the current locale voice, starting within 1 second. Given the screen is locked, Then playback continues until paused/stopped by the user. Given a new card is opened during playback, Then the previous playback stops and the new card's guidance begins within 1 second. Given no TTS engine is available for the locale, Then the control is disabled and a tooltip indicates voice unavailability.

Pre‑Proceed Banner Surfacing and Persistent Access

Given at least one hazard is detected for a claim, When the user attempts to start the claim workflow, Then a pre‑proceed safety banner is shown before task screens. Given the banner is shown, Then the Proceed button is disabled until the user taps Acknowledge. Given the user acknowledged the banner, When navigating across workflow steps, Then a persistent Safety icon in the header re‑opens the full guidance list within 1 click/tap. Given a new Critical hazard is detected after acknowledgement, Then the banner re‑surfaces on the next navigation event and the header icon shows a red badge with the critical count. Given the banner is acknowledged, Then an audit record is stored with claim ID, user ID, hazard IDs, timestamp, and locale.

Safety Gate & Acknowledgment

"As a claims manager, I want a safety acknowledgment gate so that field staff cannot proceed without confirming they’ve reviewed critical guidance."

Description

Enforces a pre-proceed safety gate when high-severity hazards are detected. Requires the user to review guidance and acknowledge via checkbox or e-signature; supports role-based overrides with mandatory reason capture. Records timestamp, user ID, and optional GPS location. Integrates with ClaimFlow workflow to block or warn based on configurable thresholds and writes a non-editable acknowledgment event to the audit log.

Acceptance Criteria

High-Severity Hazard Blocks Progress Until Acknowledgment

Given Hazard Sentinel flags one or more hazards with riskScore >= blockThreshold for the active claim When the user attempts to proceed to the next workflow step or submit intake Then a Safety Gate modal appears within 500 ms with prioritized do/don’t guidance for the flagged hazards And all proceed/submit/navigation actions remain disabled until acknowledgment is completed per orgConfig.ackMethod And closing or dismissing the modal without acknowledgment keeps the user on the current step with no progression

Medium-Severity Hazard Warning with Conditional Acknowledgment

Given max hazard riskScore >= warnThreshold and < blockThreshold When the user attempts to proceed Then a non-blocking safety warning with prioritized guidance is shown And if orgConfig.requireAckOnWarn = true, the user must click “Acknowledge and Continue” before proceeding; otherwise, the user can proceed without acknowledgment And a warningShown audit event is generated with hazardIds and riskScore

Role-Based Override Requires Mandatory Reason Capture

Given the Safety Gate is blocking due to riskScore >= blockThreshold And the current user's role is included in orgConfig.overrideRoles When the user selects “Override and Proceed” Then the system requires a reason category selection and a free-text reason of at least 10 characters And the proceed action remains disabled until both fields are provided And users without override permission do not see the override option

Acknowledgment Methods and Guidance Review Enforcement

Rule (checkbox): Given orgConfig.ackMethod = "checkbox" When the user checks the acknowledgment box labeled with “I have reviewed the safety guidance” Then the Proceed action becomes enabled and userId + timestampUTC are captured Rule (eSignature): Given orgConfig.ackMethod = "eSignature" When the user provides a valid e-signature and confirms intent Then the Proceed action becomes enabled and userId + name + signature hash/image + timestampUTC are captured Rule (review enforcement): Given the Safety Gate guidance content When not fully viewed Then acknowledgment controls remain disabled; When the guidance panel is expanded and scrolled to end Then acknowledgment controls are enabled

Non-Editable Audit Log Event Persistence

Given an acknowledgment, override, or warningShown action is completed When the system records the event Then exactly one non-editable audit entry is written containing: claimId, userId, userRole, actionType (acknowledge|override|warningShown), hazardIds, maxRiskScore, thresholdType (block|warn), guidanceVersion, timestampUTC, workflowStepId, deviceId (if available), ipAddress (if available), gps (lat,long,accuracy,permissionStatus), overrideReason (if any), ackMethod, signatureHash (if any) And Proceed/Continue is finalized only after the audit write succeeds And if the audit write fails, an error is shown and proceeding is blocked with retry available; no partial state advances And audit entries are immutable and retrievable in the claim's audit log with read-only permissions

Optional GPS Capture and Metadata Recording

Given location permission is granted and a GPS fix is available When the user completes acknowledgment or override Then latitude, longitude, and horizontal accuracy are captured within 5 seconds and stored with the audit entry And if permission is denied or a fix is not available within 5 seconds Then gps.status = "unavailable" is recorded and progression is not blocked And captured GPS data is non-editable post-write

Auditable Risk Flags & QA Dashboard

"As a compliance officer, I want auditable risk flags with immutable records so that we can demonstrate duty-of-care during audits."

Description

Persists risk flags with metadata (hazard type, severity, confidence, timestamps, acknowledgments) in an immutable event log for duty-of-care evidence. Provides a QA dashboard to filter, search, and sample claims by hazard type/severity, and to play linked audio snippets. Supports export (CSV/JSON) and API access for compliance reporting and SOC/ISO audits, with role-based access controls and retention policies.

Acceptance Criteria

Persist and Verify Immutable Risk Flag Event Log

Given a claim has detected hazards When a new risk flag is generated Then the system writes a new append-only event record containing: claimId, hazardType, severity, confidence (0.00–1.00), sourceType, startTimestamp, endTimestamp (nullable), createdAt (UTC), acknowledged (bool), acknowledgedBy (nullable), acknowledgedAt (nullable), eventId (UUID), previousEventId (nullable), integrityChecksum And the record is readable immediately via UI, export, and API When a user acknowledges a risk flag Then a new event record is appended referencing the prior eventId and updating acknowledgment fields while the original record remains unchanged When an attempt is made to update or delete an existing event record directly Then the operation is rejected or results in an appended new version; the original record persists and remains retrievable When an integrity verification job runs Then 100% of event records pass checksum validation for untampered data and any discrepancy is logged with severity Critical within 60 seconds

QA Reviewer Filters, Searches, and Samples Claims by Hazard

Given the QA dashboard is open When the reviewer filters by hazardType = "Gas Odor", severity >= "High", date range = last 30 days, and confidence >= 0.80 Then only claims with matching risk flags are shown and the total count equals the filtered set size When a free-text search for "downed line" is applied Then only claims with matching hazard tags or transcripts appear in results When a random 10% sample (with seed) of the filtered set is requested Then the returned subset is reproducible for the same seed and filters, and the UI shows sample size and parameters When the filtered set contains up to 100,000 flags Then the first page loads within 2 seconds and supports pagination and sorting by severity, timestamp, and confidence

Play Linked Audio Snippets for Hazard Mentions

Given a risk flag sourced from audio with startTimestamp and endTimestamp When the reviewer clicks Play on the QA dashboard Then the corresponding audio segment plays aligned to the timestamps with ±200ms tolerance And the UI displays the segment duration and playback position When the segment duration is under 2 seconds Then a padded snippet of at least 2 seconds is played centered on the mention where available When the original audio file is unavailable Then the UI displays a non-blocking error, logs the missing asset, and no unrelated audio is played When multiple mentions exist in the same claim Then each Play control plays the correct segment for its own timestamps

Export Filtered Risk Flags to CSV and JSON

Given the QA dashboard has active filters When the reviewer exports as CSV or JSON Then the exported data contains only records in the filtered set and includes: claimId, hazardType, severity, confidence, sourceType, startTimestamp, endTimestamp, createdAt, acknowledged, acknowledgedBy, acknowledgedAt, eventId When exporting CSV Then the file is UTF-8, comma-delimited with RFC 4180 quoting, includes a header row, and all timestamps are UTC ISO 8601 When exporting JSON Then the output validates against the published schema and preserves data types (numbers, booleans, strings) When the export exceeds 50,000 records Then the system streams or chunks the download without timeout and the total rows equal the dashboard count

API Access with Role-Based Access Controls and Audit Logging

Given the compliance API is enabled When a user with role QA_Reviewer requests GET /risk-flags with filters Then the API returns 200 with only permitted records for that role When a user without permission requests the same endpoint Then the API returns 403 without revealing record existence When a Claims_Adjuster queries via API Then only flags for claims assigned to that user are returned and bulk export endpoints respond 403 When any API request completes Then an audit log entry is recorded with userId, role, IP, endpoint, parameter hash, UTC timestamp, response code, and record count When pagination is used Then pageSize is capped at 1000, nextPage tokens are provided, and rate limits of 100 requests/min per user are enforced with 429 on exceed

Retention Policies and Legal Hold for Risk Flag Events

Given a retention policy is configured (e.g., 7 years for hazardType = "Structural Instability") When events exceed the retention age and are not under legal hold Then they are purged by an automated job within 24 hours of eligibility When a legal hold is placed on a claim or hazard type Then covered events are excluded from purge until the hold is lifted and the hold status is visible in the dashboard When purge completes Then a non-PII purge receipt is written to the audit log with counts, date range, and policyId, and purged eventIds become inaccessible via UI, export, and API (410 Gone on direct lookup) When retention settings are exported for audit Then a JSON policy file is produced listing policy name, scope, duration, holds, and lastModified metadata

Configurable Hazard Taxonomy & Rules

"As an admin, I want to configure the hazard taxonomy, thresholds, and checklists so that the guidance aligns with our SOPs and jurisdictions."

Description

Offers an admin UI and API to manage hazard taxonomy, synonyms, severity scales, and mappings to checklists and guidance. Allows per-jurisdiction rules, client-specific configurations, and threshold tuning for guidance priority and workflow gating. Supports versioning, staged publishing, rollback, and change history to ensure consistent, auditable configuration management across environments.

Acceptance Criteria

Admin UI: Manage Hazard Taxonomy, Synonyms, Severity, and Mappings

Given I am an Admin with Config:Hazards permission, When I create a hazard with a unique code, name, severity scale bounds (1–5), and at least one synonym, Then the system saves it and displays it in the taxonomy list within 2 seconds. Given a hazard exists, When I add, edit, or delete synonyms and map checklist items and guidance templates, Then changes persist and the preview shows the associated checklist and guidance exactly as mapped. Given invalid input (missing name, duplicate code, duplicate synonym within the same hazard, severity outside 1–5), When I attempt to save, Then the save is blocked and field-level errors are shown; no partial updates occur. Given audit requirements, When I save any change, Then a change record is created capturing user ID, timestamp, change summary, and before/after values for affected fields.

Configuration API: CRUD with RBAC and Validation

Given a valid admin API token, When I POST /hazards with a valid payload, Then the API returns 201 with the created resource including id, version, and timestamps. Given a read-only API token, When I attempt POST, PUT, PATCH, or DELETE on configuration resources, Then the API returns 403 with an error code of "forbidden". Given invalid payloads, When I call POST or PUT, Then the API returns 422 with machine-readable field errors including the field path, rule violated, and message. Given optimistic concurrency control, When I update a resource with a stale version value, Then the API returns 409 Conflict; when I update with the current version, Then the update succeeds with 200 and an incremented version.

Per-Jurisdiction Rules: Targeting and Precedence Resolution

Given a base hazard rule exists, When I create a jurisdiction-scoped override for "CA", Then sessions tagged with jurisdiction "CA" use the override and sessions without "CA" use the base rule. Given both client-scoped and jurisdiction-scoped rules exist, When a session is for Client X in "CA", Then the system applies precedence: client override > jurisdiction override > base rule. Given a disabled jurisdiction rule, When I publish configurations, Then the disabled rule is not applied and the next-eligible rule by precedence is used.

Client-Specific Configurations: Isolation and Inheritance

Given two clients A and B, When I create a client-scoped synonym for A, Then it is not returned in GET queries scoped to B and cannot affect B’s detections. Given Client A lacks a custom severity scale, When a hazard is resolved for Client A, Then the system uses the base severity scale for any calculations and prioritization. Given multi-tenant security, When a user scoped to Client B queries for Client A’s configurations, Then the API returns 403 or 404 per RBAC policy and no data is leaked.

Threshold Tuning: Guidance Priority and Workflow Gating

Given guidance priority threshold = 0.85 and gating severity threshold = 4, When a detection outputs confidence 0.83 and severity 5, Then no priority alert is shown but workflow gating still blocks until the mapped checklist is completed. Given thresholds are changed and published at time T, When a new session starts after T, Then the new thresholds are applied; sessions started before T continue using the previously published thresholds. Given UI gating is active, When a user attempts to proceed without completing required checklist items, Then the Proceed action is disabled and an explanatory message is displayed and logged.

Versioning: Drafts, Staged Publishing, Rollback, and Change History

Given a draft v1.3, When I stage it to QA and publish, Then the QA environment reflects v1.3 while Production remains on its current version. Given a scheduled publish for v1.3 at 14:00 UTC, When time reaches 14:00 UTC, Then the target environment activates v1.3 and an audit record with actor, time, and comment is created. Given an issue in Production, When I initiate rollback to v1.2, Then Production reflects v1.2 within 1 minute and history records the rollback event with reason and actor. Given two versions v1.2 and v1.3, When I request a diff, Then the system shows added, removed, and changed fields for taxonomy, synonyms, mappings, and thresholds.

Runtime Resolution: Synonym-Based Hazard Mapping to Checklist and Guidance

Given a published taxonomy with hazard "Gas Odor" having synonym "smell of gas" mapped to guidance "Evacuate immediately" (priority High), When a recording transcript contains "smell of gas" with detection confidence 0.92, Then the system classifies the hazard as Gas Odor, time-stamps the mention, generates the mapped checklist, and displays High-priority guidance. Given a synonym is removed in a draft but not yet published, When a session runs in Production, Then detections still match the published synonym set; after publish, the removed synonym no longer triggers the hazard. Given multiple hazards are detected, When prioritization rules are applied, Then guidance is ordered according to configured severity and priority weights, and the ordering matches the configuration.

Timecode Anchors

Pins every extracted fact to its precise moment in the audio with a tappable waveform. Users can replay the exact snippet for quick verification, dispute resolution, or audit review. Delivers transparent traceability that speeds QA, reduces clarification calls, and strengthens chain‑of‑custody alongside Proof Seal.

Requirements

Word-Level Time Alignment Engine

"As a claims QA reviewer, I want every word of the transcript aligned to the audio so that any extracted fact can be traced back to an exact moment for fast, defensible verification."

Description

Implement a robust forced-alignment service that maps every transcript token to precise start/end timestamps in the source audio. The engine must accept ASR output, handle variable audio quality, and return word- and phrase-level timings with confidence scores. It should support retries, partial alignment when segments are missing, and diarization tags for speakers when available. Performance targets: align a 10‑minute recording in under 90 seconds at p95; accuracy targets: median word offset error ≤ 120 ms. Provide gRPC/HTTP APIs, idempotent job IDs, and health metrics. Store alignment artifacts in the claim’s data model for downstream anchoring and playback.

Acceptance Criteria

API Response and Artifact Persistence

Given a valid claimId, ASR transcript JSON (v1) with tokenized words and optional phrase segments, and the source audio, When the client submits an alignment request via HTTP POST /alignments or gRPC Align with a unique jobId, Then the service acknowledges with 202 Accepted (HTTP) or OK (gRPC) containing jobId and status ∈ {queued, processing}. Given a submitted jobId, When the client polls GET /alignments/{jobId} (or calls GetAlignment), Then on completion the response status is completed and includes for every input token: tokenText, startMs, endMs, confidence ∈ [0.0,1.0], and for each phrase: startMs, endMs, tokenIndexRange, with all timestamps monotonic and startMs < endMs. Given a completed alignment, When inspecting the claim’s data store by claimId, Then alignment artifacts (word timings, phrase timings, confidence scores, jobId, schema version) are persisted and retrievable by downstream services within 1 second of completion. Rule: Missing or filtered punctuation tokens are explicitly marked alignmentStatus = skipped; aligned tokens are alignmentStatus = aligned.

p95 Performance for 10-Minute Audio

Given a test set of 30 distinct 10-minute mono 16 kHz recordings of mixed quality and their ASR outputs, When processed sequentially on the reference production instance type, Then the measured wall-clock alignment time per job from accepted to completed has p95 ≤ 90 seconds and p50 ≤ 60 seconds. Rule: Any job exceeding 120 seconds is flagged latencyOutlier = true in metrics and logs.

Median Word Offset Accuracy ≤ 120 ms

Given a benchmark corpus with human-annotated word-level ground truth, When the alignment engine processes the corpus, Then the median absolute word onset error across all aligned words is ≤ 120 ms. Rule: Words with no ground-truth match are excluded from median but counted and reported as unalignedCount; unalignedCount/totalWords ≤ 10%.

Partial Alignment and Missing Segment Handling

Given audio containing dropouts or sections missing from the ASR transcript, When the alignment runs, Then the job completes with overall status = partial and returns aligned words where possible while marking others alignmentStatus ∈ {missing_audio, missing_transcript, low_confidence}. Rule: No aligned token may overlap a detected gap; contiguous gaps are represented as gapRegions with startMs/endMs and reason. Rule: The service must not fail the entire job due to partials; HTTP status remains 200 on GET for completed partial results.

Idempotent Job IDs and Safe Retries

Given an alignment request submitted with jobId = X and payload P, When the same request with jobId = X and identical payload P is retried after a network error, Then no duplicate processing occurs and the same alignment result is returned with identical artifact hashes. Given an alignment request submitted with jobId = X and a different payload P', When submitted, Then the service rejects it with 409 Conflict and does not mutate stored artifacts. Rule: All submission endpoints accept an Idempotency-Key or jobId header/field and are safe for at-least-once retry semantics.

Speaker Diarization Propagation

Given ASR output that includes speaker diarization segments with speakerId and time ranges, When alignment completes, Then each aligned word includes speakerId matching the diarization active at word startMs, and speaker turn boundaries do not split within a single word. Rule: If diarization is absent, words have speakerId = null and no diarization-derived fields are emitted.

Health Endpoints and Metrics Exposure

Given the service is deployed, When calling GET /health/ready and /health/live, Then readiness returns 200 only when dependency checks (storage, queue, model assets) pass, and liveness returns 200 if the process is responsive. Given a metrics scraper, When scraping /metrics (Prometheus format), Then counters and histograms exist for: requests_total by endpoint and status, jobs_total by status, alignment_latency_seconds with p50/p95/p99, queue_depth, and error_total by type. Rule: Metric names and labels follow the documented contract and are present continuously under nominal load.

Fact-to-Timecode Linking

"As an adjuster, I want each extracted loss detail to include its exact audio timecodes so that I can audit where it came from without re-listening to the entire call."

Description

Extend the NLP extraction pipeline to attach one or more timecode anchors to each extracted fact, referencing start/end offsets, confidence, and speaker. Support aggregation when a fact is stated multiple times and maintain provenance across transcript edits. Persist anchors in the claim graph with versioning so that updates do not break historical references. Expose anchors in the Fact API and ensure they are queryable and filterable (e.g., by confidence, speaker, section). Validate links during ingestion and flag low-confidence anchors for manual review.

Acceptance Criteria

Anchor Creation on Fact Extraction

Given an ingested audio file with aligned transcript and speaker diarization When the NLP pipeline extracts a fact from the transcript Then the fact must have at least one timecode anchor with fields: start_ms, end_ms, confidence, speaker_id, transcript_version_id And start_ms >= 0 And end_ms > start_ms And end_ms <= audio_duration_ms And confidence is between 0.0 and 1.0 inclusive And speaker_id is one of the recognized speakers for the call or "unknown" And GET /facts/{fact_id}/anchors returns the anchors sorted by start_ms ascending

Multiple Mentions Aggregation and Anchor Union

Given a fact content that appears multiple times in the audio When aggregation is performed during extraction Then the fact's anchors list contains all unique occurrences sorted by start_ms And anchors within 100ms of each other are deduplicated into a single occurrence And the fact exposes first_seen_ms = min(anchors.start_ms) and last_seen_ms = max(anchors.end_ms) And aggregated_confidence = max(anchors.confidence)

Provenance Preservation Across Transcript Edits

Given a fact with anchors linked to transcript_version_id = V1 When the transcript is edited producing version V2 (insertions, deletions, resegmentation, or speaker changes) Then existing V1 anchors remain retrievable by ID and by transcript_version_id = V1 And new anchors for V2 are created without mutating V1 records And GET /facts/{fact_id}/anchors?transcript_version_id=V1 returns only V1 anchors and GET /facts/{fact_id}/anchors?transcript_version_id=V2 returns only V2 anchors And a speaker change in V2 does not alter the speaker_id on V1 anchors

Anchor Persistence and Versioned Graph References

Given anchors are persisted in the claim graph When a fact is updated or reprocessed Then a new anchor version is created and linked; prior versions remain intact and queryable And dereferencing any historical anchor_id returns the original start_ms, end_ms, confidence, speaker_id, and transcript_version_id And graph integrity validation reports zero dangling anchor references from fact nodes

Fact API Querying and Filtering of Anchors

Given the Fact API supports anchor filtering When a client queries with confidence_min=0.7, speaker_id="claimant", section="accident_details", and time_range=[t0,t1] Then only anchors meeting all filters are returned And results are paginated with default limit <= 100 and max limit <= 1000 And results can be sorted by start_ms ascending or descending via a sort parameter And the endpoint responds within 500 ms for up to 1000 anchors in a preloaded test dataset

Ingestion Validation and Low-Confidence Flagging

Given the ingestion pipeline processes anchors When an anchor has confidence < 0.60 (or below the configured threshold) Then the anchor is marked review_required=true and a ManualReview task is created with a reference to fact_id and anchor_id And anchors with invalid bounds (start_ms < 0, end_ms <= start_ms, or end_ms > audio_duration_ms) are rejected, logged with severity=error, and not persisted And the ingestion summary includes counts for anchors_created, anchors_rejected, and anchors_flagged_for_review

Tappable Waveform with Anchor Markers

"As a claims manager, I want a waveform with tappable markers for facts so that I can quickly jump to the exact snippet to verify details."

Description

Render an interactive waveform synced to the recording that displays visual markers for each anchored fact. Users can hover to see a tooltip (fact summary, timestamps, confidence) and tap to jump to playback. Support zooming, scrubbing, keyboard shortcuts, and accessibility (WCAG AA: focus states, screen-reader labels). Handle long recordings with virtualized rendering and clustering of dense markers. Maintain synchronization between waveform, transcript highlight, and fact panel selections.

Acceptance Criteria

Hover Tooltip on Fact Anchor Marker

Given the waveform is rendered and facts are loaded When the user hovers a fact anchor marker with a pointing device Then a tooltip appears within 200 ms anchored to the marker And the tooltip displays: fact summary (<=120 chars), start and end timestamps in mm:ss, and confidence as a percentage with 1 decimal And the tooltip remains visible while the pointer is over the marker or tooltip and hides within 300 ms after exit or immediately on Esc And the tooltip stays within the viewport (no clipping) by repositioning as needed And the tooltip content matches the corresponding fact record ID in the fact panel

Tap-to-Playback from Anchor Marker

Given an anchor marker is visible When the user clicks/taps the marker or presses Enter/Space while the marker is focused Then the audio currentTime jumps to the anchor start within ±50 ms And playback starts (or continues) from that time And the waveform playhead, transcript highlight, and fact panel selection update to that fact within 100 ms And the fact panel scrolls the selected fact into view if off-screen And repeated activation of the same marker does not queue duplicate playbacks

Zooming, Scrubbing, and Keyboard Shortcuts

Given the waveform is focused When the user performs Ctrl/Cmd + Mouse Wheel or a trackpad pinch gesture Then the zoom level changes smoothly centered on the pointer with bounds of 1x–64x When the user drags horizontally on the timeline (scrub) Then the audio currentTime updates continuously without audible artifacts and the playhead tracks the pointer And marker positions and densities remain accurate at each zoom And keyboard shortcuts work: +/- to zoom in/out, Left/Right to seek 1s, Shift+Left/Right to seek 5s, Home/End to jump to start/end And all interactions respond within 100 ms median latency

Marker Clustering and Expansion on Dense Sections

Given multiple markers would overlap within 8 px at the current zoom level Then they render as a single cluster badge showing the count of markers And the cluster tooltip shows the time span (start–end) covered by the clustered markers When the user zooms in or activates the cluster (click/Enter) Then the cluster expands so that individual markers are visible without overlap at the new zoom level And cluster counts are accurate and visually update within 150 ms And at no zoom level do overlapping markers render closer than 4 px to each other

Cross-Component Synchronization (Waveform, Transcript, Fact Panel)

Given a fact is selected in the fact panel When selection changes Then the corresponding marker is highlighted and centered in the waveform within 150 ms, and the matching transcript span is highlighted Given audio playback or scrubbing enters a time range covered by a fact When currentTime is within a fact’s [start, end] Then the corresponding fact and transcript segment become active; when currentTime leaves the range, the highlight advances to the next fact or clears And if multiple facts share the same timestamp, the highest-confidence fact is selected; on ties, select the earliest created

Accessibility Compliance (WCAG AA)

Given a keyboard-only user When navigating the waveform and markers Then all interactive elements (play/pause, zoom controls, markers, clusters) are reachable via Tab/Shift+Tab and actionable via Enter/Space with a visible focus indicator meeting 3:1 contrast And focus order follows visual order without traps; Esc dismisses tooltips/popovers And each marker/cluster has an accessible name combining fact summary, start–end time, and confidence via ARIA label And tooltip content is available on focus (not hover-only) And text and essential non-text contrast meet WCAG 2.1 AA (1.4.3, 1.4.11); interaction targets are at least 24x24 dp (2.5.5) And tested to operate with NVDA, JAWS, and VoiceOver without loss of information

Performance on Long Recordings with Virtualized Rendering

Given a 3-hour recording at 48 kHz with 5,000 fact markers When the waveform first renders Then initial render completes within 1500 ms on a standard laptop (4-core CPU, integrated GPU) And panning/zooming/scrubbing maintain >=55 FPS with median input-to-frame latency <=100 ms And DOM nodes for markers are virtualized to <=300 simultaneously And memory usage attributable to the waveform stays <=250 MB And marker clustering ensures on-screen marker elements do not exceed 300 at any zoom level

Contextual Snippet Replay Controls

"As a dispute resolution specialist, I want to replay the exact quoted snippet with a bit of surrounding context so that I can confirm intent and resolve disagreements faster."

Description

Provide precise playback of anchored snippets with configurable pre/post context (e.g., ±5–10 seconds) and automatic transcript highlighting during playback. Include variable speed, skip-back/forward, and loop region. Ensure low-latency start (≤200 ms p95 after seek) and graceful handling when anchors overlap or fall near file boundaries. Surface a quick-copy link that deep-links to the same snippet inside ClaimFlow for internal sharing.

Acceptance Criteria

Low‑Latency Start After Seek

Given an audio recording with timecode anchors is loaded on a supported browser When the user taps an anchor, scrubs the waveform to a snippet, or opens a deep link to a snippet Then playback begins within 200 ms at the 95th percentile measured over 100 seeks/taps in the QA test matrix And the first audible audio frame corresponds to the requested start time ±50 ms And the playhead and transcript highlight appear within 100 ms of audio start

Configurable Pre/Post Context Playback

Given user preferences allow configuring pre/post context between 0 and 10 seconds in 0.5 s increments (default ±5 s) When the user initiates snippet playback from an anchor Then playback starts at max(anchor.start − pre, 0) and ends at min(anchor.end + post, audio.duration) And the effective pre/post values are displayed in the UI and persisted per user And context settings apply consistently across all snippets in the session

Automatic Transcript Highlighting In Sync

Given the transcript is word‑timed to the audio When snippet playback starts, pauses, seeks, speed changes, or loops Then the currently spoken words are highlighted with average drift ≤100 ms and worst‑case drift ≤200 ms relative to audio And on seek, the highlight updates to the new position within 100 ms And on pause, the highlight remains on the last played word

Variable Speed, Skip, and Loop Controls

Given snippet playback controls are visible When the user selects a playback speed of 0.5×, 1.0×, 1.5×, or 2.0× Then audio plays at the chosen speed and transcript highlighting respects the effective timing within the defined drift thresholds When the user taps Skip Back or Skip Forward Then the playhead moves by 5 seconds backward or forward respectively, clamped to [0, audio.duration] When Loop is enabled for the current snippet region Then playback repeats seamlessly between region start and end with loop gap ≤50 ms until Loop is disabled or playback stops

Overlapping Anchors Disambiguation

Given two or more anchors overlap on the waveform When the user taps within the overlapping region Then a popover lists overlapping facts by label and timestamps for selection And tapping directly on a specific anchor chip selects that anchor without a popover And the selected anchor determines playback region, transcript highlighting, and the deep‑link payload

Graceful Handling Near File Boundaries

Given an anchor lies within the configured pre/post context of the file start or end When snippet playback is initiated Then the actual start time clamps to 0 and/or end time clamps to audio.duration without error And the UI displays a non‑blocking indicator that context was truncated And Skip Back at t=0 does not underflow; Skip Forward at end stops or pauses playback predictably

Quick‑Copy Deep Link To Snippet

Given the user is viewing an anchored snippet When the user clicks Quick Copy Link Then a deep link is copied to the clipboard within 100 ms containing claimId, recordingId, anchorId, startMs, endMs, and applied pre/post context And opening the link in a supported environment opens ClaimFlow to the same recording, selects the anchor, applies the same context, and seeks to start And if the user lacks permission, an authorization error is shown and no audio is played And if the anchor is missing, the link falls back to the provided time range and displays a notice

Immutable Anchor Audit Trail with Proof Seal

"As a compliance officer, I want every timecode anchor to be sealed with immutable evidence so that chain-of-custody is provable during audits or disputes."

Description

Record each anchor’s creation, modification, and verification events in an append-only audit log and include its audio segment hash and transcript hash in the existing Proof Seal workflow. Generate a tamper-evident chain by timestamping and sealing anchor artifacts upon claim submission or export. Support auditor view that shows the snippet, hashes, sealing receipts, and responsible user/service. Provide exportable evidence bundles for legal/audit review.

Acceptance Criteria

Append-Only Anchor Audit Log

Given a user or service creates a new anchor, When the anchor is saved, Then an audit entry is written with event=CREATED, anchorId, claimId, actorId, actorType, timestamp (RFC3339 with ms), sequence=1, prevEntryHash=null, entryHash=SHA-256(payload). Given an existing anchor is edited, When changes are saved, Then an audit entry is written with event=UPDATED, sequence incremented by 1, a changeset of fields with from/to values, prevEntryHash set to the prior entryHash, entryHash recalculated, and no prior entry is altered. Given an anchor is verified, When the verifier confirms, Then an audit entry is written with event=VERIFIED, verifierId, method (manual|auto), signatureId (if available), and hash links preserved. Given any request attempts to modify or delete an audit entry, When the API receives PUT/PATCH/DELETE to audit resources, Then the request is rejected with 405/403 and an ATTEMPTED_TAMPER event is recorded. Given an anchor with N audit entries, When the chain is validated, Then every entry's prevEntryHash links to the previous entryHash and validation returns chainValid=true. Given a request to GET /anchors/{id}/audit for up to 200 entries, When processed under nominal load, Then the response returns in ≤300 ms and entries are sorted ascending by sequence.

Proof Seal Integration for Anchor Artifacts

Given claim submission or claim export is initiated, When the sealing job runs, Then for each anchor a record is included with audioHash=SHA-256 of the normalized audio snippet and transcriptHash=SHA-256 of the normalized transcript text, and these hashes are included in the Proof Seal payload. Given the sealing provider accepts the payload, When sealing completes, Then a receipt is stored per claim with providerTxId, merkleRoot (or equivalent), sealTimestamp (RFC3339 with ms), providerSignature, and each anchor stores a pointer to the receipt. Given any anchor artifacts change after a seal, When the claim is subsequently submitted or exported, Then a new seal is created referencing the new hashes and version; the original receipt remains immutable and linked to prior versions. Given sealing fails transiently, When the job encounters provider errors, Then retries occur up to 3 times with exponential backoff starting at 10s, and persistent failure sets sealStatus=failed and surfaces an alert on the claim. Given a sealed claim, When recomputing hashes from stored snippets/transcripts, Then the hashes match the stored values and the receipt verifies with the provider.

Time-Triggered Sealing on Submission and Export

Given a claim with anchors in Ready state, When a user clicks Submit Claim, Then sealing for all anchors starts within 5 seconds and completes within 60 seconds for up to 500 anchors under nominal conditions. Given a claim export is requested, When generating the export, Then any unsealed anchors are sealed before bundle creation; already sealed anchors are not resealed. Given a user performs a draft save, When no submission or export is triggered, Then no sealing operation occurs. Given sealing is in progress during submission, When the user navigates away, Then progress persists and resumes server-side until completion, with final status reflected in the claim timeline.

Auditor Read-Only Anchor Evidence View

Given a user with Auditor role opens a claim, When navigating to Anchor Audit, Then the UI displays for each anchor: playable audio snippet (±0.5s padding from timecode), transcript snippet, audioHash, transcriptHash, audit log entries, and sealing receipt details including provider and txId. Given the Auditor view is loaded for a claim with 200 anchors, When accessed under nominal load, Then the initial screen renders within 2 seconds and remaining anchors lazy-load without blocking UI. Given any attempt to edit anchors or audit entries from the Auditor view, When an action is invoked, Then controls are disabled and any direct API call returns 403 Forbidden and is logged. Given the Auditor clicks Copy on a hash or receipt id, When the action completes, Then the value is copied to the clipboard and a confirmation toast appears.

Exportable Evidence Bundle for Legal/Audit

Given an auditor exports a claim evidence bundle, When the export completes, Then the system produces a single ZIP containing: manifest.json, audit_log.jsonl, sealing_receipts.json, /audio_snippets, /transcripts, and checksums.txt with SHA-256 for every file. Given the manifest is opened, When inspected, Then it lists each anchor with anchorId, claimId, version, timecodeStart/End, audioHash, transcriptHash, actor identities, and receipt references, and includes a top-level bundleHash. Given the offline verifier tool is run against the ZIP, When verification completes, Then 100% of file checksums match, anchor hashes recompute to the same values, and receipt signatures validate. Given the export size exceeds 500 MB, When generated, Then the bundle is split into numbered parts with individual checksums and an index manifest describing the parts; otherwise a single ZIP is generated. Given the export contains PII, When the user selects Encrypt bundle, Then the ZIP is AES-256 password-protected and the password policy enforces 12+ characters with mixed types.

Automated Tamper Detection and Alerting

Given a claim's audit chain is scheduled for validation, When the background job runs hourly, Then it validates hash chains and sealing receipts for all active claims and records results. Given any broken hash link or altered entry is detected, When validation runs, Then the claim is flagged with chainValid=false, a Security Incident is opened with firstInvalidEntry details, and an alert banner appears to Admins and Auditors within 1 minute. Given the Proof Seal provider cannot verify a receipt, When validation runs, Then the claim is flagged with sealValid=false and remediation guidance is linked. Given a tamper write is attempted outside the append-only path, When the write occurs, Then the attempt is blocked, the source IP/user is recorded, and an ATTEMPTED_TAMPER event is appended to the log.

Anchor Export and API Access

"As an operations analyst, I want programmatic access and exports of timecode anchors so that I can integrate them with external QA and reporting systems."

Description

Offer secure APIs and exports for timecode anchors, including JSON/CSV exports and signed deep links to specific snippets. Provide field mappings (fact ID, start/end ms, speaker, confidence, hashes) and pagination/filters. Enforce RBAC and scope anchors to authorized users only. Support webhook notifications when anchors are created/updated to enable downstream systems (e.g., carrier QA tools) to ingest anchors in near real time.

Acceptance Criteria

JSON Export with Field Mappings, Filters, and Pagination

Given an authorized user with role Claims Manager for claim C123 containing 3 anchors When they call GET /v1/claims/C123/anchors?format=json&limit=2&fields=factId,startMs,endMs,speaker,confidence,hashes Then the response status is 200 And the Content-Type is application/json And the body.data contains exactly 2 anchors And each anchor includes factId,startMs,endMs,speaker,confidence,hashes And the body.pagination.nextCursor is present Given the nextCursor from the previous response When they call GET /v1/claims/C123/anchors?format=json&cursor={nextCursor}&fields=factId,startMs,endMs,speaker,confidence,hashes Then the body.data contains the remaining 1 anchor And no anchor from other claims is present Given filter parameters confidenceMin=0.85&speaker=Adjuster When they call GET /v1/claims/C123/anchors?format=json&confidenceMin=0.85&speaker=Adjuster Then every returned anchor has confidence >= 0.85 and speaker = "Adjuster" Given a createdAt range filter of 2025-09-01T00:00:00Z to 2025-09-30T23:59:59Z When they call GET /v1/claims/C123/anchors?format=json&createdAtStart=2025-09-01T00:00:00Z&createdAtEnd=2025-09-30T23:59:59Z Then every returned anchor has createdAt within the specified range

CSV Export with Field Mappings and Escaping

Given an authorized user for claim C123 containing anchors When they call GET /v1/claims/C123/anchors/export?format=csv Then the response status is 200 And the Content-Type is text/csv And the first row is a header containing factId,startMs,endMs,speaker,confidence,hashes And each subsequent row contains mapped values for those columns And fields with commas, quotes, or newlines are properly quoted per RFC 4180 And the CSV includes only anchors from claim C123 Given a claim C999 with zero anchors When they call GET /v1/claims/C999/anchors/export?format=csv Then the response contains only the header row and no data rows

Signed Deep Link Generation and Validation for Snippets

Given an authorized user and anchor A123 on claim C123 When they call POST /v1/anchors/A123/deeplinks with body { ttlSeconds: 600 } Then the response status is 201 And the body contains url, token, expiresAt, anchorId, startMs, endMs And the url includes a signature and expiresAt parameter Given the generated url before expiry When it is requested by any holder Then the response resolves to the exact snippet bounded by startMs and endMs with status 200 or a 302 redirect to the playback resource Given the same url after expiresAt When it is requested Then the response status is 403 Given a tampered token or signature When the url is requested Then the response status is 401 Given the deep link for anchor A123 When a request attempts to access a different anchor or claim via the link Then access is denied

RBAC and Tenant Scoping for Anchor Access

Given a user in tenant T1 assigned to claim C123 with role Adjuster When they call GET /v1/claims/C123/anchors Then the response status is 200 And only anchors for claim C123 in tenant T1 are returned Given the same user When they call GET /v1/claims/C999/anchors for a claim they are not assigned to Then the response status is 403 Given a user with role Admin in tenant T1 When they call GET /v1/tenants/T1/anchors Then the response status is 200 And only anchors from tenant T1 are returned Given a user from tenant T2 When they attempt to access any T1 anchor endpoint Then the response status is 403

Webhook Notifications on Anchor Create and Update

Given a webhook subscription for tenant T1 with event types anchor.created and anchor.updated and a shared secret When an anchor is created for claim C123 Then a POST is delivered to the subscriber within 10 seconds And the request body includes eventId, eventType, occurredAt, tenantId, claimId, anchorId, factId, startMs, endMs, speaker, confidence, hashes And the request contains an X-Signature header with a valid HMAC-SHA256 signature over the raw body using the shared secret Given the subscriber responds with HTTP 500 When the platform retries delivery Then at least 3 retries occur with exponential backoff And all retries reuse the same eventId for idempotency Given the subscriber receives duplicate deliveries When it de-duplicates by eventId Then processing occurs exactly once

Hashes and Chain-of-Custody Integrity Fields

Given an export response or webhook payload for anchor A123 When inspecting the hashes object Then it contains deterministic SHA-256 values for anchorHash, mediaHash, and transcriptHash And anchorHash is the SHA-256 of the canonicalized anchor payload excluding the hashes object And mediaHash equals the SHA-256 of the source media file referenced by the anchor And transcriptHash equals the SHA-256 of the transcript segment text used to derive the anchor And recomputing these values from available artifacts reproduces the same hashes

API Authentication and Authorization Scopes

Given a valid access token with scope anchors:read for tenant T1 When calling GET /v1/claims/C123/anchors Then the response status is 200 Given an access token missing anchors:read When calling GET /v1/claims/C123/anchors Then the response status is 403 Given an expired or invalid access token When calling any /v1/*/anchors endpoint Then the response status is 401 Given a valid access token with scope anchors:write When calling POST /v1/anchors/A123/deeplinks Then the response status is 201

Confidence Nudge

Highlights low‑confidence extractions inline and suggests quick confirmations (e.g., “Say the VIN slowly” or “Repeat address”). Supports voice or tap corrections and routes unresolved items to a lightweight review queue. Improves data accuracy without disrupting the 2‑second experience and shortens approval cycles.

Requirements

Inline Confidence Highlighting

"As an independent adjuster, I want low-confidence fields highlighted in the transcript so that I can quickly spot and fix risky data without hunting through the record."

Description

Display low-confidence NLP extractions inline within the intake transcript and form fields using subtle visual cues (e.g., amber underline, icon). Each highlighted token/field exposes its confidence score and a minimal hint on hover/tap. Clicking or focusing a highlight opens a compact correction panel without navigating away. Supported entities include VIN, policy number, loss date, address, claimant name, phone, and email. The component consumes confidence metadata from the extraction service, respects per-entity thresholds, and emits structured events for analytics. It must be non-blocking, preserve the 2‑second experience, and gracefully degrade if confidence data is unavailable.

Acceptance Criteria

Highlight Low-Confidence Entities Inline

Given the extraction service returns entities with confidence scores and per-entity thresholds are loaded When the intake transcript and form fields render Then each VIN, policy number, loss date, address, claimant name, phone, and email with confidence below its threshold is visually marked with an amber underline and a low-confidence icon And entities at or above their thresholds render without highlight And the visual cues do not modify field values or cursor behavior

Reveal Confidence Score and Hint on Hover/Tap

Given a highlighted entity is visible on a pointer device When the user hovers the highlight or moves keyboard focus to it Then a tooltip appears within 200 ms showing the numeric confidence as a percentage (one decimal) and a concise hint And when hover/focus leaves, the tooltip dismisses within 200 ms And on touch devices, the first tap shows the tooltip and a subsequent tap or an explicit Edit affordance is required to open the correction panel

Open Compact Correction Panel In-Place

Given a highlighted token or field is present When the user clicks it or presses Enter or Space while focused Then a compact correction panel opens inline without page navigation And it pre-fills the current extracted value and entity label and provides Save and Cancel controls And the panel opens within 200 ms and does not block other input And clicking Save updates the underlying value, removes the highlight for that entity, emits an analytics event, and closes the panel And clicking Cancel, pressing Escape, or clicking outside closes the panel without changing the value

Per-Entity Threshold Application

Given thresholds exist for VIN, policy number, loss date, address, claimant name, phone, and email and a default threshold is defined When determining whether to highlight an entity Then the entity’s confidence is compared against its specific threshold, or the default when a specific one is missing And if confidence is less than the threshold the entity is highlighted, otherwise it is not And for VIN confidence 0.86 with threshold 0.90 the VIN is highlighted And for policy number confidence 0.93 with threshold 0.90 the policy number is not highlighted And updating a threshold in configuration takes effect on next render without code changes

Structured Analytics Events for Highlight Interactions

Given the analytics collector is available When a highlight is displayed, a tooltip is shown, a correction panel is opened, a value is saved, or a panel is dismissed Then the component emits a structured event for each action with fields: event_name, entity_type, confidence (0–1), action, origin (hover|focus|click|tap), timestamp, session_id And events are emitted exactly once per action and failures to emit do not affect UI behavior

Graceful Degradation Without Confidence Metadata

Given confidence metadata is missing, null, or fails to load When the intake transcript and form fields render Then no low-confidence highlights or icons are shown and no errors are displayed to the user And correction affordances are not shown solely due to missing confidence metadata And an analytics event named confidence_unavailable is emitted with a request identifier And overall render remains within the 2-second experience target

Performance and Non-Blocking Behavior

Given typical production payloads and target devices When rendering the transcript and form with highlighting enabled Then time from data availability to interactive state with highlights is less than or equal to 2,000 ms at the 95th percentile And opening a correction panel completes in less than or equal to 200 ms at the 95th percentile And highlighting computation does not block input; input latency remains less than or equal to 100 ms at the 95th percentile during interactions

Multi‑Modal Quick Confirmations

"As a claims manager, I want one-tap or voice confirmations for uncertain fields so that I can correct data without breaking my flow."

Description

Provide one-tap and voice-driven prompts to confirm or correct low-confidence fields in place. For voice, play concise guidance such as “Say the VIN slowly” or “Repeat the street address,” capture the utterance, and re-validate. For tap, present pre-filled editable inputs with smart masks (VIN, phone, postal code) and single-tap confirm. Include retry handling, partial acceptance (e.g., confirm city, edit ZIP), and immediate re-scoring. The UX must be accessible (screen readers, large tap targets), mobile/desktop friendly, and localized. All interactions must complete within the overall 2‑second interaction budget and never block continued intake.

Acceptance Criteria

Inline Low-Confidence Highlight and Prompt

Given an extracted field’s confidence is below the configured threshold T When the field is rendered in the intake UI Then the field is visually highlighted inline and labeled as “Needs confirmation” And both options “Confirm” (tap) and “Speak” (voice) are visible and enabled And the user can continue to the next field without interacting with the prompt And the highlight appears adjacent to the field it refers to And the highlight state is removed automatically once the field’s confidence is >= T

Tap Confirmation with Smart Masks and Partial Acceptance

Given a low-confidence field is selected for tap confirmation When the user opens the confirmation input Then the input is pre-filled with the current value and includes a type-specific mask and validation And a single-tap Confirm action is available to accept the pre-filled value if unchanged And for VIN: the mask enforces 17 uppercase alphanumeric characters excluding I, O, Q and validates checksum where applicable And for Phone: the mask formats per current locale and normalizes to E.164 on save And for Postal Code: the mask validates per current locale or falls back to a generic alphanumeric mask with length constraints And for multi-part addresses: the user may confirm any sub-field (e.g., City) while editing others (e.g., ZIP), with confirmed sub-fields immediately marked as accepted And validation errors are announced inline and do not block navigation to other fields

Voice Confirmation with Guided Prompt and Mic Handling

Given a low-confidence field is selected for voice confirmation and microphone access is granted When the user taps Speak Then a concise localized guidance prompt plays (<= 7 words, <= 2 seconds duration) And the system records the utterance, transcribes it using the current locale, and re-validates the field And if the resulting confidence is >= T, the field value is updated and marked confirmed And if microphone access is denied or revoked, the system gracefully falls back to tap confirmation without blocking intake And the user can cancel recording at any time without losing previously entered data

Retry Handling and Review Queue Routing

Given a confirmation attempt (tap or voice) results in confidence remaining below threshold T When the user retries Then the system allows up to R configurable retries (default 2) per field And each retry preserves any confirmed sub-fields and only requests remaining unresolved portions And the user can skip further attempts and continue intake without blocking And when retries are exhausted or the user skips, the item is added to the lightweight review queue within 1 second And the review queue entry includes field name, current value, candidate values, confidence scores, attempt count, and (for voice) transcript reference

Immediate Re-Scoring and UI Update

Given a user confirms or corrects a value via tap or voice When the user submits the confirmation Then the field’s confidence is re-scored immediately and the updated score is reflected in the UI within 200 ms And if the confidence is >= T, the inline nudge is cleared and downstream workflow rules are re-evaluated without creating duplicate tasks And if the confidence remains < T, the nudge persists and offers available confirmation options And any dependent fields (e.g., address components) are re-validated and updated accordingly

Accessibility, Localization, and Cross-Platform UX

Given the intake UI is used across supported devices and locales When interacting with prompts and inputs Then all controls have accessible names, roles, and states announced correctly by screen readers (e.g., VoiceOver, TalkBack, NVDA) And focus order is logical, visible focus indicators are present, and keyboard navigation supports all actions (including Speak trigger and Confirm) And tap/click targets are at least 44x44 px with a minimum 4.5:1 text contrast ratio And all guidance prompts, labels, error messages, and masks are localized to the current locale with correct number/address/phone formats And the UI is responsive and usable on mobile and desktop viewports per the product support matrix

Performance Budget and Non-Blocking Behavior

Given a user initiates a confirmation action (tap or voice) When the system processes the action Then the end-to-end interaction (prompt render to result or fallback) completes within 2 seconds at the 95th percentile on supported devices and networks And UI threads remain responsive (no frozen frames > 100 ms) during processing And long-running work executes asynchronously so the user can continue intake without waiting And telemetry captures latency percentiles for highlight render, prompt playback, transcription, validation, and re-scoring

Dynamic Confidence Thresholds by Entity

"As a carrier admin, I want to tune when nudges fire for different fields so that we balance accuracy with speed for our teams."

Description

Enable configurable nudge thresholds per entity type, workflow, and carrier. Provide sensible defaults (e.g., VIN > 0.95, address > 0.85) and allow admins to tune thresholds and retry limits via settings or config files. Incorporate contextual heuristics (e.g., cross-check against policy data, geocoding match quality) to raise or lower thresholds at runtime. Expose a rules API for advanced conditions (e.g., higher scrutiny for total-loss claims). Persist versioned configurations, validate changes, and roll back safely.

Acceptance Criteria

Per-Entity Defaults Applied Per Carrier/Workflow

Given a tenant (carrier) with no custom thresholds for a workflow When ClaimFlow evaluates an extracted VIN (confidence = 0.93) and Address (confidence = 0.86) Then a nudge is triggered for the VIN (0.93 < 0.95) and not for the Address (0.86 >= 0.85) And the applied thresholds are VIN 0.95 and Address 0.85 from system defaults And the thresholds are scoped to the specific carrier and workflow with no cross-tenant leakage And an audit record notes default-sourced thresholds for the evaluation

Admin UI and Config File Updates with Validation and Versioning

Given a tenant-admin is authenticated and on Settings > Confidence Thresholds When they set VIN.threshold = 0.97 and VIN.retry_limit = 2 and click Publish Then inputs are validated (threshold in [0.0,1.0], retry_limit integer 0–5) and invalid inputs are rejected with inline errors And a new config version is created with author, timestamp, semantic diff, and status Published And the new version becomes active within 60 seconds and is used for subsequent evaluations And when a valid JSON/YAML config file with the same changes is uploaded via Admin API Then the same validations, versioning, and activation semantics apply

Rules API Enforces Advanced Conditions with Defined Precedence

Given a ruleset defines: when claim.type = "total_loss" then VIN.threshold = 0.98 and VIN.retry_limit = 3 When a total-loss claim is evaluated for VIN confidence 0.97 Then a nudge is triggered because 0.97 < 0.98 and retry_limit is 3 for that entity And when a non-total-loss claim is evaluated, the rule does not alter VIN settings And if a rules payload is syntactically invalid, the API responds 400 with error details and no config version is created And precedence is Base Defaults < Tenant Overrides < Rules API < Contextual Heuristics

Contextual Heuristics Adjust Thresholds at Runtime with Traceability

Given Address base threshold = 0.85 and geocoding.match_quality = 0.60 When evaluating the effective threshold Then the threshold is raised by 0.05 to 0.90 (clamped to [0.0,1.0]) And the evaluation log includes reason "low_geocode_quality" and the before/after values And given VIN base threshold = 0.95 and policy.vin_verified = true When evaluating the effective threshold Then the threshold is lowered by 0.05 to 0.90 (clamped to [0.0,1.0]) And heuristic adjustments are applied after Rules API overrides and before nudge decision

Safe Rollback to Previous Version Without Downtime

Given active config version v7 and prior version v6 exists When an admin initiates a rollback to v6 Then v6 becomes the active version atomically without service interruption And all evaluations started after rollback use v6, while in-flight evaluations complete with their originally loaded version And an audit log records the rollback initiator, timestamp, from_version v7, to_version v6, and reason And if v6 fails validation on load, rollback is aborted and v7 remains active with an error reported to the admin

Runtime Performance and Graceful Degradation

Given threshold evaluation requires tenant overrides, rules, heuristics, and context lookups When processing 1,000 requests under nominal load Then the added evaluation latency is <= 50 ms at p95 and <= 100 ms at p99 And context lookups each have a 200 ms timeout; on timeout the system falls back to base thresholds and logs a warning And a per-tenant cache is used so that repeated evaluations with identical inputs avoid external lookups for 5 minutes or until config/rule version changes And the end-to-end nudge decision preserves the product 2-second experience budget

Retry Limits Route Unresolved Items to Review Queue

Given VIN.retry_limit = 2 and a VIN extraction with initial confidence 0.60 and threshold 0.95 When the user provides up to 2 corrections (voice or tap) and the confidence remains below 0.95 Then the system stops prompting for VIN corrections and routes the item to the review queue And the review queue record includes entity type, latest confidence, effective threshold, retries_used = 2, and suggestion history And if the confidence meets or exceeds 0.95 before retries are exhausted, the item is not routed to the review queue

Unresolved Items Review Queue

"As a claims supervisor, I want unresolved low-confidence items queued for review so that nothing blocks approvals or slips through unnoticed."

Description

Automatically route unconfirmed or repeatedly failed extractions into a lightweight review queue. Create concise review tasks with links to the original artifacts (photo, transcript snippet), suggested values, confidence scores, and change history. Support assignment, prioritization, SLAs, and bulk actions. Provide keyboard-first triage, quick approve/edit/reject, and audit logging. Notify assignees via in-app alerts and optional email. Seamlessly update the claim record upon resolution and unblock downstream workflow steps.

Acceptance Criteria

Auto-route unresolved extractions to Review Queue

Given an extraction remains unconfirmed at the end of the intake step and its confidence score is below the configurable threshold (default 0.60) When the intake step completes Then a Review Queue task is created and linked to the claim within 2 seconds and no duplicate task is created for the same extraction And the task records attemptCount incremented by 1 if a task already exists And extractions that fail confirmation twice (>=2 failed attempts) are routed immediately upon the second failure And extractions confirmed or corrected by the user are not routed

Review task content completeness and traceability

Given a Review Queue task created from an extraction sourced from a photo and transcript When the task is opened Then it displays field name and ID, claim ID, suggested value, confidence score (0–1 with two decimal places), and attemptCount And it shows change history entries with timestamp, actor, previous value, new value, and reason (if provided) And it provides working links to the original artifacts: photo opens to the correct image with the extracted region highlighted and transcript opens to the snippet with the phrase highlighted And artifact links respect permissions: authorized users can open, unauthorized users receive an access denied message And all values in the task are read-only until an approve/edit/reject action is initiated

Assignment, prioritization, SLAs, and notifications

Given Review Queue tasks exist with varying priority and SLA due times When the queue list is viewed Then tasks are sorted by priority (P1 highest to P3 lowest), then by SLA due time ascending, then by createdAt ascending And an unassigned task can be assigned to a user or team and reflects the assignee immediately When a task is assigned to a user with notifications enabled Then that user receives an in-app alert instantly and an email within 60 seconds When an SLA enters Due Soon (<=15 minutes to due) Then the task shows a yellow indicator and the remaining time countdown updates at least every 60 seconds When an SLA is breached Then the task shows a red Overdue state, escalates per configuration, and the assignee receives an in-app alert and email (if enabled) within 60 seconds

Keyboard-first triage with approve/edit/reject

Given a user focuses the Review Queue list When they use only the keyboard (Arrow Up/Down to navigate, Enter to open, A=Approve, E=Edit, R=Reject, Esc=Close) Then they can complete an approve action for a task in 3 or fewer keystrokes after opening And all actions (approve/edit/reject) execute without mouse input and provide visible focus states And edits support tabbing through fields and form validation messages appear without requiring a mouse And action latency from keypress to UI confirmation is <=500 ms on a stable connection And after action completion, focus returns to the next task in the list

Bulk actions for fast triage

Given a user selects multiple tasks using keyboard or checkbox toggle When they choose Bulk Approve with suggested values Then all selected tasks are approved in a single operation and a success count and failure count are displayed When they choose Bulk Edit and supply a value Then the value is applied to all selected tasks of the same field type; tasks with incompatible types are skipped with a clear reason When they choose Bulk Reject and select a reason code Then all selected tasks are rejected with that reason and require a confirmation step before execution And bulk operations process up to 200 tasks per request with progress feedback and per-item atomicity (a failure on one does not stop others)

Seamless claim update and workflow unblocking

Given a task is approved or edited in the Review Queue When the action is confirmed Then the associated claim record is updated atomically with the resolved value and the Review Queue task status changes to Resolved And any downstream workflow step waiting on that field transitions from Blocked to Ready within 2 seconds And repeated submissions of the same action are idempotent and do not create duplicate updates When a concurrent change to the same field occurs Then the user is prompted to reconcile differences before the action is applied

Comprehensive audit logging of triage actions

Given any triage action occurs (approve, edit, reject, bulk operation, assignment change) When the action is completed Then an immutable audit log entry is created within 1 second containing actor, timestamp (UTC), claim ID, task ID, action type, source=ReviewQueue, previous value, new value, reason (if provided), and bulkOperationId (if applicable) And the entry is visible in the claim’s audit log UI and exportable via the audit export endpoint And audit entries cannot be altered or deleted by end users

Correction Capture & Model Feedback Loop

"As an ML engineer, I want structured correction data from nudges so that we can improve extraction accuracy over time."

Description

Capture every confirmation, correction, and rejection with before/after values, context (entity type, source artifact), and confidence deltas. Store as structured, privacy-compliant events with redaction for PII per carrier policy. Batch and deliver labeled examples to the ML feedback pipeline with lineage and model/version tags. Provide opt-in controls, data retention settings, and performance safeguards so feedback collection does not impact latency. Expose a simple export for offline training and a toggle to exclude sensitive fields.

Acceptance Criteria

Inline Correction Event Capture

Given a user confirms, corrects, or rejects an extracted field in Confidence Nudge When the action is submitted Then an event is created containing action_type (confirm|correct|reject), entity_type, field_name, before_value, after_value, source_artifact_type/id, extraction_confidence_before, extraction_confidence_after (if recalculated), confidence_delta, locale, timestamp (UTC ISO8601), session_id, claim_id, anonymized_actor_id, event_id, and schema_version Given an event is created When validated against the event schema Then all required fields are present, types conform, and model_name/model_version from the originating extraction are attached Given multiple actions occur in a single intake session When events are recorded Then each event is independently persisted and linked via session_id and claim_id without overwriting prior events Given a transient failure during capture When a user submits an action Then the event is queued durably and retried with exponential backoff up to 5 attempts within 2 minutes total, without blocking the user flow

Privacy Redaction and Policy Compliance

Given carrier-specific PII policy rules are configured When an event is persisted Then PII-marked fields (e.g., SSN, full VIN if excluded, phone, email, address lines) are redacted or tokenized per policy before storage and transport Given a field is marked sensitive and exclude_from_feedback=true When a correction is captured Then before_value and after_value are replaced with redaction tokens and excluded from downstream batches and exports while non-sensitive metadata is retained Given an auditor reviews the system When exporting a 1,000-event sample Then zero unredacted PII values are present and an audit log shows policy_version, redaction method, and time of application Given a regulatory deletion request for a claim_id When the purge job runs Then all related feedback events are hard-deleted within 24 hours and the deletion is logged with evidence

Batching and Delivery to ML Feedback Pipeline

Given feedback events are captured When batching runs Then events are grouped by carrier and model_version with batch size up to 500 events or a max wait of 60 seconds, whichever occurs first Given a batch is sent to the ML feedback endpoint When the endpoint responds with 2xx Then events are marked delivered with delivery_timestamp and feedback_job_id Given the endpoint responds non-2xx or times out When retries occur Then the system retries with exponential backoff and jitter for up to 6 hours while preserving event order within a batch Given duplicate delivery risk When a batch is retried Then idempotency keys ensure the receiver achieves exactly-once application while the sender provides at-least-once delivery with deduplication rate ≥ 99.9% Given rolling 24-hour operations When delivery metrics are evaluated Then successful delivery rate is ≥ 99.5% and stuck batches trigger an alert within 5 minutes

Opt-in Controls and Retention Settings

Given tenant-level feedback capture is disabled by default When a carrier has not opted in Then no feedback events are captured or exported and admin surfaces indicate how to enable capture Given a carrier enables feedback capture When retention_days is configured (or defaults to 180) Then events older than retention_days are automatically purged daily and the purge is logged with counts and time range Given per-field sensitivity toggles exist When exclude_from_feedback is enabled for a field Then event values are masked while recording entity_type and confidence_delta for analysis Given a carrier disables feedback capture When the setting is toggled off Then new event capture stops within 1 minute; if purge_on_disable is enabled, queued and stored events are deleted within 24 hours

Latency and Experience Safeguards

Given a user submits a correction, confirmation, or rejection When capture executes Then additional client-side processing adds ≤ 5 ms p95 and server-side processing adds ≤ 20 ms p95 over 10,000 actions, preserving the overall 2-second experience target Given the ML feedback pipeline is degraded or unavailable When corrections occur Then capture is deferred to an async queue and the user flow remains uninterrupted with no error presented to the user Given queue backpressure thresholds are exceeded When thresholds are hit Then the system reduces capture via a configurable sampling rate (default 50%), emits metrics and alerts, and never blocks the user action Given retries are exhausted for an event When persistence ultimately fails Then a minimal error event with error_code and masked context is stored if possible, and an alert is fired within 2 minutes

Offline Training Export

Given an authorized admin requests an export When filters include date range, carrier, entity_types, and include_values toggle Then the system generates CSV and Parquet files with schema: event_id, timestamps, claim_id, session_id, entity_type, field_name, before_value_masked, after_value_masked, action_type, confidence_before, confidence_after, confidence_delta, source_artifact_type/id, model_name, model_version, lineage_ids, policy_version, redaction_flags Given include_values=false or sensitive fields are excluded When the export is generated Then all sensitive values are masked and no PII is present in the files Given up to 1,000,000 events match the request When export is requested Then files are available within 2 minutes at the 95th percentile via a signed URL valid for 24 hours and accompanied by a checksum Given lineage requirements When export completes Then a manifest JSON is produced with counts by entity_type, time window, model_version, and a hash of contents

Lineage and Model Tagging

Given an extraction produced an initial value When a feedback event is created Then the event includes model_name, model_version, extraction_run_id, and document_id linking to the source artifact Given models are upgraded When new events are captured post-upgrade Then events reference the new model_version and do not overwrite history from previous versions Given analysts query feedback data When filtering by model_version and entity_type Then the results contain only matching events with zero cross-version contamination, verified by sampled checks

Nudge Analytics & Reporting

"As an operations lead, I want analytics on nudge usage and outcomes so that I can optimize thresholds and demonstrate ROI."

Description

Offer dashboards and exports showing nudge volume, trigger rate, acceptance/correction rates, average time-to-confirm, and downstream impact on approval cycle time and rework. Break down by entity, workflow, carrier, channel (voice/tap), and user segment. Support cohort comparisons, A/B testing for thresholds, anomaly detection (e.g., sudden spike in VIN corrections), and funnel views from extraction to resolution. Integrate with the existing BI layer and emit standardized telemetry events.

Acceptance Criteria

Analytics Dashboard: Segmented Metrics Overview

- Users with Analytics_View permission can access the Nudge Analytics dashboard. - Time range selector supports Last 24h, 7d, 30d, and Custom (UTC), and applies to all widgets. - Metrics tiles display: Nudge Volume, Trigger Rate, Acceptance Rate, Correction Rate, Avg Time-to-Confirm, Unresolved Rate, Review Queue Count; definitions are shown on-hover. - Filters for entity_type, workflow_id, carrier_id, channel (voice|tap), and user_segment can be applied singly or in combination; applied filters are reflected in URL for shareable views. - With filters applied, all widgets update within 3 seconds p95 for datasets ≤ 1,000,000 nudges. - Given a seeded dataset of 10,000 extractions with 2,500 nudges and 1,000 acceptances, Trigger Rate displays 25.0% ±0.1% and Acceptance Rate 10.0% ±0.1%. - Channel breakdown totals equal overall totals within ±1 event.

CSV Export: Filtered Metrics and Events

- Export button is enabled on the analytics dashboard; clicking opens a modal with two modes: Aggregated Metrics and Event-Level. - Aggregated export includes one row per selected breakdown (entity_type, workflow_id, carrier_id, channel, user_segment, time_bucket) with columns: nudge_volume, trigger_rate, acceptance_rate, correction_rate, avg_time_to_confirm_ms, unresolved_rate, review_queue_count. - Event-level export includes one row per nudge event with columns: event_id, claim_id, entity_type, channel, user_segment, workflow_id, carrier_id, event_type, event_ts, ab_variant, confidence_score, resolution_type (confirmed|corrected|unresolved), time_to_confirm_ms. - Exports respect current filters and time range; time zone is UTC; numbers use dot decimal; CSV is UTF-8 with header. - For data sets ≤ 500,000 rows, export completes and downloads within 60 seconds p95; for larger sets, user is prompted to narrow filters. - Exported totals match on-screen metrics within ±0.1%.

Cohort Comparison and A/B Threshold Testing

- Users can create up to 5 cohorts by specifying filters and/or configuration snapshots (e.g., low-confidence threshold versions) and save them by name. - The comparison view displays side-by-side metrics per cohort and the absolute and percent differences. - For A/B tests where ab_variant ∈ {A,B}, the system computes uplift for acceptance_rate and avg_time_to_confirm_ms and displays 95% confidence intervals; results are marked "insufficient sample" if any arm has < 500 nudges. - Variant assignment is read from telemetry and is immutable per nudge; mismatched or missing variants are excluded from A/B calculations and reported as a count. - Users can export the cohort comparison table as CSV; exported numbers match on-screen within ±0.1%.

Funnel View: Extraction-to-Resolution Journey

- Funnel stages are defined as: Extractions Attempted -> Nudges Triggered -> Nudges Viewed -> Confirmations -> Corrections -> Unresolved -> Routed to Review -> Resolved in Review. - The funnel shows counts, conversion rates between stages, and average time between (Triggered->Viewed, Viewed->Resolved). - Applying filters/time range updates the funnel within 3 seconds p95 and persists stage definitions in a help tooltip. - The sum of Confirmations + Corrections + Unresolved at the 'Viewed' step equals 'Viewed' within ±1 count. - Clicking any stage opens a sample list (up to 100 items) with claim_id and entity_type for audit.

Anomaly Detection and Alerting for Nudge Metrics

- Users can enable anomaly detection per metric (e.g., VIN correction rate) with a 7-day rolling baseline and 3-sigma threshold; minimum baseline volume is 1,000 events. - When an anomaly is detected, an alert banner appears on the dashboard and a webhook is fired within 2 minutes with payload including metric, current value, baseline mean/std, z-score, filters context, and sample_ids. - Anomalies are de-duplicated within a 60-minute window; users can mute a metric for 24 hours. - Synthetic test: injecting a 3x spike in VIN corrections for carrier X triggers an alert with z-score > 3.0. - All anomaly events are logged and can be exported.

Downstream Impact on Approval Cycle Time and Rework

- The impact view attributes claims to cohorts {with_nudges, without_nudges} based on whether any Confidence Nudge event occurred during intake. - Metrics displayed: median and p90 approval_cycle_time_hours (intake_start to approval_complete) and rework_rate (% claims with post-approval corrections). - Results can be filtered by entity_type, workflow_id, carrier_id, channel, and user_segment; filters apply to both cohorts. - Differences (absolute and percent) are computed along with 95% CIs using bootstrapping (1,000 resamples) for cycle times and proportion CIs for rework; mark "insufficient sample" if cohort < 200 claims. - Data freshness SLA: metrics include data up to 15 minutes ago; last_updated timestamp is shown.

Standardized Telemetry and BI Integration

- The system emits the following events to the BI layer: nudge_triggered, nudge_shown, nudge_confirmed, nudge_corrected, nudge_unresolved, review_routed, review_resolved, funnel_transition, ab_variant_assigned, export_generated. - Each event includes required properties: event_id (UUIDv4), claim_id, workflow_id, carrier_id, entity_type, channel, user_segment, confidence_score, ab_variant (nullable), event_ts (ISO 8601 UTC), schema_version, and, where applicable, resolution_type and time_to_confirm_ms. - Events are delivered to the BI ingestion topic within 60 seconds p99 with end-to-end success rate ≥ 99.9% and are idempotent by event_id; duplicates are < 0.1%. - Contract tests validate schema compliance; any schema change increments schema_version and is backward compatible; noncompliant events are quarantined with an error code and surfaced in ops dashboard. - A reference model/dashboard in the existing BI layer reproduces core metrics (trigger_rate, acceptance_rate, correction_rate) and matches in-app metrics within ±0.2%.

Latency Guardrails for 2‑Second Experience

"As an adjuster, I want nudges to appear instantly without slowing intake so that I can maintain my rhythm and finish faster."

Description

Define and enforce an end-to-end latency budget for nudge detection and rendering (95th percentile under 2 seconds). Precompute candidate nudges where possible, stream confidence metadata, and render nudges asynchronously to avoid blocking input. Prefetch prompt templates and voice guidance, cache speech models locally, and fall back to non-voice prompts on poor networks. Instrument with SLOs, alerts, and traceability to isolate slow components. Provide graceful degradation rules (e.g., limit concurrent nudges, batch updates) to preserve user flow.

Acceptance Criteria

E2E 95th-Percentile Nudge Render ≤2.0s (Ref Devices, Typical Networks)

Given a low-confidence extraction is produced during a claims intake on reference devices (iPhone 12/13, Pixel 6/7) over typical LTE/Wi‑Fi (20–80 ms RTT, 3–50 Mbps) When the nudge is detected and prepared Then the time from extraction availability to nudge visible is ≤ 2000 ms at P95 and ≤ 1200 ms at P50 across ≥ 5,000 events per platform, verified via RUM timers and traces And fewer than 0.5% of sessions exceed 2500 ms end-to-end

Asynchronous Rendering Does Not Block User Input

Given the user is typing or speaking during intake When a nudge is detected, fetched, and rendered Then keystroke latency is ≤ 50 ms at P95 and audio capture has zero dropped samples attributable to nudge processing And the UI thread is not blocked > 16 ms for more than 5 consecutive frames during render And input focus remains unchanged and caret position is preserved

Streaming Confidence Metadata Within Budget

Given the server computes confidence scores for new photos/messages When confidence metadata becomes available Then the first metadata chunk arrives at the client ≤ 250 ms at P95 after server compute completion via WebSocket And if WebSocket is unavailable, Server-Sent Events or HTTPS polling is used with added latency ≤ 300 ms at P95 And the client begins render preparation immediately upon first chunk without awaiting the full payload

Prefetch Prompt Templates, Voice Guidance, and Local Speech Model Caching

Given app cold start or session start with network available and ≥ 200 MB free storage When the nudge subsystem initializes Then prompt templates and voice guidance audio prefetch achieve a ≥ 90% cache hit rate over a rolling 7-day window And on-device speech model loads from cache ≤ 300 ms at P95 when a voice nudge is first invoked And on cache miss, model download is deferred to idle and a non-voice prompt is shown within 1200 ms at P95 without blocking input And total initial voice asset download size is ≤ 25 MB and supports resumable download

Adaptive Fallback to Non‑Voice on Poor Networks

Given network quality drops below thresholds (RTT > 200 ms or throughput < 1 Mbps or packet loss > 2%) When a voice nudge would otherwise be presented Then a non-voice prompt is shown within 1200 ms at P95 and no voice assets are fetched mid-flow And users can complete the correction via tap with added latency ≤ 100 ms versus baseline

Graceful Degradation with Concurrency Limits and Batching

Given multiple low-confidence extractions occur within a short interval When nudges are queued for display Then no more than 2 nudges are shown concurrently; additional nudges are queued And updates are batched to render at most once per 500 ms And the E2E nudge render SLO (P95 ≤ 2000 ms) is maintained under a synthetic load of 5 simultaneous extractions

Observability: SLOs, Alerts, and Traceability for Latency Breaches

Given production traffic is receiving nudges When any slice (device, network, region, version) breaches the P95 ≤ 2000 ms SLO for 5 consecutive minutes Then an alert is emitted within 5 minutes with slice metadata and top suspected components And distributed traces include spans for detection, streaming, and render with a shared correlation ID for ≥ 95% of sampled sessions And dashboards display P50/P95/P99 by component and slice with ≤ 1-minute staleness And at least 1% of sessions have full-fidelity traces for root-cause analysis

Phrase Normalizer

Maps field jargon and regional slang to standardized terms and codes (ACORD fields, cause‑of‑loss categories, part names) during transcription. Auto-expands abbreviations and harmonizes synonyms so downstream systems receive clean, consistent values. Reduces manual mapping, prevents integration errors, and improves analytics quality.

Requirements

Canonical Terminology Dictionary

"As a product administrator, I want to manage a canonical dictionary of synonyms and codes so that claims data is standardized and integrations remain consistent across all workflows."

Description

Provide a centrally managed, versioned lexicon that maps industry jargon, abbreviations, and regional slang to standardized codes and labels (e.g., ACORD fields, ISO cause-of-loss, OEM part catalogs). Supports per-line-of-business scoping (Auto, Property, GL), regional tagging, effective dating, and bulk import/export. Exposes read APIs to the NLP engine at runtime and an admin UI for CRUD operations, conflict detection, and rule testing. Ensures consistent normalization across transcription, forms, and messaging within ClaimFlow.

Acceptance Criteria

Effective Dating and Versioned Resolution

- Given multiple dictionary versions with effective_start and effective_end dates, when the NLP engine requests a mapping at a specified timestamp T, then the API returns the mapping from the version where effective_start <= T < effective_end. - Given a term that is retired (end-dated) before T, when requested, then the API returns HTTP 404 with code TERM_NOT_EFFECTIVE. - Given a future-dated change, when T is before effective_start, then the current active version is returned. - Given no applicable version exists for T, then the API returns 404 with code NO_ACTIVE_VERSION. - Given request omits T, then the API resolves using the server's current UTC time. - All responses include version_id, effective_start, and effective_end in the payload.

Line-of-Business and Regional Scoping

- Given lob and region are supplied, when a phrase maps differently by scope, then the mapping returned matches the most specific available scope in this order: lob+region > lob-only > region-only > global. - Given an unsupported lob or region value, then the API returns 400 with code INVALID_SCOPE and includes the list of allowed values. - Given overlapping rules of equal specificity exist in a published state, then the read API returns 409 with code RULE_CONFLICT and the conflicting rule identifiers. - Mapping responses include applied_scope fields: lob, region, and specificity_rank.

Abbreviation Expansion and Synonym Harmonization

- Given input phrase 'R/F Fender' in Auto-US scope, when normalized, then the API returns canonical_label 'Right Front Fender' and OEM part code per configured catalog, and ACORD field identifier if applicable. - Given known abbreviations (e.g., 'TPD', 'PD'), when normalized, then expansions match dictionary entries; unknown abbreviations are not expanded and return 422 UNMAPPED if no synonym match exists. - Given phrase variants with differing case, punctuation, or plurality, when normalized, then the same canonical_code is returned (case- and punctuation-insensitive matching). - Exact dictionary matches return confidence >= 0.99; synonym matches return confidence >= 0.90; results below thresholds return 422 LOW_CONFIDENCE with top 3 alternatives including their confidence scores.

Admin UI CRUD, Conflict Detection, and Rule Testing

- Given an admin with the 'Dictionary Editor' role, when creating a term, then required fields are enforced: canonical_code, canonical_label, lob[], region[], effective_start, synonyms[], abbreviations[]. - Given a proposed term duplicates canonical_code within an overlapping scope/effective window, when saving, then the UI blocks publish, surfaces conflict details (conflicting rule ids, scopes, dates), and saves the record as Draft until resolved. - Given a rule is edited, when the admin clicks 'Test', then the test harness returns normalization results for provided sample phrases within 500 ms P95 and shows source_rule_id and applied_scope. - All create/update/delete operations write audit logs including actor, timestamp, before/after values; audit logs are filterable and exportable to CSV.

Bulk Import/Export with Validation and Transactional Apply

- Given a valid CSV or JSON file with required columns and schema version, when imported in Dry Run mode, then the system produces a report of row-level actions (create/update/delete), conflicts, and schema errors without persisting any changes. - Given Apply mode, when any row fails validation or unresolved conflicts exist, then no changes are committed (atomicity) and the job status is Failed with a downloadable error file containing row numbers and error codes. - Given a 100k-row valid import, then the job completes within 5 minutes, increments the dictionary version once, and returns a job_id for status polling and a summary of actions taken. - Export endpoint returns a file containing active and future-dated terms filtered by scope, includes checksum and schema_version, and completes within 60 seconds for up to 1M records.

Read API Contract, Performance, and Caching

- Given GET /v1/normalize?phrase=...&lob=...&region=...&at=..., then the API returns 200 with fields: canonical_code, canonical_label, applied_scope, version_id, effective_start, effective_end, confidence, source_rule_id. - Given batch POST /v1/normalize with up to 500 items, then per-item P95 latency <= 150 ms and aggregate P95 <= 300 ms; P99 error rate < 0.1% during a 15-minute load test at 1000 RPS. - Responses include Cache-Control: max-age=60 and ETag; when the dictionary is republished, the ETag changes and conditional GETs with If-None-Match return 304. - API enforces rate limiting at 1000 requests/min/client; exceeding the limit returns 429 with Retry-After and no more than 1% of legitimate traffic is throttled during normal operation.

Cross-Channel Consistency Within ClaimFlow

- Given the same input phrase appears in transcription, web forms, and messaging for the same claim context (lob, region, timestamp), then the normalized canonical_code and canonical_label are identical across channels. - Given a new dictionary version is published, when clients have cached values, then all channels reflect the new normalization within 60 seconds of publish. - Given normalization confidence is below threshold, then all channels display the raw phrase, tag the field as Unmapped, and create a review task with a consistent task type and payload. - Analytics events from each channel include canonical_code, version_id, confidence, and applied_scope for 100% of normalized events; missing fields trigger a schema validation error in telemetry pipelines.

Real-time Transcription Normalization

"As an adjuster, I want abbreviations and slang auto-expanded and standardized while I’m capturing a claim so that I don’t need to retype or clean data later."

Description

Normalize phrases as audio, chat, and email are ingested, expanding abbreviations and mapping terms to canonical field values in real time. Annotate tokens with both original and normalized values and surface inline confirmations when user-facing. Provide structured outputs (code, label, system) to ClaimFlow’s workflow engine so downstream steps operate on clean data without manual intervention. Must perform within latency budgets (<150ms per phrase) and support offline retry for intermittent connections.

Acceptance Criteria

Audio Intake: Real-time Abbreviation Expansion and Mapping

Given a streaming audio phrase contains abbreviations, jargon, or slang When the phrase is transcribed and normalized Then normalization completes within 150 ms per phrase at P95 and within 250 ms at P99 And abbreviations are expanded to full terms in the transcript view And terms are mapped to canonical values with fields {code, label, system} And the normalized value, original text, and confidence score are stored together And no inline confirmation is shown when confidence >= 0.85 And all mappings conform to configured vocabularies (ACORD fields, cause-of-loss, part names)

Chat Intake: Inline Confirmation for Low-Confidence Normalizations

Given a user-facing chat session and a term normalizes with confidence < 0.85 When the message is displayed Then an inline confirmation UI is rendered within 150 ms of message render And up to 3 candidate normalizations are shown with {code, label, system} and confidence And the user can accept or override in 2 clicks or fewer And the selected normalization replaces the pending value and is persisted with original text and user ID/timestamp And declining a suggestion leaves the original text unnormalized and logs the event And subsequent workflow steps consume the selected normalization without manual mapping

Email Ingestion: Structured Output to Workflow Engine

Given an email intake event containing loss details When the email is parsed and phrases normalized Then for each normalized field the payload to the workflow engine includes {code, label, system, originalText, confidence, sourceChannel=email} And the payload validates against the Normalization Output schema (no validation errors) And all mapped fields reference a known vocabulary version ID And delivery to the workflow engine is acknowledged; on failure a retry is scheduled And downstream workflow executes without unmapped-field errors

Token Annotation and Char Offsets for Auditability

Given any normalized phrase from audio, chat, or email When stored Then tokens are annotated with original text, normalized value, startOffset, endOffset, and confidence And 100% of normalized tokens have non-null offsets aligned to the source transcript/message And an API endpoint returns annotations filterable by channel, sessionId, and time range And audit logs include user actions on confirmations (accept/override/decline) with timestamps

Performance Under Load: Latency and Error Rate

Given 100 concurrent intake sessions producing 10 phrases/second each When normalization runs for 10 minutes Then P95 latency per phrase < 150 ms and P99 < 250 ms And error rate (5xx or failed normalizations) <= 0.1% And CPU and memory utilization remain below configured thresholds (CPU < 75%, memory < 75%) And no backlog exceeds 1 second of phrases per session

Offline Retry, Ordering, and Idempotency

Given network connectivity is intermittently unavailable during intake When normalization requests fail to deliver to the workflow engine Then requests are queued locally and retried with exponential backoff up to 7 attempts And upon reconnection, queued phrases are delivered in original order within each session And duplicate deliveries are prevented using idempotency keys/correlationIds And after final failure, the item is dead-lettered and observable via monitoring

Regional Slang and Synonym Harmonization

Given the tenant region is configured (e.g., en-US or en-GB) When phrases contain regional slang or synonyms (e.g., "bonnet" vs "hood", "wing" vs "fender") Then mappings resolve to the correct canonical values for the configured region And per-tenant overrides take precedence over global mappings And mapping changes can be hot-reloaded without downtime and take effect within 60 seconds And if a regional mapping is missing, the system falls back to the default mapping and logs a warning

Context-aware Disambiguation Engine

"As a claims manager, I want ambiguous phrases resolved using claim context so that normalized values are accurate without increasing manual reviews."

Description

Use claim context (loss type, LOB, coverage, geography, vehicle/property attributes, stage) to resolve ambiguous terms and abbreviations. Combine rule-based heuristics with an NLP classifier to select the most probable canonical code, with configurable fallbacks. Provide tunable confidence thresholds, explainability metadata (top features, rules hit), and evaluation metrics (precision/recall by field, confusion matrices) accessible to admins. Integrates seamlessly with the dictionary and real-time pipeline.

Acceptance Criteria

Auto PD: Disambiguate 'totaled' to canonical code using context and threshold

Given claim context LOB=Auto, loss_type=Collision, geography=US-Midwest, stage=Intake, and message "the car is totaled" And the per-LOB confidence threshold for Auto is 0.85 When the disambiguation engine processes the message Then it selects the canonical code configured for 'Total Loss' in the dictionary for Auto LOB And the selected label probability is >= 0.85 And the processing time for this message is <= 75 ms at p95 measured over at least 1,000 similar events And the decision record includes model_version, dictionary_version, triggered_rules, top_5_features, and decision_probability

Property: Expand 'HVAC' and map to part code using property attributes

Given claim context LOB=Property, loss_type=Water Damage, property_type=Single Family, and note "HVAC flooded" And the dictionary contains a canonical part code for 'HVAC System' When the engine processes the note with the provided context attributes Then it expands 'HVAC' to 'Heating, Ventilation and Air Conditioning' And maps to the canonical part code for 'HVAC System' in the dictionary And emits explainability metadata listing used_context_features (e.g., property_type, loss_type, geography), and any rules_hit And processing time is <= 75 ms p95 over at least 1,000 events

Low-confidence fallback routes to Review queue with candidates

Given a configured per-LOB confidence threshold of 0.80 for Auto And an ambiguous term in an Auto claim where the top candidate probability is 0.62 When the engine evaluates the term Then the engine must not write a canonical code to downstream systems And it routes the item to the 'Needs Review' queue with the top 3 candidates and their probabilities And the event is tagged with fallback_reason='below_threshold' and is auditable by claim_id and event_id And SLA to enqueue is <= 200 ms end-to-end

Admin API/UI exposes explainability and decision details

Given an admin with role 'Claims Config Admin' When the admin requests decision details by claim_id and event_id via API or UI Then the response includes: model_version, dictionary_version, rules_hit (ids and names), top_5_features (name and weight), candidate_ranking (label, code, probability), and final_decision And the response time is <= 500 ms p95 And access is logged with user_id, timestamp, and purpose for audit

Metrics dashboard shows precision/recall and confusion matrices by field

Given a curated validation dataset with >= 2,000 annotated ambiguous mentions per target field and LOB When the admin opens the Evaluation tab and selects Field='Cause of Loss', LOB='Auto', Region='US' Then the dashboard displays precision, recall, F1 for the current model, and a confusion matrix for the selected slice And precision >= 0.92 and recall >= 0.90 on the selected slice And metrics are recomputed nightly and reflect data from the last 24 hours And the admin can export the metrics and confusion matrix as CSV

Real-time pipeline integration and resilience

Given the engine is running in the real-time intake pipeline When processing a sustained 95th percentile load of 50 events/sec Then the added latency from disambiguation is <= 75 ms p95 and <= 150 ms p99 measured over 10,000 events And on dictionary service unavailability, the engine trips a circuit breaker within 5 seconds And it falls back to local cached dictionary and rule-only disambiguation, emitting warning telemetry And all failures and fallbacks are exposed via observable metrics (rate, error, latency) using OpenTelemetry/Prometheus semantics

Configurable thresholds, rules, and fallbacks per LOB and region

Given an admin updates the Auto LOB threshold from 0.80 to 0.88 for Region='US-Midwest' and toggles rule 'Prefer OEM part names' ON When the admin saves the configuration Then a new configuration version is created with id, author, change_summary, and rollback link And changes propagate to all engine instances in <= 60 seconds And subsequent decisions reflect the new threshold and rule state And the previous version can be rolled back in <= 60 seconds, with all changes captured in the audit log

Standardized Output Mapping & API Integration

"As a systems integrator, I want normalized terms delivered in the correct standardized fields and codes so that downstream platforms ingest data without mapping errors."

Description

Emit normalized values in structured formats expected by downstream systems and partners (e.g., ACORD XML/JSON fields, internal canonical schemas). Enforce schema validation, code-set validation, and required-field checks before handoff. Provide idempotent, versioned APIs and workflow events so ClaimFlow routes clean, consistent data to policy, billing, and analytics systems. Include comprehensive error handling and retry logic with dead-letter queues.

Acceptance Criteria

ACORD JSON/XML Emission Post-Normalization

Given a transcribed claim containing slang and abbreviations When the Phrase Normalizer and standardized output mapper generate ACORD JSON and XML payloads Then the JSON validates against the configured ACORD JSON Schema with zero errors And the XML validates against the configured ACORD XSD with zero errors And required ACORD fields (e.g., causeOfLossCode, coverageTypeCode, partNameCode, lossDate) are present and non-empty And all coded fields resolve to values within the configured ACORD/partner code sets And the JSON and XML payloads are semantically equivalent by comparing a canonicalized hash of mapped fields

Required Field and Code-Set Enforcement

Given an intake missing any required mapped field per integration configuration When attempting downstream handoff Then the handoff is blocked and no payload is delivered to downstream systems And a 422 response is returned with an error list containing fieldPath, code=REQUIRED_FIELD_MISSING, and message for each missing field Given any mapped coded field not in its configured code set When attempting downstream handoff Then a 422 response is returned with code=CODESET_VIOLATION and details for each offending field Given the emitted payload fails schema structure validation When validation runs Then a 400 response is returned with code=SCHEMA_VALIDATION and a pointer to the failing schema paths

Idempotent, Versioned Ingestion API

Given a client calls POST /v1/normalized-claims without an Idempotency-Key header When the request is processed Then a 400 response is returned with code=IDEMPOTENCY_KEY_REQUIRED Given two POST requests to /v1/normalized-claims with identical bodies and the same Idempotency-Key within 24 hours When the second request is processed Then no additional downstream side effects occur and the response returns 200 with Idempotency-Replayed: true and the original response body Given two POST requests with the same Idempotency-Key but different bodies When the second request is processed Then a 409 Conflict is returned with code=IDEMPOTENCY_KEY_BODY_MISMATCH Given requests to /v1 and /v2 endpoints When responses are compared Then each version conforms to its published OpenAPI contract and changes in /v2 do not alter the /v1 response schema or semantics

Workflow Event Publication After Successful Handoff

Given a normalized payload passes all validations and the downstream handoff is accepted When processing completes Then a ClaimNormalized.v1 event is published to the topic claimflow.normalized within 2 seconds at p95 And the event payload validates against the published event JSON Schema with zero errors And the event includes claimId, policyId, correlationId, version, checksum, and codeSetVersion fields And the event contains a dedupId so consumers can safely ignore duplicates

Structured Error Responses for Validation and System Failures

Given a client input or validation error occurs When responding to the client Then the API returns 4xx with a machine-readable error object including code, message, fieldPath (if applicable), details[], and traceId, and includes X-Correlation-ID in headers Given an unexpected server error occurs When responding to the client Then the API returns 500 with code=INTERNAL_ERROR and includes traceId and X-Correlation-ID headers And all errors are logged with level=ERROR and include correlationId and traceId for end-to-end diagnostics

Retry Policy and Dead-Letter Queue for Handoffs

Given a transient downstream failure (timeouts or 5xx) occurs during handoff When retry policy executes Then retries use exponential backoff starting at 1s, doubling up to a max backoff of 60s, for up to 7 attempts or 15 minutes total, whichever occurs first And on a successful retry, the payload is marked Delivered with no duplicate side effects to downstream systems Given retries exhaust without success When the final attempt fails Then the payload and error metadata are written to the DLQ claimflow.normalized.handoff.dlq And an alert is issued to operations with claimId and failure reason And the DLQ dashboard surfaces the item with a Reprocess action that preserves original idempotency keys

Confidence Scoring & Human Review

"As a QA reviewer, I want low-confidence normalizations routed to me with suggested mappings so that I can quickly correct them and maintain data quality."

Description

Calculate confidence scores for each normalization and route cases below configurable thresholds to a dedicated review queue. Present reviewers with the original phrase, top suggestions with scores, context signals, and code definitions for rapid triage. Support one-click accept/override, keyboard shortcuts, bulk actions, and SLA tracking. Feed accepted overrides back into the system for continuous improvement and emit workflow events for audit and reporting.

Acceptance Criteria

Deterministic Confidence Scoring Per Normalization

Given an input phrase and its context When the Phrase Normalizer generates normalized suggestions Then each suggestion includes a confidence score as a float between 0.00 and 1.00 rounded to two decimals And the highest-scoring suggestion is identified as the top suggestion And given the same input, configuration, and model version When reprocessed Then the scores and ranking are identical within ±0.01 and the same top suggestion is produced And at least the top 3 suggestions are returned when available

Configurable Thresholds and Queue Routing

Given a configured review_threshold per field type (overridable per client) When a normalization result's top score is less than the effective threshold Then the item is routed to the Human Review Queue with status "Pending Review" And when the top score is greater than or equal to the effective threshold Then the normalization is auto-accepted and bypasses the review queue And when an admin updates a threshold Then new items created after the change use the updated threshold within 5 minutes and existing routed items retain their prior decision unless manually re-evaluated And the most specific threshold (client + field) takes precedence over global settings

Reviewer Panel Displays Required Context

Given a review queue item is opened When the reviewer panel loads Then it displays the original phrase text, source artifact link (message/photo), surrounding text snippet (±30 chars), detected language, region (if available), and field type And it displays the top 5 normalized suggestions with their codes, human-readable code definitions, and confidence scores And all panel data renders within 2 seconds at the 95th percentile

One-Click Accept/Override with Keyboard Shortcuts

Given a reviewer is viewing a review item When they click Accept on a suggestion or invoke its keyboard shortcut Then the decision is saved, the item is removed from the queue, the case is updated with the normalized value, and an audit record is written (user, timestamp, chosen suggestion, prior top suggestion, scores) And when they Override by selecting a different suggestion or entering a custom code and confirming via one click or shortcut Then the override is saved with equivalent audit metadata and the case is updated accordingly And keyboard shortcuts exist for Accept, Override, and Next Item and execute without mouse interaction And after action, the next item loads within 500 ms at the 95th percentile

Bulk Review Actions with Partial Success Handling

Given a reviewer multi-selects N items (N ≤ 200) in the review queue When they choose a bulk action (e.g., Accept Top Suggestion) Then the system attempts the action on each selected item and returns a per-item success/failure summary And items that have changed state since selection are skipped with a "stale" reason and are not duplicated And the bulk operation completes within 10 seconds at the 95th percentile when N ≤ 200

SLA Tracking and Escalation for Review Queue

Given an SLA policy (e.g., 4 business hours) is configured for the review queue When an item enters the queue Then an SLA deadline is computed using the tenant business calendar/time zone and stored on the item And the UI shows remaining time countdown with states: green (>50% remaining), amber (10–50%), red (<10%) And when remaining time reaches 10% of SLA Then an escalation notification is sent to the designated channel/user And when the SLA deadline passes without decision Then the item is marked Breached, a breach timestamp is recorded, and the item appears in SLA breach reports

Feedback Loop and Event Emission on Reviewer Decisions

Given a reviewer accepts or overrides a normalization When the decision is saved Then a feedback record is persisted containing original phrase, selected code, all suggestions with scores, context signals, user id, and decision type And an idempotent workflow event "Normalization.Decision" is published within 5 seconds including tenant/client id, case id, field id, chosen value, confidence, decision type, reviewer id, and correlation id And when the same phrase and context recur for the same tenant after feedback ingestion Then the chosen mapping receives a higher confidence and is auto-accepted if it meets the current threshold, or otherwise ranks above prior alternatives And feedback records are exportable and retained for at least 365 days for training and analytics

Audit Trail & Dual-Value Storage

"As a compliance officer, I want complete provenance of normalized values so that audits and disputes can be resolved with verifiable evidence."

Description

Persist both the original phrases and their normalized outputs along with canonical codes, code system, mapping source (rule/model), model version, dictionary version, timestamp, and reviewer identity (if applicable). Ensure immutability guarantees, searchable provenance, and data lineage views. Expose audit data via APIs and reports to support compliance, dispute resolution, and analytics backtesting across ClaimFlow environments.

Acceptance Criteria

Dual-Value Persistence with Codes

Given a claim intake event produces a normalized field When the normalization completes Then an audit record is created for that field capturing: claim_id, environment, field_name, original_text, normalized_text, canonical_code, code_system, mapping_source, model_version (if mapping_source = "model"), dictionary_version (if mapping_source = "rule" or "dictionary"), timestamp_utc, reviewer_identity (null) And the normalized_text and canonical_code in the audit record exactly match the values sent to downstream systems And the audit record is retrievable via the Audit API by claim_id and field_name

Immutable Audit Log with Append-Only Corrections

Given an existing audit record id When a client attempts to update or delete the record via the Audit API Then the operation is rejected and the stored record remains unchanged And any human review adjustment is recorded as a new audit record linked by previous_version_id, with reviewer_identity and review_timestamp populated And both the original and the new record are independently retrievable and time-stamped And a tamper-evident checksum or signature for each record can be verified via the Audit API

Complete Provenance: Source, Versions, Reviewer

Given a normalization produced by a rule When the audit record is stored Then mapping_source = "rule" and rule_id and dictionary_version are populated Given a normalization produced by a model When the audit record is stored Then mapping_source = "model" and model_version is populated Given a normalization adjusted by a human reviewer When the review is submitted Then mapping_source = "manual_review" and reviewer_identity and review_timestamp are populated

Searchable Lineage and Traceability API

Given multiple versions of a normalized field over time When a client calls GET /audit?claim_id={id}&field_name={name}&sort=asc Then the response lists all audit records for that field in chronological order with previous_version_id links And filtering by mapping_source, date range, and canonical_code returns only matching records And the response includes total_count and a pagination cursor when results exceed the page size

Environment-Scoped Backtesting Export

Given a request to export audit data for environment = "prod" and a date range When a client calls the export endpoint Then the system returns a downloadable file or stream containing: claim_id, environment, field_name, original_text, normalized_text, canonical_code, code_system, mapping_source, model_version, dictionary_version, timestamp_utc, reviewer_identity, previous_version_id for each record And the export includes a manifest with record_count and a checksum that validates the file content And the export can be filtered by model_version and dictionary_version

Compliance Report: Original vs Normalized with Provenance

Given a claim_id with normalized fields When a user generates the Normalization Audit report Then the report shows, for each field, original_text, normalized_text, canonical_code, code_system, mapping_source, timestamp_utc, and reviewer_identity if present And the report can be filtered by date range, field_name, and mapping_source And the report is downloadable in CSV and PDF formats

Continuous Learning & Governance Workflow

"As a taxonomy owner, I want the system to learn from corrections under an approval workflow so that the dictionary improves continuously without risking bad mappings in production."

Description

Capture reviewer corrections and field overrides as training signals to propose new synonyms or rule adjustments. Queue proposals for admin approval with impact analysis (affected fields, collision risks, test results) before publishing. Support safe rollout via versioning, environment promotion, rollback, and A/B evaluation. Provide dashboards for coverage gaps, drift detection, and quality KPIs to guide taxonomy curation.

Acceptance Criteria

Reviewer Correction Capture & Proposal Generation

- Given a reviewer modifies a normalized value during QA, When they click Save, Then the system records a training signal containing original text, prior normalized value, corrected value, target field, claim ID, reviewer ID, and UTC timestamp within 2 seconds. - Given a reviewer marks a correction as Do Not Learn, When saved, Then the signal is excluded from model training and proposal generation and is labeled as DNL in the audit log. - Given ≥3 identical corrections across ≥3 unique claims occur within a 7‑day rolling window, When the threshold is met, Then the system auto-creates a synonym/rule proposal with a suggested mapping, confidence score, and aggregated evidence links. - Given a correction is captured, When stored, Then any configured sensitive data elements are masked per data policy in the signal payload and logs.

Impact Analysis Report Generation for Proposals

- Given a synonym/rule proposal exists, When an analyst opens the proposal, Then the system displays an impact report including: affected fields and entities, potential collisions with existing rules, estimated coverage gain %, and regression test summary (precision, recall, F1, false positive delta). - Given access to a historical corpus, When impact analysis runs, Then it simulates the proposal on a configurable sample size (default 10,000 records) and completes within 15 minutes with reproducible seed and dataset snapshot ID. - Given a collision risk score ≥ configured threshold, When the report is generated, Then the proposal status is set to Blocked by Conflict and approval action is disabled until conflicts are resolved. - Given the analysis completes, When results are persisted, Then a versioned PDF/JSON report is attached to the proposal and is downloadable via API.

Admin Approval Workflow with Audit Trail

- Given a queued proposal, When an Admin views it, Then they can Approve, Reject, or Request Changes and must enter a reason comment (min 10 characters) for any decision. - Given a decision is submitted, When processed, Then the system records approver ID, decision, timestamp, diff summary, and changes the proposal status accordingly (Approved, Rejected, Changes Requested). - Given a non-Admin attempts to approve, When action is invoked, Then the system denies the action with a 403 error and logs the attempt. - Given any decision, When stored, Then the immutable audit trail is append-only and exportable (CSV/JSON) with filter by date range, user, proposal ID.

Versioning, Environment Promotion, and Rollback

- Given an Approved proposal, When published to Dev, Then a new taxonomy version is created using semantic versioning (e.g., 1.4.0) with a signed manifest and changelog link. - Given a version in Dev, When promoting to Staging or Prod, Then promotion is gated by automated regressions achieving ≥99.0% pass rate and 0 critical collisions; otherwise promotion is blocked with actionable errors. - Given a rollback is requested in Prod, When executed, Then the system restores the previous stable version within 5 minutes, routes all new requests to that version without errors, and records the rollback in the audit log. - Given a promotion or rollback completes, When checked, Then the active version per environment is visible via UI and API and consistent across nodes within 60 seconds.

A/B Evaluation and Guardrails

- Given two taxonomy versions (control and candidate), When an A/B test is configured, Then traffic split is configurable from 1% to 99% and is sticky per claim/session for the test duration. - Given the A/B test is running, When metrics are computed, Then the system reports at 15‑minute intervals: normalization accuracy on a labeled set, reviewer correction rate, and downstream integration error rate per variant. - Given KPI degradation exceeds configured thresholds for two consecutive intervals (e.g., accuracy drop >1.0 pp or error rate increase >0.2%), When detected, Then the system auto-pauses the candidate and routes 100% of traffic to control and sends alerts to the on-call channel. - Given the test ends, When results are finalized, Then the system produces a statistically annotated summary (effect sizes, confidence intervals) and archives raw metrics for 13 months.

Drift and Coverage Monitoring with Alerts

- Given live traffic, When monitoring runs hourly, Then the system computes drift per key field using PSI and flags any field with PSI > 0.2 over a 24‑hour window. - Given normalization attempts, When coverage is calculated, Then the system reports % unmatched slang/jargon and % mapped to fallback per LOB/region and creates auto-proposals for top N gaps. - Given a drift or coverage alert is triggered, When thresholds are breached, Then notifications are sent to configured channels (email/Slack/Webhook) with links to impacted samples and suggested actions. - Given monitoring configurations, When updated, Then changes take effect within 10 minutes and are versioned with rollback capability.

Quality and Governance Dashboard

- Given a user with Viewer role, When opening the dashboard, Then they can see KPIs (proposal counts by status, median approval time, coverage gap %, drift indicators, accuracy trend) with read-only access. - Given data refresh schedule, When checked, Then all widgets update at least hourly and display last refresh timestamp; historical views retain 13 months of data. - Given an Admin user, When using the dashboard, Then they can filter by environment, LOB, region, carrier, and export any widget’s underlying data to CSV within selected date ranges. - Given access control, When permissions change, Then dashboard visibility updates within 5 minutes and is enforced at API level.

Smart Pathing

Adaptive micro-forms that ask only what’s missing. Questions adjust in real time based on claim type, jurisdiction, and what’s already on file, auto-skipping verified items and inserting only the required follow-ups. Cuts claimant effort, boosts completion rates, and raises first‑pass completeness while auto-updating downstream workflow steps per answer.

Requirements

Real-time Adaptive Questioning

"As a claimant, I want the form to only ask me what’s truly needed based on my situation so that I can finish quickly without repeating or irrelevant questions."

Description

Builds and renders micro-forms on the fly based on claim type, jurisdiction, and data already on file, showing only the next necessary question. Automatically skips verified fields, inserts conditional follow-ups when answers introduce new requirements, and suppresses questions marked not applicable. Maintains answer state across steps, supports "unknown" responses with deferred follow-ups, and prevents contradictory inputs through validation and mutually exclusive choices. Ensures sub-200 ms next-question latency with client-side caching and prefetch of likely branches, and gracefully degrades to a linear path when rules cannot be evaluated. Exposes a configuration layer for product ops to author decision trees without code, and logs a decision trace for each question rendered for auditability and debugging.

Acceptance Criteria

Context-driven next question rendering

Given claim type "Auto" and jurisdiction "CA" and on-file verified fields [policyNumber, claimantName] When the micro-form session loads or the user submits any answer Then only questions required by the "Auto-CA" rules and not already answered appear And questions marked Not Applicable by rules are never rendered in UI or API payload And the next question is the highest-priority unanswered required question per rules And the question order respects declared dependencies

Auto-skip of verified fields

Given a field is present with status "Verified" in the claim record When the flow reaches the step where that field would be asked Then the question is skipped without rendering And the field is marked as completed with source "VerifiedOnFile" And the skip is reflected in progress indicators And the audit trail records the skip reason and source identifier

Immediate conditional follow-ups

Given answer "Injury involved?" = Yes When the answer is submitted Then follow-up questions [injuryType, numberOfInjured, treatmentSought] are inserted immediately after the current step And if "Injury involved?" changes to No, those follow-ups are removed and any captured answers are invalidated with reason "No longer applicable" And jurisdictional rule "NY-PhotoRequired" adds "Upload injury photos" only when jurisdiction = NY And follow-ups appear within 200 ms of submission at p95

'Unknown' responses with deferred follow-ups

Given a question supports an "Unknown" option When the user selects "Unknown" and submits Then the response is saved with value "Unknown" and timestamp and actor And a deferred follow-up task is created with due date per configuration and assigned owner And the flow continues to the next required question without blocking And the claim completeness indicator shows "Pending Info" with count incremented And when the deferred info is later provided, the task auto-closes and dependent workflow steps re-evaluate

Contradiction prevention and mutual exclusivity

Given mutually exclusive choices exist for "Vehicle condition" [Total Loss, No Damage, Minor Damage] When the user selects one option Then any conflicting options are automatically deselected And on submit with a conflict attempt, an inline validation error appears referencing rule code and resolution And the API rejects conflicting payloads with HTTP 422 and error code "RULE_CONFLICT"

Performance and graceful degradation

Given normal network conditions and a warmed client cache When the user submits an answer Then the next-question API response time is <= 200 ms at p95 and <= 300 ms at p99 over 5,000 interactions And the client renders the next question within 16 ms of receipt And the client prefetches the top 2 likely branches; prefetch hit rate >= 70% in test cohort And if the rules service times out or is unavailable, the UI switches to a linear fallback within 500 ms, displays a non-blocking notice, and continues collecting all required questions And after recovery, the flow resumes adaptive mode without re-asking already collected answers

Config authoring and decision trace logging

Given a Product Ops user with "DecisionTree.Editor" permission When they author or edit a decision tree in the configuration UI Then they can define conditions on claimType, jurisdiction, field presence, dependencies, mutually exclusive groups, and follow-up inserts without code And cyclic dependencies and unreachable nodes are prevented by validation with actionable errors And changes are versioned as drafts, previewable via a simulated micro-form, and publishable with a change log and rollback option And each rendered question emits a decision trace including rule version, inputs considered, selected branch, latency, timestamp, and session/claim identifiers And the trace is queryable per claim and exportable; PII fields are masked according to policy

Auto Pre-Fill & Evidence Verification

"As a claims manager, I want key fields pre-filled and clearly verifiable so that I reduce manual entry and trust the accuracy of the captured data."

Description

Pre-populates micro-form fields using existing claim data, policy metadata, prior messages, and extracted facts from documents and photos via the NLP engine. Displays provenance and confidence for each pre-filled value, requires confirmation when confidence is below configurable thresholds, and locks fields once verified. Allows users to view linked evidence snippets (e.g., image crop, message quote) before confirming, and supports one-tap acceptance of all high-confidence items. Prevents overwriting of user edits by subsequent data syncs, records verifier identity and timestamp, and emits verification events for downstream systems. Provides admin controls for source priority, field-level confidence thresholds, and fallback behaviors when sources disagree.

Acceptance Criteria

Pre-Fill With Provenance Display

Given a claim has existing claim data, policy metadata, prior messages, and NLP-extracted facts When the Smart Pathing micro-form loads for that claim Then fields are pre-populated according to the configured source priority And each pre-filled field displays its selected source label and a confidence score as a percentage to two decimal places And if multiple sources exist, the UI indicates the selected source and provides a View Evidence link And fields with no available sources remain empty and are flagged as Needs input

Confidence Threshold Enforcement

Given field-level confidence thresholds are configured by an admin And a field is pre-filled with a confidence below its configured threshold When the user attempts to proceed past the section or submit Then the system blocks progression and highlights the field for confirmation or correction And if a field’s confidence is at or above its threshold, the field does not require mandatory confirmation And changing a field’s value clears any prior verification status and requires re-verification

Evidence Snippet Preview

Given a pre-filled field has linked evidence When the user clicks View Evidence Then a preview opens within 500 ms showing the exact snippet (image crop or message quote) with the matched value highlighted And the user can Accept, Reject, or Close from the preview And Accept marks the field Verified; Reject clears the pre-fill and flags the field as Needs input And the system records which evidence item(s) were reviewed for the decision

One-Tap Accept High-Confidence Items

Given there are one or more pre-filled fields at or above their configured confidence thresholds and not user-edited When the user selects Accept All High-Confidence Then only those qualifying fields are marked Verified in a single action And a summary shows the count of accepted fields and any fields skipped with reasons And below-threshold or user-edited fields remain unverified and highlighted for review And a single audit entry records the bulk verification action with the list of field keys

Locking and Sync Overwrite Protection

Given a field has been Verified or manually edited by a user When a subsequent data sync provides a different value for the same field Then the existing value remains unchanged and is not overwritten by the sync And the incoming value is stored as a non-destructive suggestion with source and confidence, visible via Review updates And Verified fields are visibly locked in the UI and cannot be edited by default And any attempt to change a locked field is prevented and logged

Verifier Identity and Timestamp Audit

Given a field is Verified via individual confirmation or bulk acceptance When the verification is saved Then the system records the verifier’s user ID and an ISO 8601 timestamp And the field UI displays Verified by <user> at <timestamp> And the claim’s audit log includes the field key, value, source, confidence, verifier ID, and timestamp And exporting the claim includes verifier identity and timestamp for each Verified field

Downstream Verification Events

Given downstream integrations are configured (webhook or message bus) When any field transitions to Verified or is Rejected following evidence review Then the system emits an event within 2 seconds containing claimId, fieldKey, value, status, source, confidence, verifierId (if applicable), timestamp, and actionType And events are delivered over the configured channel and must receive a 2xx response or be retried up to 5 times with exponential backoff And failed deliveries are surfaced to admins with error details and current retry status

Jurisdictional & Line-of-Business Rules Library

"As a compliance lead, I want jurisdiction-specific requirements enforced and traceable so that every intake meets regulatory standards without manual checklists."

Description

Centralizes required fields, conditional questions, and validation rules per jurisdiction and line of business with effective-dating and versioning. Supports carrier- and program-level overrides, multi-tenant isolation, and rule provenance (citation, author, change notes). Provides a rule evaluation service used by the Smart Pathing engine to decide which questions to ask, with deterministic outcomes and performance budgets. Includes a no-code rule editor with preview, test cases, and staging environments, plus a publish workflow requiring review and approval. Maintains an audit trail of rule changes and backfills affected active sessions when a critical rule update occurs, prompting re-collection only where needed.

Acceptance Criteria

Deterministic Version Resolution by Jurisdiction, LOB, and Effective Date

Given a claim with jurisdiction=CA-ON, LOB=Auto, and asOf=2025-03-15T10:00:00Z When the rules are evaluated Then the version whose effectiveStart <= asOf < effectiveEnd is applied and the evaluation trace includes that versionId Given identical inputs (jurisdiction, LOB, asOf, claim facts) repeated 100 times When evaluated Then the question set, order, and evaluation trace hash are identical across all runs Given asOf exactly equal to effectiveEnd When evaluated Then the next version (if any) is applied per [start inclusive, end exclusive] boundaries Given no matching version for jurisdiction and LOB When evaluated Then the outcome code is NO_RULES_MATCH and the question list is empty

Carrier and Program Overrides Precedence

Given a base rule requires field `policeReportNumber`, a carrier override marks it optional, and a program override requires field `selfie` When evaluated for that carrier and program at an asOf where both overrides are effective Then `policeReportNumber` is optional and `selfie` is required in the resulting question set Given both carrier and program overrides with different effective windows When evaluated at an asOf within only one window Then only overrides effective at asOf are applied and others are ignored Given conflicting settings across layers for the same field When evaluated Then precedence is program > carrier > base, and the evaluation trace enumerates the applied layers in order

Multi-Tenant Isolation

Given Tenant A and Tenant B When a user from Tenant A queries rules or evaluates with tenantContext=A Then no rules, metadata, or traces from Tenant B are accessible; cross-tenant attempts are denied with an authorization error Given an evaluation request without tenantContext When evaluated Then the request is rejected with a validation error and no default tenant is assumed Given a full export of Tenant A rules and metadata When the export is generated Then only Tenant A data is present and record counts match in-tenant queries

No-Code Rule Editor with Preview and Test Cases

Given a rule author with Editor role When they create or edit a rule in the UI Then client- and server-side validation prevents saving with schema or logic errors and inline errors display within 200 ms of field blur Given a saved draft in Staging When Preview is run against a sample claim input Then the simulator shows the exact question set, required/optional flags, and skip logic that the evaluation service returns for those inputs Given one or more failing test cases attached to the draft When Publish is attempted Then publish is blocked and failing test cases are listed with expected vs actual deltas Given provenance fields citation, author, and changeNotes When the draft is saved Then these fields are required and persisted with the draft

Publish Workflow with Review, Approval, and Audit Trail

Given a draft ruleset in Staging When Publish is initiated Then a maker-checker workflow requires at least two distinct Approvers to approve before production activation Given all linked test cases pass and required approvals are recorded When the final approval is submitted Then the ruleset moves to Production, versionNumber increments, and the change is immutable Given any publish or rollback event When the audit log is queried Then it contains timestamp, actorId, diff of changes, provenance metadata, and approval chain; entries are read-only and exportable as CSV Given a rollback to prior version V When activated Then V becomes current for evaluations within 60 seconds

Critical Update Backfills Active Sessions

Given a rule update is marked Critical When published Then the system identifies all active Smart Pathing sessions impacted within 5 minutes and records their sessionIds for backfill Given impacted sessions When users resume their sessions Then only newly required fields or fields with changed validations are requested; previously answered unchanged items are not re-collected Given backfilled sessions are re-evaluated When downstream workflow steps are recalculated Then new tasks are inserted only where answers changed and obsolete tasks are canceled with reasons Given the backfill job completes When the summary report is generated Then it shows counts of sessions identified, notified, re-collected, and errors with error rate < 0.5%

Evaluation Service Performance and Idempotency

Given a normal load of 500 RPS with realistic payloads When the evaluation service runs for 10 minutes Then p95 latency per evaluate call <= 50 ms and p99 <= 150 ms, with error rate < 0.1% Given a burst load of 2000 RPS for 60 seconds When backpressure engages Then the service sheds load with 429s only and recovers to baseline p95 latency within 2 minutes after the burst Given identical inputs and the same ruleset version When evaluated repeatedly Then the response body and evaluation trace hash are identical (idempotent) across runs, and a deterministic cache key is emitted Given tracing is enabled for a request When the response is returned Then it includes a traceId and the list of applied ruleIds and provenanceIds for each decision

Answer-Driven Workflow Updates

"As an adjuster, I want the workflow to update automatically as the claimant answers questions so that I don’t have to manually create or re-sequence tasks."

Description

Maps answers and verification states to downstream workflow actions, automatically creating, updating, or skipping tasks in ClaimFlow based on responses. Publishes structured events (e.g., injury_reported=true) to the workflow engine, ensures idempotency, and replays updates when answers change to keep tasks in sync. Supports SLAs, assignees, and due dates derived from answers (e.g., injury within 24 hours triggers a statutory notice task) and provides a trace that links each task to the originating answers and rules. Allows adjustable severity and fallback behaviors when mappings are incomplete, and sends notifications to adjusters when critical path changes occur.

Acceptance Criteria

Create statutory notice task with SLA from injury answer

Given a claim with claimType='WorkersComp', jurisdiction='CA', injuryReported=true, and injuryReportedAt is set When the answers are saved by Smart Pathing Then the system creates a task name='Statutory Notice' with status='Open' And sets assigneeRole='Compliance' And sets dueAt = injuryReportedAt + 24h And sets priority='High' and sla='24h' And stores task.source.type='rule' and task.source.ruleId is not null

Idempotent task creation and update on repeated processing

Given a task name='Statutory Notice' already exists for claimId C sourced from ruleId R When the same answers are processed again (no effective change) Then no additional 'Statutory Notice' task is created And the existing task id remains unchanged And no task fields are modified When a derived field changes due to non-functional metadata (e.g., clock drift) but answers are identical Then the system preserves the existing task and recalculates only permissible fields (e.g., dueAt) without creating duplicates

Replay updates when answers change

Given injuryReported=false and no statutory notice task exists When injuryReported changes to true and answers are saved Then create 'Statutory Notice' with dueAt derived per rule and status='Open' And publish event injury_reported=true for the claim Given injuryReported=true and the task exists When injuryReported changes to false and answers are saved Then cancel the 'Statutory Notice' task with reason='Rule no longer applicable' And publish event injury_reported=false for the claim And the task trace shows both transitions with timestamps

Structured events emitted to workflow engine

Given any rule-to-workflow mapping fires due to an answer being created, updated, or cleared When the event is emitted Then the payload includes eventName, claimId, ruleId, answerPath, answerValue, occurredAt, and idempotencyKey And the event is published to the workflow engine topic/queue And the idempotencyKey remains stable for identical state so duplicate events are ignored by subscribers

End-to-end traceability from task to originating answers and rules

Given a task created or updated by an answer-driven mapping When the task is retrieved via API or viewed in UI Then the task detail includes: source.ruleId, source.ruleVersion, evaluationTimestamp, derivedFields {assigneeRole, dueAt, sla, priority}, and sourceAnswers[] with path and value And a 'View Rule Trace' action reveals the rule and the exact answers that triggered it

Fallback behavior and severity when mappings are incomplete

Given a required mapping is missing for an answer and severity='High' When the answer is saved Then create a 'Manual Review' task with dueAt=now+4h and priority='High' And notify the assigned adjuster via email and in-app notification within 1 minute And record a RULE_MISSING warning in the claim log and trace the fallback path Given the same condition with severity='Low' When the answer is saved Then do not create a task And record a non-blocking log entry and continue processing

Jurisdiction-based routing adjusts tasks and SLAs

Given jurisdiction='CA' and injuryReported=true with injuryReportedAt set When answers are saved Then create task name='State Notice - CA' with assigneeRole='Compliance-CA' and dueAt=injuryReportedAt+24h Given jurisdiction='TX' and injuryReported=true with injuryReportedAt set When answers are saved Then create task name='State Notice - TX' with assigneeRole='Compliance-TX' and dueAt=injuryReportedAt+48h

Save-and-Resume Across Devices

"As a claimant, I want to pause and resume my claim on another device so that I can complete it when it’s convenient without losing progress."

Description

Enables claimants to pause intake and continue on any device via secure magic links or authenticated sessions. Autosaves progress after each answer, tolerates network loss with local caching and queued sync, and resolves conflicts with last-write wins plus a review of changed fields. Supports configurable session expiry, redaction of sensitive fields in emails/SMS, and encryption at rest and in transit. Preserves decision context so that upon resume the next question remains correct even if rules have been updated, with options to prompt for re-validation only when necessary.

Acceptance Criteria

Autosave and Cross-Device Resume

Given a claimant is completing a Smart Pathing micro-form and is online When they submit an answer or 2 seconds elapse after typing stops Then the current state is persisted server-side within 1 second of the event and a resume token is updated Given the claimant opens a valid magic link on a different device within its configured TTL When the link is accessed Then the intake resumes at the next unanswered step with all prior answers pre-populated and zero data loss Given the claimant is authenticated via an existing portal session When they navigate back to the in-progress claim Then the session resumes without a magic link and at the same progression step

Offline Caching and Queued Sync

Given connectivity is lost mid-intake When the claimant continues answering Then each response is cached locally encrypted and an offline indicator is shown within 500 ms Given connectivity is restored When the app detects network availability Then queued responses are synced to the server within 5 seconds in original answer order and the offline indicator clears Given the app remains offline When 72 hours pass Then the cached progress persists and is available to resume without data loss

Conflict Resolution with Review

Given the same field is changed on two devices while one was offline When both devices sync Then the value with the latest server-received timestamp is stored (last-write-wins) and the field is flagged as "needs review" Given a field is flagged as "needs review" When the claimant resumes Then they see a review panel listing the conflicting fields with prior and current values, timestamps, and device labels Given the claimant chooses to revert a change When they confirm the selection Then the chosen value is saved, the review flag is cleared, and an audit entry records user, timestamp, old and new values

Configurable Session and Link Expiry

Given an administrator sets session expiry to 7 days and magic link TTL to 24 hours When a magic link older than 24 hours is opened Then access is denied, a secure re-auth prompt is shown, and upon successful auth the saved progress is restored Given no activity occurs for 7 days When the claimant next attempts to resume Then they must re-authenticate and continue from the last saved step without data loss Given a magic link is revoked by the user or admin When it is used Then access is denied and no PII is disclosed

Sensitive Data Redaction in Messages

Given an email or SMS containing a magic link is generated When the message includes references to answers or fields marked sensitive Then those values are redacted (masked except last 2 characters) and no raw sensitive values appear in the message body, subject, or URL Given message logs or templates are stored When they are viewed by support staff Then sensitive values remain redacted and cannot be reconstructed from stored content Given a magic link is generated When inspected Then it contains a short-lived opaque token and no inline PII or claim data

Encryption at Rest, In Transit, and Local Cache

Given data is transmitted between client and server When a connection is established Then TLS 1.2+ with strong ciphers is enforced and HTTP is redirected to HTTPS with HSTS enabled Given claimant data is stored server-side When persisted Then it is encrypted at rest using AES-256 or stronger with KMS-managed keys and documented rotation at least annually Given data is cached locally for offline use When written to device storage Then it is encrypted using the platform keystore and is unreadable when the device is locked

Decision Context Preservation and Re-Validation

Given Smart Pathing rules may change between pause and resume When a claimant resumes an in-progress intake Then the next question is computed using the rules snapshot version captured at the time of the last saved step and the rules version used is recorded in the audit log Given updated rules invalidate specific previously answered fields When the claimant resumes Then only the affected fields are marked for re-validation with clear prompts, and unaffected answers remain accepted without re-entry Given re-validation occurs When the claimant completes required confirmations Then the decision context is updated, downstream workflow steps are re-synced, and the next question remains correct and consistent

WCAG AA Accessibility & Localization

"As a claimant with accessibility needs, I want the adaptive form to work with assistive technologies and in my language so that I can complete my claim independently."

Description

Delivers Smart Pathing experiences compliant with WCAG 2.2 AA, including full keyboard navigation, screen reader labels, focus management, color contrast, and accessible error messaging. Localizes all question text, help content, and validation messages with support for RTL scripts and locale-specific formats (addresses, dates, currency, units). Provides admin tooling for translation management, fallbacks when a translation is missing, and runtime locale switching without breaking decision logic. Ensures performance and accessibility parity across mobile and desktop, and includes automated and manual accessibility testing in CI/CD.

Acceptance Criteria

Keyboard Navigation & Focus Management for Dynamic Smart Pathing

Given a user completing Smart Pathing using only a keyboard, When questions are revealed, skipped, or re-ordered based on answers, Then focus moves in a logical visual order with no keyboard traps and lands on the first interactive control of newly revealed sections. Given any modal, tooltip, or popover is opened from the form, When it is closed via Esc or action buttons, Then focus returns to the triggering control. Given any interactive element receives focus, Then a visible focus indicator is present with at least 3:1 contrast against adjacent colors and a minimum 2px outline or equivalent area, and the element remains fully visible in the viewport. Rule: All functionality is operable via keyboard (Tab, Shift+Tab, Arrow keys, Enter, Space, Esc) without requiring specific timings. Rule: Text contrast is ≥ 4.5:1 and non-text/interactive component contrast is ≥ 3:1.

Screen Reader Semantics and Live Announcements

Given Smart Pathing loads, Then landmarks and headings are structured (one H1; H2/H3 reflect hierarchy), and every control has an accessible name, role, and state via native HTML or ARIA. Given a question is dynamically inserted, updated, or removed, When a screen reader is running, Then changes are announced via aria-live="polite" without interrupting current speech. Given any label, When focus is on its input, Then the relationship is programmatic (label for/id or aria-labelledby) and the input’s purpose is unambiguous. Rule: Decorative icons are aria-hidden; meaningful images have alt text; reading order matches visual order.

Accessible Validation and Error Messaging

Given the user submits with missing or invalid inputs, When validation runs, Then focus moves to the first invalid field and an error summary appears at the top listing errors as links that move focus to each field. Rule: Each invalid field has inline error text programmatically associated via aria-describedby and does not rely on color alone; error text contrast is ≥ 4.5:1. Rule: Inputs present required/format instructions before entry (not placeholder-only), and errors are announced by screen readers when they appear. Rule: Errors are cleared and messages updated immediately upon correction without requiring a full page reload.

Localization, RTL Support, and Translation Management

Given a supported locale is selected (e.g., en-US, es-ES, fr-CA, ar-SA), Then all question text, help content, controls, system messages, and option values are localized with correct pluralization and variable interpolation. Given an RTL locale, Then layout, text alignment, iconography, and step progression mirror; html dir and lang attributes update; focus order remains logical. Given a translation key is missing, Then the UI uses the configured fallback chain (locale → language → default) without placeholder strings, and missing keys are logged for admins. Given an admin with translation permissions, When they upload/edit translations, Then the tool validates keys, flags missing/unused keys, maintains an audit trail, and supports export/import without breaking runtime.

Locale-Specific Formats and Validation Rules

Given a user enters dates, numbers, currency, addresses, and units, Then input masks, placeholders, grouping/decimal separators, and validation rules adapt to the active locale and country. Rule: User-entered values are stored in canonical formats (ISO 8601 dates, ISO 4217 currency codes with minor units, normalized numbers, standardized country-specific address schemas) independent of locale. Rule: Displayed values reflect the active locale; parsing and formatting are round-trippable without data loss. Rule: Country selection dynamically adjusts address fields and validation (e.g., postal code length/patterns).

Runtime Locale Switching Without Logic Regression

Given a user switches locale mid-session, Then all visible strings and formats update within 500 ms without a full page reload, focus is preserved, and current answers remain unchanged. Rule: Decision logic and workflow branching operate on canonical values and do not re-trigger questions or alter routing due to locale changes. Rule: Screen readers are informed of the language change (html lang updates; change is announced via a polite live region).

Performance, Mobile/Desktop Parity, and CI/CD Accessibility Gates

Rule: On mid-range mobile (simulated 4x CPU throttle, 4G), Smart Pathing’s initial view meets LCP ≤ 2.5 s, TTI ≤ 3.5 s, and interaction latency p95 ≤ 100 ms; desktop meets equal or better thresholds. Rule: Automated checks in CI/CD must pass with Lighthouse Accessibility ≥ 95, axe-core 0 critical/serious, and pa11y 0 errors; failing checks block merge. Rule: Manual assistive tech testing per release passes on JAWS + Chrome, NVDA + Firefox (Windows), VoiceOver (iOS Safari), and TalkBack (Android Chrome) for keyboard-only navigation, screen reader flow, and error handling. Rule: Accessibility features and behavior are functionally equivalent across mobile and desktop; any exceptions are documented with accessible alternatives.

Path Analytics & Experimentation

"As a product manager, I want visibility into where users drop off and the ability to test improvements so that I can continuously raise completion and first-pass accuracy."

Description

Captures granular funnel metrics (completion rate, time to complete, drop-off by question, first-pass completeness) and emits privacy-safe event streams for analysis. Provides dashboards segmented by claim type, jurisdiction, channel, and device to identify friction points and measure the impact of Smart Pathing. Supports controlled experiments on question order, wording, and UI affordances with guardrails to prevent non-compliant variants in regulated jurisdictions. Integrates with feature flags for gradual rollout, records experiment assignment in audit logs, and exposes APIs for exporting insights to BI tools.

Acceptance Criteria

Privacy-Safe Event Instrumentation

- Given a user interacts with Smart Pathing via web, mobile, or SMS, When a trackable action occurs (screen_view, question_shown, answer_submitted, validation_error, step_completed, session_ended), Then an analytics event is emitted with fields: event_name, timestamp_utc_ms, claim_id (opaque UUIDv4), session_id, user_role, jurisdiction_code, claim_type, channel, device_type, question_id (when applicable), variant_id (when applicable), schema_version, and event_id (UUID) for idempotency. - Given PII is present in the UI, When emitting analytics events, Then no raw PII values are included; sensitive fields are either omitted or irreversibly hashed with rotating salt, and a pii=false flag is set on all analytics payloads. - Given consent is required by jurisdiction, When consent is absent or revoked, Then analytics events are not sent (or only consent_change and aggregate counters are sent), and consent_state is captured without PII. - Given connectivity issues occur, When events cannot be delivered, Then events are locally queued encrypted-at-rest, retried with exponential backoff, and dropped after 72h; duplicates are prevented at the collector via event_id idempotency. - Given jurisdictional restrictions exist, When a prohibited field is configured for an event, Then the guardrail removes the field and rejects the release if removal would break schema, with a compliance test asserting the mapping per jurisdiction.

Funnel Metrics Capture and Segmentation

- Given Smart Pathing sessions occur, When events stream in, Then the system computes and stores per-day metrics: completion_rate, median_time_to_complete, p90_time_to_complete, dropoff_rate_by_question_id, and first_pass_completeness_rate (defined as zero follow-up tasks generated post-submission) using UTC event time. - Given a user applies filters for claim_type, jurisdiction, channel, and device_type, When viewing metrics, Then results reflect only the selected segment and update within 3 seconds of filter change. - Given new events arrive, When metrics are refreshed, Then end-to-end data freshness (event to dashboard) is ≤ 15 minutes and the dashboard displays last_processed_at. - Given a question_id is part of the flow, When calculating drop-off, Then drop-off is defined as percentage of sessions that reach question_shown and do not submit answer_submitted for that question before session_end or timeout.

Analytics Dashboards and Drilldowns

- Given an analytics user with analytics_viewer role opens the Path Analytics dashboard, When loaded, Then the default view shows last 7 days, overall metrics (completion_rate, time_to_complete, first_pass_completeness), and the top 5 questions by drop-off with counts and percentages. - Given a user clicks a specific question in the dashboard, When drilldown is requested, Then the view shows step-level conversion, pre/post step timing, and breakdown by device_type and jurisdiction with sortable columns. - Given an export is initiated from the current view, When CSV export is requested, Then a file is generated within 60 seconds containing the applied filters, column headers per data dictionary v1.2, values in UTC, and row counts matching the on-screen totals. - Given RBAC is enforced, When a user without analytics_viewer attempts access, Then access is denied and an access_denied event is logged without exposing data.

Controlled Experiments with Jurisdictional Guardrails

- Given an experiment is configured to test question order, wording, or UI affordances, When targeting rules are saved, Then eligible sessions are assigned deterministically using a stable hash (claim_id + session_id) and assignments are mutually exclusive across variants. - Given regulated jurisdictions have non-negotiable wording/order, When a variant violates a jurisdictional compliance rule, Then publishing is blocked with a specific validation error and no traffic is served to the non-compliant variant. - Given a session is first exposed to an experiment, When the first exposure occurs, Then an exposure event is recorded with experiment_id, variant_id, timestamp, targeting_snapshot, and is appended to an immutable audit log within 1 minute. - Given multiple experiments target overlapping surfaces, When conflicts are detected, Then the system either enforces predefined layering/holdouts or blocks activation with a conflict error prior to start.

Feature Flags and Gradual Rollout

- Given a feature flag gates Smart Pathing changes, When rollout is set to X% globally or per segment, Then observed traffic share per segment matches X within ±1 percentage point once n ≥ 10,000 sessions, and a kill switch disables the flag within 5 minutes when toggled off. - Given a user returns within 30 days, When the flag remains active and targeting unchanged, Then the user receives the same treatment (sticky bucketing) and the evaluation is logged. - Given an incident requires rollback, When the flag is set to 0%, Then all associated experiments pause automatically and an annotation appears on dashboards at the rollback timestamp.

Audit Logging and Compliance Traceability

- Given any experiment assignment or feature flag evaluation occurs, When a decision is made, Then an audit record is written with actor/service, subject (claim_id/session_id), inputs (targeting attributes), decision (on/off, experiment_id, variant_id), policy_version, and timestamp, retained for ≥ 2 years in a tamper-evident (hash-chained) store. - Given a compliance export is requested, When the export job completes, Then a signed JSONL file is produced with page size 10,000, SHA-256 checksum, delivered to a secure bucket, and both request and completion are logged with correlation_id.

Insights Export APIs for BI Tools

- Given an API client with analytics_export scope and valid OAuth2 credentials, When calling GET /api/v1/analytics/funnels with segment filters and date range, Then the API responds with HTTP 200 within 5s p95 and returns paginated results (limit, cursor) including metrics, segment keys, and last_processed_at. - Given experiment results are queried, When calling GET /api/v1/experiments/{id}/results, Then the response includes variant metrics, sample sizes, 95% confidence intervals, p-values, and an underpowered flag if minimum sample size is not met; schema is versioned. - Given rate limiting is enforced, When a client exceeds 600 requests/minute, Then the API returns 429 with Retry-After; TLS 1.2+ is required and all responses include a data dictionary version header.

Live Proofcheck

Real-time validation as claimants upload and enter data. Detects document type, page count, blur/glare, and date/name mismatches via OCR; confirms legibility and completeness; and provides instant, plain-language fix tips or auto-recapture prompts. Prevents back‑and‑forth, reduces manual QA, and delivers ingestion‑ready artifacts on the first try.

Requirements

Real-time Document Type & Page Detection

"As a claimant, I want the system to recognize the document I’m uploading and whether all pages are included so that I don’t submit the wrong file or miss pages."

Description

Classifies uploaded content as specific insurance document types (e.g., driver’s license, repair estimate, invoice, police report) and counts pages in real time during upload or capture. Detects duplicates and missing pages for multi-page documents, surfaces immediate alerts, and blocks submission until required pages are captured. Integrates with web and mobile capture flows, streaming frames to a lightweight classifier for instant feedback. Emits structured metadata (doc_type, page_count, is_duplicate) for downstream routing and fix-tip generation. Expected outcome is a reduction in wrong-file submissions and incomplete document sets, improving first-pass acceptance rates and cutting rework.

Acceptance Criteria

Web capture: real-time doc type classification and latency

Given a supported document set {driver_license, repair_estimate, invoice, police_report} and a valid image or PDF input When a claimant uploads a file or live-captures frames via web Then the system returns doc_type with top-1 confidence >= 0.90 for >= 95% of validation samples and latency <= 300 ms p95 per page/frame And When confidence < 0.60 Then doc_type = "unknown" and the document does not count toward required-document completion And Then the UI label for doc_type updates within 300 ms after each new page/frame is received

Multi-page detection and submission blocking

Given doc_type is detected and configured with min_pages for that type When a multi-page document is uploaded or sequentially captured Then page_count equals the actual number of pages with accuracy >= 99% on the validation set And When page_count < configured min_pages Then an immediate alert displays required vs. captured counts and submission is blocked until page_count >= min_pages And When page headers or footers indicate numbering (e.g., "Page X of Y") Then gaps or Y > captured are identified and the missing pages are listed

Duplicate document detection and alerting

Given a new upload or capture within the same claim session When its perceptual hash is within a Hamming distance <= 4 of an existing artifact or its content hash matches exactly Then is_duplicate = true and duplicate_of is set to the original artifact_id And Then an alert "Duplicate detected" is shown and required-document progress does not advance And The duplicate true positive rate is >= 98% and the false positive rate is <= 1% on the validation set

Mobile frame-stream classifier performance

Given the mobile capture SDK streaming camera frames to the lightweight classifier When the camera is pointed at a document Then on-device inference returns doc_type and provisional page detection feedback within 150 ms p95 on reference devices and maintains >= 15 FPS visual feedback And When on-device inference is unavailable Then the client falls back to edge inference with end-to-end latency <= 300 ms p95 And No more than 5% of frames are dropped due to inference

Metadata emission for downstream routing

Given any upload or capture event When detection completes Then a metadata payload is emitted with fields {doc_type:string, doc_type_confidence:number[0..1], page_count:int>=1, is_duplicate:boolean, artifact_id:string} And Then the payload is published to the "doc_detected" stream and available to downstream consumers within 100 ms p95 And Field names and types conform to schema version v1; unknown fields are ignored by consumers without runtime errors

Submission gating and completeness outcomes

Given the claimant is at the submission step When required documents per workflow configuration are incomplete or any required doc_type has page_count < min_pages Then the Submit action is disabled and a list of blocking items is displayed And When all blocking conditions are resolved Then the Submit action becomes enabled within 100 ms and proceeds without document-completeness errors And Over a 2-week A/B test window, first-pass acceptance rate for document completeness improves by >= 30% relative to pre-feature baseline

OCR Field Extraction & Mismatch Detection

"As a claims manager, I want uploaded documents to be checked for name, date, and policy mismatches against the claim so that errors are caught before intake."

Description

Performs OCR on uploaded documents to extract key fields (claimant name, policy number, dates of loss/service, VIN, invoice totals) and cross-checks them against the active claim context. Calculates confidence scores and flags mismatches or missing values with field-level highlights. Supports multiple languages and common insurance document formats, and normalizes outputs to standard schemas. Provides structured mismatch events to drive real-time guidance and prevents submission when critical inconsistencies are detected. Expected outcome is fewer data-entry errors, reduced manual QA, faster approvals, and lower follow-up volume.

Acceptance Criteria

OCR Extraction Accuracy and Normalization

Given a gold-standard test set of 200 insurance documents (invoices, estimates, proof-of-loss, police reports, IDs) across EN/ES/FR with clean scans (300 DPI, <=5% skew) When OCR runs on upload Then the system extracts claimant_name with F1 >= 0.97, policy_number exact-match accuracy >= 0.98, date_of_loss/service exact-match accuracy >= 0.96, VIN exact-match accuracy >= 0.98, invoice_total absolute error <= $0.01 in 95% of cases And dates are normalized to ISO 8601 (YYYY-MM-DD) and amounts to cents with currency code per ISO 4217 And VINs are normalized to 17 uppercase alphanumerics with invalid characters removed And policy numbers are normalized by stripping spaces and hyphens and uppercasing

Cross-Check and Mismatch Detection Against Claim Context

Given an active claim context containing claimant_name, policy_number, and date_of_loss And an uploaded document with extracted fields and per-field confidence scores When cross-checking runs Then exact mismatches on policy_number (after normalization) are flagged as Critical with reason=POLICY_MISMATCH And claimant_name is matched using case-insensitive, punctuation-insensitive comparison with Levenshtein distance threshold <= 2; distances > 2 are flagged as Critical with reason=NAME_MISMATCH And date_of_loss differences > 3 calendar days are flagged as Critical with reason=DATE_MISMATCH; differences <= 3 days are flagged as Warning And missing required fields are flagged as Critical with reason=MISSING_FIELD And all flags include field, extracted_value, expected_value, confidence, and normalized_value

Field-Level Highlighting in Document Viewer

Given a document preview is displayed in the Live Proofcheck UI And OCR produced bounding boxes for each extracted field When a field is flagged as mismatch or missing Then the corresponding region is highlighted in red overlay with 60% opacity and tooltip showing reason and values And overlay bounding boxes align with the text within ±5 pixels or ±1% of page width/height, whichever is greater, in 95% of cases And non-flagged extracted fields are highlighted in green on hover

Structured Mismatch Event Emission

Given any Critical or Warning flag is generated during cross-check When the flag is created Then an event with type=ocr.mismatch.v1 is published to the event bus within 500 ms of flag creation And the payload conforms to the JSON Schema contract including claim_id, document_id, field, severity, reason, extracted_value, expected_value, confidence, normalized_value, bbox, locale, and timestamp (RFC 3339) And events are idempotent by flag_id and deduplicated within a 2-minute window And 99.5th percentile end-to-end publish latency <= 1500 ms

Submission Blocking on Critical Inconsistencies

Given Critical flags exist for any of policy_number, claimant_name, or date_of_loss with confidence >= 0.8 When the user attempts to submit the document set Then the Submit action is disabled and an inline error list identifies each blocking issue with fix tips And submission becomes enabled immediately after all Critical flags are resolved or downgraded below threshold And backend submission endpoints reject attempts with unresolved Critical flags with HTTP 409 and machine-readable error codes

Multilingual and Document Format Coverage

Given documents in English, Spanish, and French across the following formats: invoice, repair estimate, proof-of-loss form, police report, government ID When OCR and classification run Then language detection accuracy >= 0.97 and field extraction F1 >= 0.92 per language on the gold set And document type classification accuracy >= 0.95 macro-average and page count detection accuracy >= 0.995 And numeric, date, and name fields are correctly localized during parsing (e.g., day-month order, thousand/decimal separators) and normalized to the standard schema

Confidence Scoring Calibration and Thresholding

Given per-field confidence scores in [0,1] When evaluated against the gold set Then scores are calibrated such that predicted probability bins are within ±0.05 of observed accuracy (ECE <= 0.05) And fields with confidence < 0.80 are labeled LowConfidence and trigger guidance prompts without blocking unless also mismatched or missing And confidence thresholds are configurable per field with default=0.80 and persisted in system settings

Image Quality Scoring with Live Retake Prompts

"As a claimant, I want live prompts to retake blurry or glary photos so that my documents are legible on the first try."

Description

Evaluates image frames and uploads for blur, glare, skew, shadows, resolution, and crop completeness in real time. Provides immediate visual and textual prompts to retake or adjust angle/lighting, and supports auto-capture when the view is steady and quality thresholds are met. Includes edge detection for page boundary guidance and automatic deskew/crop where permissible. Works offline for mobile capture with graceful degradation to server-side checks on web. Expected outcome is legible, ingestion-ready images on first capture, reducing retry loops and abandonment.

Acceptance Criteria

Mobile Real-Time Quality Scoring and Prompting

Given a claimant has opened the in-app camera, When each preview frame is processed, Then the system computes scores for blur, glare, shadow, skew, resolution, and crop completeness within 120 ms per frame on Tier-1 devices, And if any score is below threshold (blur<70/100, glare area>5% of page, shadow area>10% of page, pre-correction skew>20° or post-correction skew>1°, crop completeness<98% page included, shorter side<1200 px or longer side<1600 px), a visible on-screen tip and icon are shown within 200 ms indicating the primary issue (e.g., "Reduce glare", "Hold steady", "Move closer"), And if all thresholds are met, a "Looks good" indicator is shown within 200 ms, And the decision and per-metric scores are available in debug logs and capture metadata for QA verification.

Auto-Capture Trigger on Steady High-Quality Frame

Given the preview feed is active, When 8 consecutive frames over ≥1.0 s meet all quality thresholds and device motion is low (gyro delta <0.02 rad/s and translation <2 cm/s), Then the app auto-captures without user tap and provides a 200 ms pre-capture visual ring animation and haptic feedback, And if quality drops below thresholds before the trigger window completes, auto-capture is canceled and a "Hold steady" prompt appears, And if the user manually taps capture while below thresholds, the image is not accepted; a blocking banner lists the top failing metric(s) with fix tips and a one-tap "Retake" option.

Edge Detection with Live Guides and Auto Deskew/Crop

Given a rectangular document is in view, When edges are detectable, Then a live quadrilateral overlay snaps to the page within 50 ms and updates at ≥15 fps, And on capture, auto-crop and deskew produce an output with residual rotation ≤1° and no content loss (all page corners present with ≥2% margin), And if edges are not confidently detected for ≥500 ms, auto-capture is disabled and a "Align document inside frame" prompt appears, And when organization policy disables auto-crop, the app preserves the original image and still displays alignment guides and retake prompts.

Offline Mobile Operation with Graceful Degradation

Given the device is in airplane mode on mobile, When the user opens the capture flow, Then on-device quality scoring, prompts, edge detection, and auto-capture function without any network access, And no network requests are attempted until the session ends, And if advanced ML models are unavailable locally, the system degrades to baseline checks (resolution, exposure, edge presence) and displays a non-blocking "Limited checks offline" notice while allowing capture and retake prompts.

Web Capture Fallback to Server-Side Quality Checks

Given a claimant uses a desktop browser without supported real-time processing (e.g., no WebGL/WASM or camera permissions withheld), When the user uploads or captures an image, Then the client uploads the frame and receives server-side quality evaluation within 2 s of upload completion, And the UI presents the same issue-specific prompts (e.g., glare, blur) and a one-click "Retake/Upload new" action, And if the server response exceeds 2 s or fails, the UI shows a retry option and does not block the user from re-uploading.

Performance and Responsiveness Targets

Given reference devices (e.g., iPhone 12, Pixel 6, mid-tier Android 2021) and average indoor lighting, When the capture view starts, Then time-to-first-prompt is ≤300 ms and preview frame rate remains ≥24 fps during active scoring, And CPU utilization attributable to the module averages ≤60% on big cores with no thermal throttling over a 2-minute session, And memory usage increase stays ≤250 MB peak over baseline, And UI interactions (tap shutter, open/close tips) respond within 100 ms.

Accessible, Plain-Language Prompts and Feedback

Given any prompt is shown to the claimant, When assistive technologies are enabled (VoiceOver/TalkBack), Then all actionable controls and prompts are screen-reader accessible with descriptive labels and focus order logical, And prompt text is at or below 8th-grade reading level and localized for en-US and es-US, And visual indicators meet WCAG 2.1 AA contrast (≥4.5:1) and include non-color cues; haptic feedback accompanies auto-capture and blocking errors.

Dynamic Completeness Checklist & Rule Engine

"As an intake specialist, I want a dynamic checklist of required items based on claim type so that submissions arrive complete without follow-ups."

Description

Generates a real-time, claim-type–aware checklist of required documents and fields (e.g., ID, proof of loss, estimates, receipts) based on line of business, jurisdiction, coverage, and claim stage. Applies configurable business rules and thresholds to determine what is mandatory versus optional and blocks submission until mandatory items meet quality and match criteria. Provides admin controls to author rules, set quality confidence thresholds, and map requirements to claim attributes. Expected outcome is first-submission completeness, fewer follow-ups, and faster routing into automated workflows.

Acceptance Criteria

Real-time checklist generation by claim attributes

Given a claim with defined line of business, jurisdiction, coverage, and stage When the claim is opened or any of those attributes change Then a checklist is generated or updated within 1 second reflecting requirements mapped to the current attributes And each item is labeled Mandatory or Optional per the evaluated rules And irrelevant items are excluded from the checklist And the checklist shows a version and timestamp of evaluation

Mandatory vs optional determination and rule precedence

Given multiple rules apply to the same requirement When rules are evaluated Then precedence is applied as: Explicit Override > Jurisdiction > Line of Business > Default And the most specific scope wins in ties (e.g., coverage+stage over coverage only) And the final determination of Mandatory or Optional is stored with the rule IDs that contributed to the decision

Submission block until mandatory items meet quality and match criteria

Given a claimant attempts to submit the intake When any Mandatory requirement is incomplete, fails document-type match, has date/name mismatch, or is below quality thresholds Then submission is blocked And the blocking message lists each unmet requirement with reason and fix-tip And submission becomes enabled immediately after all Mandatory items meet their criteria without page reload

Admin authoring of rules, thresholds, and mappings

Given an authenticated admin with proper permissions When the admin creates or edits a rule that maps requirements to claim attributes and sets quality confidence thresholds Then the rule is validated for syntax and conflicts before save And the rule is versioned with author, timestamp, and effective date And a preview shows impacted requirements for a sample claim profile before publishing And the admin can rollback to a prior version

Document quality gating for requirement satisfaction

Given a document is uploaded for a checklist item When OCR detects document type and page count, and QC computes blur/glare scores Then the item is marked Satisfied only if the detected type matches the expected type, page count is within configured bounds, and quality scores meet or exceed thresholds And otherwise the item remains Unsatisfied and the user receives specific auto-recapture or fix guidance

Real-time recomputation on stage or coverage changes

Given the claim stage or coverage changes during intake When the change is saved Then the checklist is re-evaluated within 1 second And newly required items are added and highlighted And items no longer required are moved to Not Required and do not block submission And a change log records added, removed, and status-changed items

Transparent audit trail of rule evaluation

Given a user views Why required? for a checklist item When the decision details are expanded Then the system displays the evaluated rules (IDs and names), input attributes, thresholds applied, evaluation timestamp, and final decision And the details can be exported as JSON for audit purposes

Plain-Language Fix Tips & Localization

"As a claimant, I want plain, localized instructions that tell me exactly how to fix issues so that I can complete my submission quickly."

Description

Delivers contextual, plain-language guidance to resolve detected issues (e.g., “Your photo is too blurry; move closer and hold steady”) with step-by-step microcopy, icons, and short animations. Localizes tips into supported languages with region-specific terminology and ensures accessibility compliance (screen readers, high-contrast, large text). Personalizes tone and brevity to claimant context while maintaining regulatory-safe phrasing. Expected outcome is clearer self-service remediation, reduced abandonment, and higher first-pass success across demographics.

Acceptance Criteria

Blurry Image Tip and Auto-Recapture

- Given the camera preview blur score exceeds the blur threshold for 2 consecutive frames, When the claimant taps Capture or uploads a photo, Then a plain-language tip with 2–4 steps and an icon is displayed within 500 ms and an option to Retake is presented. - Given the Retake flow is active, When the blur score meets the acceptance threshold for at least 500 ms, Then auto-recapture triggers and the tip auto-dismisses. - Given the tip is displayed, When measured for readability, Then the microcopy reads at Grade 6 or below and is ≤180 characters for the primary message and fully visible on a 360×640 viewport without scrolling. - Given the claimant does not improve image quality after 2 attempts, When the third capture fails, Then the tip expands to show step-by-step guidance with a short looped animation (<3 s) demonstrating correct technique and a link to “Learn more.”

Document Type/Name/Date Mismatch Guidance

- Given OCR detects the uploaded document type is not among the expected types for the current task OR detects a name/date mismatch with the claimant profile, When the file is uploaded, Then a tip appears within 700 ms enumerating each specific issue and highlighting the conflicting value(s). - Given any blocking issue remains, When the claimant attempts to submit, Then the Submit action is disabled and an accessible explanation is announced; When all issues are resolved, Then Submit becomes enabled within 200 ms. - Given a date mismatch is detected, When showing guidance, Then the valid date range and correct field label for the claimant’s region are shown; If multiple issues exist, Then they are ordered by severity (blocking before advisory).

Spanish (US) Localization of Tips

- Given the device/browser locale is es-US or the user selects Español (EE. UU.), When any fix tip is displayed, Then all tip text, button labels, alt text, and captions render in Spanish using region-approved terminology per the glossary with no mixed-language fragments. - Given a translation key is missing for es-US, When the tip would render, Then the system falls back to English, exposes a visible language switcher, and logs a missing_translation event with key and locale. - Given Spanish is active, When viewed at 200% zoom on a 360×640 viewport, Then no text truncates or overlaps and numbers/dates are formatted per es-US conventions; readability is at Grade 6 equivalent in Spanish.

Screen Reader and Live Region Support

- Given a screen reader is active, When a new tip appears, Then its content is announced once via aria-live="polite" within 1 s and focus order remains consistent without trapping. - Given an icon conveys information in a tip, When read by assistive tech, Then it has a meaningful accessible name; decorative icons are aria-hidden. - Given a tip includes an animation, When Reduce Motion is enabled, Then a static frame replaces the animation and an accessible description is provided; otherwise, a Pause control is available and focusable. - Given keyboard-only navigation, When traversing tips, Then all interactive elements are reachable via Tab/Shift+Tab and operable via Enter/Space.

High-Contrast and Large Text Accessibility

- Given system high-contrast or in-app high-contrast mode is on, When a tip is shown, Then text meets WCAG 2.1 AA contrast (≥4.5:1), UI components/focus indicators meet ≥3:1, and link states are visually distinct. - Given the user sets text size to 200%, When viewing any tip, Then layout remains usable without truncation/overlap, critical actions remain visible without scrolling, and touch targets are ≥44×44 px. - Given dark mode is active, When tips render, Then colors maintain required contrast and do not introduce color-only cues for meaning.

Personalized Tone and Brevity Rules

- Given claimant context flags (e.g., first-time user, prior friction events ≥2, small-screen device), When generating a tip, Then the system selects a tone variant (concise, friendly, step-by-step) from a rules table and logs variant_id and rule_version. - Given the concise variant is selected, When the tip renders, Then the primary message is ≤120 characters; Given the step-by-step variant is selected, Then 2–4 numbered steps with icons are shown. - Given any variant, When passing through the readability check, Then the text is Grade 6 or below and avoids jargon per the plain-language glossary.

Regulatory-Safe Phrasing and Audit Trail

- Given a tip is generated, When evaluated by the compliance lexicon, Then prohibited phrases (e.g., guarantees, absolutes, liability-shifting) are absent; violations block rendering and trigger a lexicon_block event with offending tokens. - Given a tip is shown to a claimant, When the interaction completes, Then an audit entry is stored with timestamp, locale, variant_id, rule_version, and a SHA-256 hash of the tip text; entries are retained ≥7 years and exportable via API. - Given the claimant’s jurisdiction requires specific phrasing (e.g., CA, EU), When the tip renders, Then jurisdictional substitutions/disclaimers are applied per active rule set and recorded in the audit entry.

Ingestion-Ready Output Packaging & Workflow Handoff

"As an operations lead, I want validated documents and extracted data to be standardized and auto-routed into workflows so that manual QA and data entry are eliminated."

Description

Normalizes validated artifacts into standardized outputs: consolidated PDFs with correct page order, original media, and a structured JSON payload containing extracted fields, quality scores, and validation results. Attaches system tags (doc_type, claimant_id, claim_id, completeness_status) and pushes artifacts to ClaimFlow via internal events, APIs, or webhooks with idempotency and deduplication. Ensures downstream steps can auto-route without human QA. Expected outcome is immediate handoff into configurable workflows, eliminating manual data entry and reducing cycle time.

Acceptance Criteria

Consolidated PDF Assembly with Correct Page Order

Given a validated intake with one or more documents and images When packaging is executed Then a single consolidated PDF is produced containing all included pages And page order preserves original per-document sequence; multi-page documents remain contiguous And the PDF total page count equals the sum of included source pages And the PDF opens in standard viewers and passes integrity checks (no corruption) And identical inputs produce an identical PDF checksum to support idempotency

Structured JSON Payload with Extracted Fields, Quality Scores, and Validation Results

Given packaging completes successfully Then a structured JSON payload is generated and includes: schema_version, package_id, claim_id, claimant_id, completeness_status, artifacts[], extracted_fields[], quality_scores, validation_results And the JSON validates against the defined schema with zero errors And every extracted field includes: name, value, confidence (0.0–1.0), source {doc_id, page_number} And quality_scores provide per-page and per-field scores And validation_results list each rule with {rule_id, status, details} And every PDF page index is mapped to its source artifact(s) in the JSON

Original Media Preservation and Linkage to Outputs

Given packaging is executed Then all original media files are stored without modification and their SHA-256 checksums are recorded And JSON includes stable URIs/IDs and checksums for each original media file And each PDF page has a traceable linkage to its originating media (one-to-one or one-to-many as applicable) And if a recapture/auto-recapture occurred, both original and recaptured assets are retained and linked with role tags {original|recaptured}

System Tagging for Routing (doc_type, claimant_id, claim_id, completeness_status)

Given packaging is executed Then the consolidated PDF, original media records, and JSON payload are tagged with doc_type, claimant_id, claim_id, and completeness_status And tag values conform to controlled vocabularies and format rules (e.g., claim_id UUID/enterprise ID format) And completeness_status is set to Complete only when all required documents for the claim type are present and validated, else Incomplete And tags are present both in transport metadata and within the JSON payload

Idempotent Delivery and Cross-Transport Deduplication

Given delivery is attempted via internal event, API, or webhook and an idempotency_key is provided When the same payload is delivered again with the same idempotency_key Then the system responds with HTTP 200 (or equivalent ACK) indicating duplicate and does not create new artifacts And cross-transport duplicates are detected using package_id and content checksums within a defined time window, preventing multiple packages for the same input And concurrent duplicate deliveries result in exactly one committed package; others are de-duplicated And delivery receipts include {package_id, delivery_status, dedup=true|false} And retry logic is safe to repeat without creating duplicates

Immediate Workflow Handoff and Auto-Routing without Human QA

Given a package is successfully delivered to ClaimFlow When the routing service processes the delivery event/API/webhook Then the configured workflow starts automatically without any manual QA gate And the next workflow step is enqueued within p95 < 3s and p99 < 10s of delivery receipt And downstream steps can fetch artifacts by package_id/claim_id and receive completeness_status and tags for routing And if routing fails, the system performs automated retries and emits an alert without requiring repackaging

Validation Audit Trail & Evidence Export

"As a compliance officer, I want an audit trail of validation outcomes and what users saw so that we can evidence decisions and meet regulatory requirements."

Description

Captures an immutable log of validation checks, model versions, confidence scores, prompts shown, user actions (retakes, overrides), timestamps, and outcomes per artifact. Supports secure, role-based access, PII minimization, and retention policies aligned with regulatory requirements. Provides export-ready summaries for adjusters and compliance reviews, including why a document passed or failed and what corrections were made. Expected outcome is full traceability for internal review and external audits, reducing compliance risk and dispute resolution time.

Acceptance Criteria

Immutable Audit Log for Validation Lifecycle

Given a claimant uploads an artifact and Live Proofcheck runs validations When each validation check completes Then an append-only event is recorded containing artifact_id, validation_id, validation_type, rule_id_or_model_version, confidence_score, input_checksum, result (pass|fail), outcome_details, and timestamp in UTC ISO8601 with millisecond precision Given a client, API, or admin attempts to modify or delete an existing event When the operation is executed Then the change is rejected with 405/409, the attempt is logged as a security event, and the original event remains unchanged Given an integrity verification request for an artifact’s audit log When the system computes the hash chain/Merkle proof over the event sequence Then verification passes for untampered logs and fails for any alteration, returning the index of the first mismatched link Given transient storage or service outages during event writes When service recovers Then pending events are written exactly once using idempotency keys and original order is preserved per artifact

Comprehensive Capture of User Actions and Prompts

Given a user performs a retake, override, dismissal, or correction during Live Proofcheck When the action is submitted Then an event is recorded with user_id, user_role, action_type, targeted_field, previous_value_hash, new_value_hash (if applicable), reason_code (required for overrides), client_ip, and timestamp Given a guidance prompt or auto-recapture prompt is shown to a user When the prompt is displayed and acted upon Then prompt_id, prompt_text_hash, language, guidance_category, user_action (accepted|ignored|snoozed), and resulting outcome are recorded Given an override action missing a reason_code When the user submits the override Then the system blocks the action and returns a validation error requiring justification, and no state change occurs Given multiple sequential user actions on the same artifact When events are queried Then their order is strictly preserved and reconstructs the full decision history

Role-Based Access Controls for Audit Trail and Exports

Given an adjuster assigned to a claim When they request the audit trail summary for that claim Then access is granted and only claim-scoped data is returned with PII masked per policy Given a compliance officer with Compliance_Audit role When they request the full audit log for any claim Then access is granted; all access is logged with actor, purpose, and timestamp Given a user without Export_Audit permission When they attempt to generate or download an audit export Then the request is denied with 403 and no file is created Given any successful export download When the file is generated Then it is watermarked with requester_id, request_time, and claim_id and the download event is recorded

PII Minimization and Redaction in Logs and Exports

Given audit events contain PII fields (name, SSN, DOB, phone, address, email) When events are persisted Then values are tokenized or stored as hashes with format-preserving masking (e.g., SSN last4 only), and cleartext is excluded from the audit store Given an export is generated for a region with an active privacy policy (e.g., GDPR, CCPA) When the export content is prepared Then PII fields are redacted per policy defaults unless the requester has Compliance_Audit role and explicitly selects unredacted mode; the selection is logged Given a configuration change to PII masking rules When the new policy is activated Then subsequent events and exports apply the new rules, and the policy version is included in export metadata

Retention Policy and Legal Hold Enforcement

Given a retention policy of 7 years for LOB=Auto in region=US When an artifact’s retention end date is reached Then its audit events and generated exports are purged within 30 days, and a purge receipt (counts by object type, timestamps) is recorded Given a legal hold is applied to a claim When the retention end date elapses Then no purge occurs until the hold is lifted; hold_applied and hold_released events are recorded with actor and justification Given backups contain audit data When purge executes Then corresponding backup entries are deleted within 30 days and the deletion job produces a verifiable report with job_id and affected ranges

Export-Ready Compliance Summary with Evidence

Given an artifact with completed validations and user actions When an adjuster or compliance officer requests a compliance summary Then the system generates both JSON and PDF within 5 seconds at the 95th percentile including: pass/fail reasons, triggered rules, model_versions, confidence_scores, user overrides with reasons, prompts shown, timestamps, final disposition, and integrity checksum Given a claim with up to 50 artifacts When a single consolidated export is requested Then the file size is <= 10 MB; if exceeded, the export is chunked into sequential parts with an index manifest Given an export is generated When validation occurs Then the package is signed with the platform signing key, includes a signature and public key reference, and signature verification succeeds

Quick Prefill

One-tap confirmation of prefilled details pulled from policy and prior intake. Securely ties the SMS link to the claim, auto-populates names, policy numbers, loss dates, vehicle/property info, and relevant contact blocks so the claimant only verifies or edits. Slashes typing, lowers error rates, and speeds submissions.

Requirements

Secure SMS Link-to-Claim Binding

"As a claimant, I want to open a secure link that already knows my claim so that I can quickly verify details without re-entering my information or risking exposure of my data."

Description

Implement a cryptographically signed, single-use, time-bound token embedded in outbound SMS links that deterministically binds the recipient session to the correct claim. On open, the token authorizes read-only access to prefill context (policyholder, policy, loss, risk objects, contacts) with least-privilege scopes and optional device binding. Include replay protection, rate limiting, and automatic token rotation, with OTP fallback if the link is opened on an unrecognized device. Ensure end-to-end transport security, server-side verification, and detailed audit logging. On token verification failure, gracefully route to manual intake without exposing claim data. Integrates with ClaimFlow notifications and identity layers to ensure secure, frictionless entry into Quick Prefill.

Acceptance Criteria

Signed, Time-Bound Token Generation and SMS Link Embedding with Rotation

- Given a claim notification is triggered by ClaimFlow, when the outbound SMS link is generated, then the link contains exactly one cryptographically signed token parameter and no other identifiers (no claim ID, no PII). - Given token issuance, when the token is created, then it includes only token_id, claim_ref, scopes, iat, exp, nonce, and optional device_binding claims, with exp set to a configurable TTL between 5–60 minutes (default 15 minutes). - Given server-side verification, when the token signature or payload is tampered with, then verification fails and no claim context is returned. - Given a resend is requested, when a new token is issued, then all prior tokens for the same claim-recipient are revoked within 5 seconds and become unusable. - Given a token is opened within 2 minutes of expiry, when verification succeeds, then the server rotates to a fresh token transparently and continues the session without user action. - Given the notifications layer, when an SMS is dispatched, then the shortlink domain is from an approved allowlist and redirects preserve HTTPS only.

Single-Use Enforcement and Replay Protection

- Given a valid token is used to start a session, when a subsequent request reuses the same token, then the server responds 401 or 409 with a generic message and offers a safe path to request a new link, without revealing claim existence. - Given any attempt to use a consumed, revoked, or expired token, when verification occurs, then no prefill data is returned and the token state remains unchanged from consumed/expired. - Given network retries, when the same token_id is presented concurrently within 2 seconds from the same device, then exactly one session is allowed and others are rejected idempotently. - Given a token is intercepted and used from a different device or IP before the intended recipient, when verification occurs, then the token is rejected and flagged as suspected replay. - Given replay detection is triggered, when logging occurs, then an audit event with reason=replay_detected is written and alerts are sent to security telemetry within 60 seconds.

Deterministic Claim Binding with Least-Privilege Prefill Access

- Given a token is validated, when the session is established, then the bound claim context matches the token's claim_ref deterministically and cannot be switched by client input. - Given the Quick Prefill flow, when data is fetched, then only read-only endpoints with scope=prefill.read are accessible; any write or non-prefill endpoints return 403. - Given scope enforcement, when prefill data is returned, then only fields required for Quick Prefill are included (policyholder name, policy number, loss date, risk descriptor, contact channels) and no financials, notes, or adjuster data are present. - Given cross-claim attempts, when a user tries to access a different claim_ref, then access is denied and the original session claim_ref remains unchanged. - Given successful binding, when the UI renders, then prefilled fields match the claim data exactly and pass checksum/ETag validation.

Device Recognition with OTP Fallback for Unrecognized Devices

- Given device binding is enabled, when the link is opened on a previously recognized device, then the session is established without OTP and within 2 seconds of server verification. - Given the link is opened on an unrecognized device, when verification succeeds, then the user is prompted for a 6-digit OTP delivered via SMS to the verified number. - Given an OTP is issued, when the user enters the correct code within 5 minutes, then access is granted; after 3 failed attempts or expiry, the flow routes to manual intake. - Given OTP delivery, when sends are requested, then a maximum of 5 OTP sends per hour per claim-recipient is enforced with exponential backoff. - Given OTP validation, when mismatched phone numbers are detected, then no OTP is sent and the user is routed to manual intake without disclosing claim existence.

Token Validation Rate Limiting and Anomaly Controls

- Given the token validation endpoint, when requests exceed 30 per minute per IP or 5 per minute per token_id, then subsequent requests receive 429 with no indication of token validity. - Given burst traffic from multiple IPs against the same token_id, when thresholds are exceeded (100 attempts in 5 minutes), then the token is auto-revoked and a security alert is generated. - Given WAF integration, when known malicious patterns are detected in requests, then the request is blocked before application processing and logged. - Given rate limiting triggers, when a legitimate user subsequently accesses with a new token, then access is restored without persistent blocks. - Given configuration, when limits are updated, then changes take effect without downtime and are tracked in audit logs.

Transport Security and Server-Side Verification Safeguards

- Given an SMS link is opened, when the client connects, then TLS 1.2+ is enforced with HSTS enabled; HTTP access is disallowed and not auto-upgraded silently. - Given URL construction, when the link is generated, then no PII or claim identifiers appear in the URL path or query other than the opaque token; server and proxy logs redact the token value. - Given verification, when the token is processed, then all checks (signature, exp, revocation, scope) occur server-side; the client never decodes or inspects the token. - Given cryptographic verification, when signatures are compared, then constant-time comparison is used and keys are rotated per policy with zero downtime. - Given session establishment, when cookies are set, then they are HttpOnly, Secure, and SameSite=Lax or stricter.

Audit Logging and Graceful Degradation to Manual Intake

- Given any token lifecycle event (issued, rotated, validated, consumed, expired, revoked, failed), when it occurs, then an audit record is written including timestamp, correlation_id, token_id hash, claim_ref, event_type, device_fingerprint hash, IP, user_agent, result, and reason_code. - Given audit storage, when records are persisted, then they are append-only, tamper-evident (hash-chained), and retained per compliance policy (e.g., 7 years) with access restricted to authorized roles. - Given token verification fails for any reason, when the user flow continues, then the user is routed to the manual intake start without any prefilled data or confirmation of claim existence. - Given the manual intake route is displayed, when the page loads, then no claim data is present in HTML, API responses, or client storage, and copy uses generic language. - Given accessibility, when the failure screen renders, then it meets WCAG 2.1 AA for contrast, focus, and screen reader announcements.

Policy & Intake Data Aggregation

"As a claims manager, I want ClaimFlow to prefill claimant forms from policy and prior intake data so that submissions are faster and more accurate."

Description

Create a data aggregation service that retrieves and merges policy system records, prior intake submissions, and claim context into a canonical prefill schema. Map and normalize key fields (names, policy number, loss date, asset identifiers like VIN or property address, contact methods), preserving field-level provenance and timestamps. Apply recency and effective-date logic to select authoritative values, with configurable precedence rules by line of business. Provide resilient connectors with retry/backoff, caching, and idempotent requests to minimize latency during prefill. Expose a versioned API to the Quick Prefill UI and log payloads for traceability, while enforcing PII access controls and redaction where appropriate.

Acceptance Criteria

Canonical Prefill Schema Mapping and Normalization

Given an authenticated request with claimId and lineOfBusiness When the aggregation service is invoked Then it returns a 200 response with a payload that validates against Canonical Prefill Schema v1.0 And the payload includes required fields: claimId, lineOfBusiness, policyNumber, insured.fullName, loss.date, and one asset identifier (asset.vin or asset.property.address) And contact methods are normalized into contacts[] with method in [phone,email,sms] and values normalized (phone E.164, email RFC 5322), addresses normalized to the configured postal standard with country codes And all date/time fields use ISO 8601 (UTC) When an upstream field cannot be mapped Then it is excluded and a warning is added to metadata.warnings with code MAP_UNSUPPORTED_FIELD

Field-Level Provenance and Timestamps Preservation

Given any prefilled field in the response Then metadata.provenance exists for that field with sourceSystem, sourceType (policy|intake|context), sourceRecordId, and observedAt (ISO 8601) When effective dates are available from the source Then provenance.effectiveStart and provenance.effectiveEnd are populated for that field When a value is selected from multiple sources Then provenance.selectionReason includes ruleId, selectedSource, and candidateSources[] with at least two alternatives and their observedAt Then 100% of returned prefilled fields have provenance.sourceSystem populated

Recency and Effective-Date Resolution with LoB Precedence

Given multiple candidate values for the same field across sources When selecting the authoritative value Then the engine applies precedence rules configured for the request's lineOfBusiness When loss.date falls within a candidate's [effectiveStart, effectiveEnd) Then that candidate is prioritized over more recent observedAt values When no candidate has effective dates Then the most recent observedAt value is selected Then the selected value includes provenance.selectionReason.ruleId and selectedSource matching the applied rule And per-field decisionDurationMs is less than 5 ms When precedence configuration is changed and the service is redeployed Then new requests reflect the updated rules

Resilient Connectors with Retry/Backoff and Idempotent Requests

Given an upstream call returns HTTP 429, 5xx, or times out When the connector retries Then it uses exponential backoff with full jitter (base 200 ms, factor 2, max 3 retries) And each upstream call has a client timeout of 1500 ms; cumulative time per source does not exceed 5000 ms Given duplicate requests with the same Idempotency-Key within 5 minutes When the service processes the second request Then no duplicate upstream side effects occur and the cached result is returned with header Idempotent-Replay: true When all retries fail for a non-critical source (per configuration) Then the service returns HTTP 206 with partial prefill and metadata.warnings including code SOURCE_UNAVAILABLE and the failed source When all retries fail for a critical source (per configuration) Then the service returns HTTP 502 with error code PREFILL_UPSTREAM_FAILURE

Caching Strategy to Minimize Prefill Latency

Given a repeat request for the same claimId within TTLs (policy=10m, priorIntake=5m, claimContext=1m) When the aggregation runs Then cached results are used for those sources and no upstream calls are made Then end-to-end latency meets SLOs: cold-cache P95 ≤ 1200 ms and warm-cache P95 ≤ 600 ms measured over the last 24 hours with ≥ 1000 requests When an upstream change event is received for a cached entity Then relevant cache entries are invalidated within 60 seconds Then cache hit ratio for repeat requests within 10 minutes is ≥ 70%

Versioned API and Payload Traceability

Given a GET request to /api/prefill/v1 with claimId and valid Authorization When the request is processed Then the service responds 200 with header X-Schema-Version: 1.0 and a body conforming to that version When the client requests an unsupported API version Then the service responds 400 with error code UNSUPPORTED_API_VERSION and a list of supportedVersions Then every request and response is logged with correlationId, claimId, apiVersion, latencyMs, and outcome; PII fields are redacted per policy; logs are queryable by correlationId within 2 minutes When a breaking change is introduced Then the previous version remains available for at least 90 days and deprecation headers (Deprecation, Sunset) are included

PII Access Controls and Redaction

Given the caller's access token scopes and role When the response is built Then only fields permitted by RBAC policy are included; unauthorized fields are omitted or replaced with REDACTED and metadata.redactions lists field paths and reasons Then transport uses TLS 1.2+ and responses containing PII include Cache-Control: no-store When an unauthorized subject requests PII Then the service returns 403 with error code INSUFFICIENT_SCOPE and no PII fields in the body Then an audit record is written for each response containing PII with subjectId, claimId, fieldsServed count, timestamp, and correlationId

One-Tap Prefill Confirmation UI

"As a claimant, I want to confirm prefilled details with one tap and edit only what’s incorrect so that I can finish my submission quickly on my phone."

Description

Deliver a mobile-first, accessible UI that renders prefilled sections (policyholder, policy details, loss details, vehicle/property info, and contacts) with a single "Confirm All" action and inline, field-level edit capability. Visually differentiate prefilled fields, support quick section expand/collapse, and enable smart defaults and masked sensitive values. Persist changes in real time with optimistic updates and autosave, and handle intermittent connectivity gracefully. On confirmation, commit verified data to the claim record and trigger downstream workflow routing. Ensure compatibility with white-label theming and embed contexts within ClaimFlow’s intake flows.

Acceptance Criteria

Secure SMS Link Binding and Prefill Integrity

- Given a claimant opens the SMS intake link, When the token is exchanged, Then the session is bound to the claimId and policyId and scopes access to that claim only. - And the token is single-use, expires after first successful bind or 30 minutes of inactivity, and reuse returns 401 with a "Link expired" screen offering re-request. - When prefill data is retrieved, Then only fields in the allowed prefill schema are populated; all fields include prefillSource and prefillTimestamp in the payload. - If the link token is invalid or mismatched, Then no data is displayed and no API calls leak the existence of the claim.

Render Prefilled Sections with Visual Differentiation and Accessibility

- Given the session is bound, When the screen loads on a mobile device 360–430px width, Then sections render with prefilled values visually differentiated (badge or tint) and data-prefilled=true attributes for testability. - All colors meet WCAG AA contrast; focus order and aria labels include the "prefilled" state; tap targets are at least 44x44 px. - First Contentful Paint <= 2.0s on 3G Fast; total payload for initial render <= 1.0 MB. - Applied theme tokens (colors, fonts, spacing, radius) are respected; no default brand assets are visible.

Inline Field Edit with Optimistic Autosave and Validation

- Given a prefilled field is editable, When the user changes the value and blurs, Then the UI shows "Saving…" and updates the value immediately. - A PATCH is sent within 200ms; on 2xx, "Saved" appears within 1s with a lastSaved timestamp; the field is marked verified=false until "Confirm All". - On validation error (4xx), the field reverts to the last saved value, shows an inline error with guidance, and announces the error to screen readers. - While offline, the change is queued with status "Pending"; on reconnect, it syncs automatically in edit order; no data loss after app refresh.

Confirm All Commits and Triggers Workflow Routing

- Given all required fields are valid, When the user taps "Confirm All", Then a single commit API call with current field values and verification flags is sent and returns 2xx within 2s on 3G Fast. - The claim record persists verified=true per field with server timestamps and user context; an Intake.Verified event with correlationId is emitted for routing. - The UI transitions to the next step within 1s and disables the button during in-flight; repeated taps are idempotent (no duplicate events or updates). - If the commit fails, a non-blocking error banner appears with retry; no partial verification is applied.

Section Expand/Collapse with Progress and Accessibility

- Given sections are collapsed by default except the first, When a section is toggled, Then aria-expanded updates, the chevron rotates, and content renders within 300ms without layout shift. - Section completeness shows a checkmark when all required fields in that section are valid; counts update in real time as fields change. - Keyboard users can toggle via Enter/Space and navigate fields in logical order; open/closed state persists across navigation and reload within the session.

Masked Sensitive Values with Reveal and Audit

- Given sensitive fields exist (e.g., policy number), When rendered, Then values are masked by default and labeled "Hidden". - When "Reveal" is tapped, Then the full value is shown for up to 30 seconds and re-masked on timeout, navigation, or screen lock. - Unmasked values are never logged to analytics or client logs; copy is disabled until revealed. - An audit event is recorded with field name and reveal timestamp (no value).

Connectivity Resilience and Offline Sync

- Under simulated 150ms latency and 5% packet loss, When editing and confirming, Then autosave operations eventually succeed; retries use exponential backoff up to 5 minutes. - When offline is detected, Then an offline banner appears within 1s; edits are queued locally and marked "Pending"; navigation does not discard queued changes. - On reconnect, queued changes sync within 10s preserving field-level ordering; conflicts resolve by last-write-wins per field with a notification only if server validation fails.

Validation & Conflict Resolution Rules

"As an adjuster, I want the system to flag and resolve inconsistent prefilled data so that I can trust the submission and avoid rework."

Description

Implement a configurable validation engine that enforces format checks (e.g., policy number patterns, VIN length), semantic rules (loss date within coverage period, asset on policy), and cross-field consistency. When conflicts exist between sources (policy vs. prior intake), select a winner using configurable precedence and surface clear prompts for claimant verification. Normalize addresses, phone numbers, and names to standard formats, and provide real-time error messaging and suggestions. Log validation outcomes and unresolved conflicts for adjuster review, and expose admin-managed rule sets by line of business and jurisdiction.

Acceptance Criteria

Real-time Format Validation on Quick Prefill

Given a claimant opens the prefilled intake via secure SMS link and fields are auto-populated When a prefilled field loses focus or is edited (policy number, VIN, email) Then the system validates the value against the configured format rule within 250 ms, displays an inline error with a helpful example if invalid, and disables Submit until all format errors are resolved Given Line of Business = Auto and a VIN is present When the VIN is validated Then it must be exactly 17 characters, exclude I/O/Q, and match the configured VIN pattern; otherwise, show an inline error and a “Paste VIN from clipboard” suggestion if clipboard content matches VIN pattern Given a policy number is present When validated Then it must match the active policy-number regex for the claim’s carrier and jurisdiction; on failure, show the rule name and an example pattern Given an email or phone number is present When validated Then email must match the configured email pattern and phone must be parseable; on failure, display fix suggestions (e.g., “Add country code”)

Semantic Coverage Validation for Loss Date and Asset

Given policy effective and expiration dates are retrieved for the claim When the loss date is prefilled or edited Then the loss date must be within the coverage period (inclusive); otherwise, show a blocking error “Loss date is outside coverage period” and prevent submission Given Line of Business = Auto and a VIN is present When validating semantic rules Then the VIN must match an asset on the active policy; if not found, prompt the claimant to confirm or correct, and flag “Asset not on policy” for adjuster review Given Line of Business = Property and a loss address is present When validating semantic rules Then the loss address must match a covered location on the policy; if unmatched, require claimant confirmation and flag for adjuster review

Cross-Field Consistency by Line of Business

Given Line of Business = Auto When Incident Type = Collision Then require either “Other Party Name” or “Hit-and-run” = true, and hide property-only fields Given Contact Preference = SMS When validating cross-field dependencies Then require a valid mobile phone; if Contact Preference = Email, require a valid email address Given Loss Address Country and Postal Code are provided When validating cross-field consistency Then postal/ZIP format must correspond to the selected country; otherwise, display an inline error and prevent submission

Conflict Resolution with Configurable Source Precedence

Given conflicting values exist for a field between Policy System and Prior Intake When a precedence rule set is active (e.g., Policy > Prior Intake > Claimant Entry) Then the system selects the higher-precedence value, marks the field as “Resolved by precedence,” and records the losing value Given a field is configured as “claimant-verifiable” and a conflict is detected When displaying the prefilled form Then show a clear prompt with both values and require the claimant to choose or edit before submission Given a conflict is auto-resolved by precedence When the claimant edits the field Then the system revalidates, updates the resolution status to “Claimant override,” and logs the change

Data Normalization for Addresses, Phones, and Names

Given an address is entered or prefilled When the field loses focus Then normalize to the selected country’s postal standard with standardized casing, street suffix, and ZIP+4 when available; display the normalized form with an option to accept or revert Given a phone number is entered or prefilled When the field loses focus Then normalize to E.164, preserving country and area codes; display the normalized number and store both raw and normalized values Given a person or company name is entered or prefilled When the field loses focus Then normalize casing, remove leading/trailing whitespace, and strip invalid characters; do not alter intentional capitalization patterns beyond standard rules

Audit Logging of Validation and Conflicts

Given any validation rule executes When the outcome is produced Then log claim ID, user/session ID, timestamp (UTC), field, input value hash, normalized value hash, rule ID, rule set version, outcome (pass/fail), and any messages Given an unresolved conflict remains at submission When the claimant submits the form Then block submission if the field is mandatory; otherwise, allow submission but create an adjuster task with conflict details and link back to the field Given a claim is opened by an adjuster When viewing the Validation Log Then the adjuster can filter by rule set, field, outcome, and date range and export logs as CSV

Admin Rule Sets by LOB and Jurisdiction

Given an admin user is in the Rules console When creating or editing a rule set Then they can scope it to Line of Business and Jurisdiction(s), assign a version number, and set status (Draft, Active, Deprecated) Given multiple rule sets match a claim’s LOB and jurisdiction When evaluating rules Then the engine selects the single Active rule set with the highest version; ties are prevented by the console Given a rule set is published as Active When a new claim intake starts Then the engine loads that rule set within 200 ms and records the applied rule set ID and version on the claim

Confidence Indicators & Edit Audit Trail

"As a claims manager, I want visibility into what was auto-filled and what the claimant changed so that I can audit accuracy and improve our processes."

Description

Attach source metadata and confidence scores to each prefilled field, and display subtle confidence indicators to claimants and more detailed provenance to internal users. Track all claimant edits with before/after values, timestamps, and actor, and store an immutable audit record linked to the claim. Provide exportable audit views and events to feed QA and model training pipelines, enabling continuous improvement of extraction and mapping accuracy. Respect privacy constraints by minimizing on-screen exposure of sensitive data while retaining compliant auditability.

Acceptance Criteria

Claimant Confidence Indicator UI

Given a claimant opens a prefilled Quick Prefill form via a secure SMS link for an existing claim When any field is displayed with an associated confidence score c in [0.00, 1.00] Then a subtle indicator appears next to that field with tiers mapped as: High (c >= 0.85), Medium (0.60 <= c < 0.85), Low (c < 0.60) And numeric confidence values are not shown to the claimant And Medium/Low tiers display helper text prompting “Please review” while High does not And the indicator has an accessible name that states the tier (e.g., “Confidence: Low, please review”) And fields lacking a confidence score display an “Unknown” indicator and require explicit confirmation before submission And no source system or model details are shown in claimant-facing views

Internal Provenance Panel for Prefilled Data

Given an authenticated internal user with role Adjuster or Admin views a claim in ClaimFlow When the user expands the provenance for a prefilled field Then the panel shows: source_system, source_type (e.g., policy, prior intake, OCR/NLP), source_record_id, extractor (method), model_version, confidence_score (0.00–1.00, 2 decimals), ingest_timestamp (UTC ISO-8601), mapping_path, and transformation_rules_applied And if a source asset is available and permission allows, a “View Source” link is shown; otherwise a permission notice is displayed And if any metadata item is unavailable, a “Not available” placeholder is rendered for that item And claimant-facing screens never surface the provenance panel or its contents

Comprehensive Edit Audit Trail Capture

Given a claimant edits a prefilled field and saves or submits the form Then an audit event is appended containing: claim_id, field_key, before_value, after_value, actor="claimant", channel="SMS", timestamp_utc (ms precision), event_id, and sequence_number And multiple edits to the same field generate distinct sequential events preserving each before/after pair And a claimant’s explicit confirmation without change logs a "verified" event including value, actor, and timestamp And only committed changes (save/submit) generate audit events; transient keystrokes without commit do not

Immutable Audit Record and Integrity Verification

Given audit events are written for a claim Then events are persisted in an append-only store with write-once semantics And each event includes a sha256 hash of its contents and previous_event_hash to form a verifiable chain And a daily Merkle root over events is computed and stored separately for independent verification And attempts to modify or delete prior events are rejected by the storage layer and generate security alerts And a verification endpoint reports "valid" for an untampered chain and "invalid" if any event or linkage is altered

Audit Export and Streaming for QA and Model Training

Given an internal user with Export Audit permission applies filters (date range, claim_id(s), field_key(s), actor, confidence_tier) When the user requests an export Then the system produces a downloadable file in CSV or JSONL within 60 seconds for up to 100,000 events and returns a checksum And by default, exports redact Sensitive fields and exclude extraneous PII; values necessary for QA remain included per policy And including raw before/after values for Sensitive fields requires PII_View permission and an audit-noted justification entered by the user And webhook streaming can be configured with target URL and secret; committed events are delivered within 5 seconds with exponential backoff retries for up to 24 hours on failure

Privacy Minimization and Access Controls

Given fields are classified as Sensitive or Non-Sensitive Then claimant-facing UIs never display source metadata, raw extraction text, or numeric confidence values And internal provenance and audit views are accessible only to roles Adjuster, QA, or Admin; viewing raw before/after values for Sensitive fields requires PII_View permission And all exports default to masking Sensitive data unless explicitly overridden by a permitted user with justification captured in the audit And audit records are encrypted at rest, and all access to provenance/audit views is logged with user, timestamp, and purpose

Prefill Performance Analytics & A/B Controls

"As a product owner, I want analytics on how Quick Prefill performs and impacts completion so that I can optimize the experience and demonstrate time savings and error reduction."

Description

Instrument the Quick Prefill flow to capture funnel metrics (open rate, prefill load time, confirm rate, edit rate, error rate, completion time) and segment by channel, device, line of business, and geography. Provide configurable feature flags to enable/disable prefill or specific field groups, supporting A/B and holdout tests. Surface dashboards and export feeds to BI with privacy-safe, aggregated data, and define success KPIs and alerts (e.g., latency SLO breaches, increased edits on specific fields). Use insights to iteratively tune mappings, rules, and UI for measurable throughput and accuracy gains.

Acceptance Criteria

Funnel Metrics Instrumentation Coverage and Event Semantics

Given a claimant opens the SMS link, When the landing page renders, Then an "open" event with claim_id, session_id, and timestamp_ms is captured within 1 second of render. Given prefill begins, When data fetch is initiated, Then a "prefill_load_start" event is captured; When prefill is fully rendered, Then a "prefill_loaded" event is captured with load_time_ms = t_loaded − t_start. Given the claimant interacts, When one-tap confirm is pressed, Then a "confirm" event is captured; When a field is edited, Then an "edit" event is captured with field_group_id and field_id and diff_type (added/modified/cleared) without storing raw values. Given an error occurs, When a client or server error is encountered, Then an "error" event is captured with error_code, http_status (if applicable), and retry_count. Given a submission completes, When the claimant submits, Then a "completion" event is captured with total_duration_ms from first render. Rule: ≥95% of sessions that reach the landing page contain a coherent event sequence open → prefill_load_start → prefill_loaded before confirm/edits/completion under the same session_id.

Segmentation by Channel, Device, Line of Business, and Geography

Rule: All analytics events include dimensions channel (sms/email/portal), device_os (ios/android/other), device_type (mobile/tablet/desktop), browser_family, line_of_business (auto/home/commercial/other), and geo_country/geo_region derived from IP or claim metadata. Rule: Time is normalized to UTC with event_day and event_hour derived fields. Rule: Non-derivable dimensions are set to "unknown"; data completeness targets: ≥99% non-unknown for channel and line_of_business; ≥97% for device_os/device_type; ≥95% for geo_country. Given malformed user-agents, When parsing fails, Then device fields default to "unknown" and an internal parse_error flag is set for monitoring. Given segment filters are applied downstream, When events are queried by any single dimension, Then the filtered counts match unfiltered totals partitioned by that dimension within 0.5% for the same date range.

Feature Flags and A/B/Holdout Assignment Controls

Given prefill master flag and field_group flags, When toggled for a tenant/LOB/channel, Then changes propagate to clients within 5 minutes without redeploy and are audit-logged with actor, timestamp, and scope. Given an experiment configured with target split (e.g., 50/50), When eligible claims arrive, Then assignment is deterministic by hash(claim_id, experiment_id), sticky for 48 hours per claim, and mutually exclusive across experiments in the same namespace. Rule: Observed assignment proportion remains within ±2 percentage points of target per arm over any window of ≥10,000 assigned claims. Given a holdout of X%, When traffic flows, Then X%±2pp of eligible claims bypass prefill (or specified field_groups) and follow the default path. Given exclusion rules (e.g., region=CA or device_os=other), When a claim matches an exclusion, Then it is not assigned to the experiment and receives the default flag state. Given a user is assigned to control where prefill is disabled, When they reach the form, Then prefilled values are not displayed and manual entry is required.

Dashboards and BI Export with Freshness and Consistency

Given the analytics dashboard, When a user selects a date range, Then charts display open rate, prefill load time p50/p95, confirm rate, edit rate (overall and by field_group), error rate, and completion time p50/p95 for that range. Given filters for channel/device/LOB/geo, When filters are applied, Then all widgets update consistently and reflect the filtered population. Rule: Dashboard data freshness is ≤15 minutes delay for streaming views covering the last 24 hours. Rule: Dashboard p50 chart loads in ≤3 seconds for last 24 hours and ≤10 seconds for last 90 days under p95 of user requests. Given BI exports, When hourly exports run, Then partitioned files (parquet or csv) with schema version and event_date are delivered to the configured S3/BigQuery destination by HH:15 each hour with success markers. Rule: 7-day reconciliation: dashboard daily totals match BI export aggregates within 1% for each metric and segment. Rule: Dashboard monthly availability ≥99.5%.

Privacy-Safe Aggregation and Access Control

Rule: No raw PII (names, policy numbers, phone numbers, emails, VINs, street addresses) is stored in event payloads; only pseudonymous IDs, field_group_id/field_id, and categorical codes are retained. Rule: All data in transit uses TLS 1.2+ and at rest uses AES-256 equivalent encryption. Rule: Aggregated metric views enforce k-anonymity with k≥20; segments below threshold are suppressed or rolled up to higher-level aggregates. Given a user without Analytics role, When attempting to access dashboards or exports, Then access is denied with a 403 and the attempt is logged with user_id and timestamp. Rule: Data retention for raw events is 13 months; purge jobs run daily and log deletions with counts. Given a subject requests data removal, When a deletion request for claim_id is processed, Then associated analytics events are purged within 30 days and are excluded from future aggregates.

KPI Definitions and Alerting for Latency, Edit Rate, and Errors

Rule: For each tenant/LOB, baseline and target KPIs are stored: confirm_rate_target (e.g., +5pp over baseline), edit_rate_target per field_group, and prefill_load_time_p95_target ≤ 2000 ms. Given live traffic, When prefill_load_time_p95 exceeds 2000 ms for 5 consecutive 5-minute windows for any tenant/LOB, Then a Sev-2 alert is sent to Slack/Email/PagerDuty with affected services and top error codes. Given weekly aggregates, When edit_rate for any field_group increases by >25% week-over-week with two-proportion z-test p<0.05, Then a Sev-3 alert is created with field identifiers and segment heatmap link. Given hourly monitoring, When error_rate (events with error_code / sessions) exceeds 2% in the last hour for any segment, Then an alert is emitted and on-call is paged if sustained for 15 minutes. Rule: All alerts include runbook links and auto-close when metrics recover for 30 consecutive minutes.

Experiment Reporting and Iterative Tuning Workflow

Given an experiment with predefined primary metric(s), When minimum sample size per arm (e.g., 5,000 sessions) is reached, Then an automated report computes uplift, confidence intervals, and p-values using a predefined test and stores results with versioned artifacts. Given a statistically significant win on the primary metric at α=0.05 with power ≥0.8, When the report is approved, Then a change request is generated to promote the winning flag configuration to 100% in targeted segments. Given no significance or a regression, When the experiment completes, Then the system recommends rollback or additional sampling and documents next steps. Given field-level edit/error hotspots are detected (top 5 fields by excess edit rate vs. baseline), When weekly jobs run, Then a recommendation summary lists candidate mapping changes or UI copy tweaks with estimated impact. Rule: All experiment decisions, flag promotions, and rollbacks are audit-logged with approver, timestamp, and diffs.

AutoLocale

Delivers micro-forms in the claimant’s preferred language and reading level, detected from the conversation context. Localizes guidance, units, and date formats and auto-inserts jurisdiction-specific disclosures. Increases comprehension, reduces confusion, and supports compliant, inclusive communication at scale.

Requirements

Contextual Language Detection & Confirmation

"As a claims manager, I want the system to automatically detect a claimant’s preferred language so that I can communicate clearly without manual setup."

Description

Automatically infer the claimant’s preferred language and dialect from conversation context (incoming messages, previous interactions, device/browser locale, and policy metadata), compute a confidence score, and apply a deterministic fallback strategy with an optional one-tap confirmation prompt. Persist the selected language per claimant and per claim, support model-based detection for mixed-language threads, and expose an internal API and event to downstream components (micro-forms, guidance, disclosures) to consume the resolved locale. Ensure sub-200ms detection latency for chat, with graceful degradation to last-known locale.

Acceptance Criteria

Chat Message Language Inference with Confidence Thresholds

Given a new incoming chat message with available context (prior interactions, device/browser locale, policy metadata) When the language detection service processes the input Then it returns a BCP 47 tag (e.g., es-MX), a numeric confidence in [0.0–1.0], and the top-2 candidates with scores And the p95 end-to-end detection latency for chat is ≤ 200 ms over the last 10,000 requests And the p99 latency is ≤ 300 ms Given a detection result with confidence ≥ 0.85 When resolving the claimant’s locale for this chat Then the detected locale is applied without user prompt and recorded as auto-detected (not confirmed) Given a detection result with 0.50 ≤ confidence < 0.85 When resolving the locale Then the system applies the last-known locale if present and triggers a one-tap confirmation for the detected locale Else it applies the detected locale temporarily and triggers a one-tap confirmation Given a detection result with confidence < 0.50 and no last-known locale When resolving the locale Then the system applies the product default (en-US) and triggers a one-tap confirmation for the detected locale

Deterministic Fallback & Defaulting Order

Rule: Locale resolution order is deterministic: (1) claimant-confirmed per-claim locale, (2) claimant-confirmed global locale, (3) current-message detection (within latency budget), (4) last-known auto-detected per-claim, (5) last-known auto-detected per-claimant, (6) device/browser locale, (7) policy metadata locale, (8) product default en-US Given multiple sources are available When resolving the locale Then the earliest applicable item in the order above is selected and the chosen source is recorded Given the detector has not returned within 150 ms for chat When applying the order Then the resolver skips item (3) for this turn, proceeds to the next item, and completes resolution within an additional 50 ms budget Given any locale is resolved When persisting the decision Then the system stores: locale (BCP 47), source, confidence (if applicable), resolvedAt timestamp, and traceId Given the resolution order configuration is updated behind a feature flag When the flag is off Then production continues to use the prior order without behavior change

Locale Persistence per Claimant and per Claim

Given a locale is resolved for a claim interaction When persisting Then the system writes a per-claim record with fields: claimId, claimantId, locale (BCP 47), confirmed (bool), source, confidence (nullable), updatedAt, updatedBy (system|user) Given a claimant confirms a locale in the one-tap prompt When saving Then confirmed=true is stored for the current claim and the claimant’s global locale is updated only if the user selects “apply to all my claims” Given a new claim is created for an existing claimant with a confirmed global locale When initializing the claim Then the claim’s initial locale is set to the claimant’s confirmed global locale and marked confirmed=true, source=“claimantConfirmed” Given a subsequent auto-detection disagrees with a confirmed locale When resolving Then the confirmed locale remains in effect and no prompt is shown unless the user manually requests a change Given a persistence write is retried When the same resolution is submitted within 10 seconds Then the operation is idempotent and results in a single stored state

Mixed-Language Thread Handling

Given the last 5 user messages contain multiple languages When the model computes language distribution Then it outputs per-language weights summing to 1.0 and selects a dominant language if any weight ≥ 0.60 Given no language weight ≥ 0.60 and a prior confirmed locale exists When resolving Then the prior confirmed locale is retained and a one-tap confirmation is queued non-intrusively (no modal) for the alternative top language Given code-switching within a single message is detected When ranking candidates Then the top-2 candidates are returned; if score delta < 0.15, the result is marked lowConfidence and the confirmation prompt is triggered Given dialect cues are detected (e.g., es-MX vs es-ES) When emitting the locale Then a dialect-specific BCP 47 tag is returned; otherwise the base language tag is returned with reason=dialectAmbiguous

One-Tap Confirmation Prompt Behavior

Given locale resolution yields lowConfidence or first-time detection for this claimant/claim When presenting the UI Then a one-tap prompt appears within 300 ms showing the detected language name in both the current UI language and the detected language, with options: Confirm and Change Given the user taps Confirm When processing the action Then the locale is persisted as confirmed=true with source=userConfirmed and the prompt is dismissed without reappearing for 24 hours or until the locale changes Given the user taps Change When the language picker opens Then the user can search and select from supported locales; upon selection, the chosen locale is applied immediately and persisted as confirmed=true Given no user action within 30 seconds When the prompt times out Then the current applied locale remains, the prompt quietly dismisses, and a non-blocking reminder is allowed at most once per 24 hours Accessibility rule: The prompt, buttons, and picker are fully operable via keyboard and screen readers and meet WCAG 2.1 AA labels and contrast requirements

Internal Locale Resolution API and Event

Given a consumer requests GET /internal/locale/resolve?claimId={id}&claimantId={id} When the locale resolver handles the request Then it returns 200 with JSON: { locale, baseLanguage, region, script, confidence, confirmed, source, resolvedAt, traceId, version } where locale is a valid BCP 47 tag and version="v1" And p95 API latency ≤ 100 ms and p99 ≤ 200 ms under nominal load Given a locale is resolved or updated When publishing to the event bus Then a LocaleResolved.v1 event is emitted within 50 ms containing the same fields as the API plus claimId and claimantId Given the request references unknown IDs When resolving Then the API returns 404 only if both claimId and claimantId are unknown; otherwise it resolves using the known identifier Given a client supplies an override locale in the request with override=true When processing Then the resolver applies the override, persists it as confirmed=true with source=apiOverride, and emits the event

Performance, Timeouts, and Graceful Degradation

SLA: For chat interactions, p95 end-to-end locale resolution time (from message receipt to resolved locale applied) is ≤ 200 ms; p99 ≤ 300 ms Given the detector service returns error or exceeds 150 ms When resolving Then the resolver falls back per the deterministic order without exceeding an extra 50 ms and marks reason=fallbackTimeout or reason=detectorError Given 5 consecutive detector timeouts occur within 60 seconds When monitoring Then a circuit breaker opens for 5 minutes, all resolutions skip live detection, and an alert is sent to on-call Given the system is operating in fallback-only mode When a user interacts in chat Then no user-visible errors occur, last-known or default locale is used, and a LocaleResolved.v1 event is still emitted with source=fallback and confidence=null Observability rule: Metrics are emitted for latency, timeouts, error rates, confidence distributions, prompt trigger rates, and overrides; logs exclude PII and include traceId for correlation

Reading-Level Adaptation Engine

"As a claimant with varying literacy, I want forms written at an appropriate reading level so that I can understand and complete them accurately."

Description

Generate and deliver micro-form copy and guidance at configurable reading levels (e.g., Grade 6, 8, 10) using plain-language templates, controlled vocabulary, and automated readability scoring (e.g., FKGL), with per-locale tuning. Support fallback to nearest available level, highlight complex terms with tooltips or alternatives, and allow admins to set default reading-level policies by product line and jurisdiction. Provide an API for dynamic adjustments mid-conversation when comprehension signals drop.

Acceptance Criteria

Generate Copy at Requested Reading Level

Given a claimant locale of en-US and a requested reading level of Grade 8 When the micro-form copy and guidance are generated Then the FKGL score for the visible copy (excluding legal disclosures) is between 7.5 and 8.5 And only terms from the approved Grade 8 controlled vocabulary for en-US are present And the selected template is the en-US Grade 8 plain-language template And the response payload includes metadata fields readingLevelApplied=8, fkglScore, locale="en-US", templateId And no individual sentence exceeds FKGL 9.0

Fallback to Nearest Available Reading Level

Given the requested reading level (e.g., Grade 7) is not available and the available levels are Grade 6 and Grade 8 When the engine generates the micro-form Then the engine selects the nearest available level; if equidistant, it selects the lower level And the response metadata includes fallbackFrom=7 and fallbackTo=6 And the applied FKGL is within ±0.5 of the fallback level And an analytics event "reading_level_fallback" is emitted with conversationId and reason="level_unavailable"

Highlight Complex Terms with Tooltips or Alternatives

Given the generated copy contains terms outside the controlled vocabulary for the applied level When the micro-form is rendered Then each such term is either replaced with an approved simpler alternative or annotated with a tooltip containing a plain-language definition in the claimant's locale And tooltips are accessible via keyboard and screen reader And the presence of tooltips does not increase the computed reading level by more than 0.2 grade And at least 95% of flagged terms are replaced; no more than 5% rely solely on tooltips And highlighted terms use a consistent icon and aria-label "Definition"

Admin Policy Defaults by Product Line and Jurisdiction

Given an admin with role "Localization Admin" sets default reading-level policies via Admin API for ProductLine=Auto, Jurisdiction=CA-ON -> Grade 6; and ProductLine=Property (global) -> Grade 8 When new conversations start with matching attributes Then the engine applies Grade 6 for Auto claims in CA-ON and Grade 8 for Property claims in all jurisdictions And policy precedence is Jurisdiction+ProductLine > ProductLine > Global And overrides via conversation API are permitted and logged with actor, timestamp, oldLevel, newLevel, reason And an audit record is written for each policy change including who, when, before/after values

Dynamic Reading-Level Adjustment API Mid-Conversation

Given an active conversation with readingLevelApplied=10 and a comprehension score below threshold (e.g., 0.4) is detected When POST /conversations/{id}/reading-level/adjust is called with target=lower and an idempotencyKey Then the next generated micro-form uses the nearest lower allowed level per current policy And the API responds with 200 within 500 ms and body containing oldLevel, newLevel, effectiveAt="next_message", and reason And duplicate requests with the same idempotencyKey are idempotent and return the same newLevel And an event "reading_level_adjusted" is emitted and persisted with conversationId and trigger="low_comprehension"

Per-Locale Tuning and Readability Scoring

Given the claimant locale is es-MX and the requested reading level is Grade 6 When the copy is generated Then the engine uses the configured locale-appropriate readability metric for es-MX and achieves a score mapped to Grade 6 ±0.5 And date, number, and measurement formats are localized for es-MX And jurisdiction-specific disclosures for MX are inserted and tagged as legalDisclosure and excluded from readability scoring And the response metadata includes scoringMethod, scoreValue, readingLevelApplied, locale, and disclosureIds

Locale-aware Units, Dates, and Currency

"As a claimant, I want units and dates displayed in my familiar format so that I don’t make mistakes entering information."

Description

Localize measurement units, date/time formats, and currency presentation based on the resolved locale and jurisdiction, converting inputs and outputs as needed (e.g., inches↔centimeters, MM/DD/YYYY↔DD/MM/YYYY), and normalize captured data to system canonical units for downstream processing. Handle time zone offsets for appointment windows, ensure disambiguation in UI (e.g., month names), and support per-form section overrides where compliance requires fixed formats.

Acceptance Criteria

Locale-Dependent Unit Display with Canonical Storage

Given the resolved locale is en-US and the canonical length unit is centimeters When the claimant enters 12 in for the field "Dent Length" Then the value is stored as 30.48 cm (±0.01) in canonical storage And the claimant UI continues to display 12 in And a reviewer with locale fr-FR sees 30,48 cm for the same field And validations use canonical units (e.g., a minimum of 10 cm is satisfied)

Locale-Specific Date Entry with UI Disambiguation

Given the resolved locale is en-GB When the claimant enters 03/04/2025 in a date-only field Then the system parses as 3 April 2025 and stores 2025-04-03 (ISO 8601, date-only) Given the resolved locale is en-US When the claimant enters 03/04/2025 Then the system parses as March 4, 2025 and stores 2025-03-04 And the UI renders the chosen date with an unabbreviated month name to avoid DD/MM vs MM/DD ambiguity

Appointment Windows Converted Across Time Zones

Given the adjuster timezone is America/Los_Angeles and the claimant timezone is America/New_York When the adjuster schedules 2025-03-10 2:00–3:00 PM PT Then the claimant sees 5:00–6:00 PM ET And the window is stored as UTC start 2025-03-10T22:00:00Z and end 2025-03-10T23:00:00Z And all notifications include the local time with zone abbreviation plus the ISO 8601 timestamp with offset And times that are nonexistent or ambiguous due to DST cannot be scheduled without explicit offset selection

Currency Formatting by Locale with Canonical Normalization

Given currency code USD and amount 12345.67 in canonical value When rendered for locale en-US Then it displays as $12,345.67 Given currency code EUR and amount 12345.67 When rendered for locale fr-FR Then it displays as 12 345,67 € (locale-appropriate grouping and decimal separators) And values are stored canonically as minor units and ISO 4217 code (e.g., 1234567 + USD, 1234567 + EUR) And zero-decimal currencies (e.g., JPY) display with no decimals and store exact minor units And changing locale does not convert currency; only formatting changes

Compliance Overrides Enforce Fixed Formats Per Section

Given the "Regulatory Statement" section is configured to use fixed formats: date YYYY-MM-DD, currency "USD 1,234.56", units "cm" When the claimant locale is es-MX Then the section renders and validates exactly the configured formats regardless of locale And any input not matching the fixed formats is rejected with a localized error And stored values still normalize to canonical units and ISO formats for downstream processing

Provide configurable, multi-stage approval flows for applying access changes, with routing based on risk tier, department, and value thresholds. Include inline rationale, attachments, and reviewer comments. Persist a tamper-evident audit log of recommendations, decisions, changes, and rollbacks with timestamps and actors. Offer export in CSV/PDF and an API for GRC systems, with retention settings aligned to compliance requirements.

Product Details

Vision & Mission

Problem & Solution

Details & Audience

User Personas

Product Features

Angle Assist

Requirements

Smart Alignment Overlay

Description

Acceptance Criteria

Real-time Quality Meter

Description

Acceptance Criteria

Auto-Capture Lock

Description

Acceptance Criteria

OCR-Ready Metadata Packaging

Description

Acceptance Criteria

Cross-Device Performance & Offline Mode

Description

Acceptance Criteria

On-Device Privacy, Consent & Audit

Description

Acceptance Criteria

Burst Boost

Requirements

Confidence-Triggered Burst Capture

Description

Acceptance Criteria

Multi-frame Fusion Engine

Description

Acceptance Criteria

Inline Re-extraction Pipeline

Description

Acceptance Criteria

Alternate Target Guidance

Description

Acceptance Criteria

Plate-to-VIN Lookup Integration

Description

Acceptance Criteria

Configurable Thresholds and Policies

Description

Acceptance Criteria

Outcome Analytics and Audit Trail

Description

Acceptance Criteria

Severity Map

Requirements

Damage Segmentation & Type Classification

Description

Acceptance Criteria

Severity Scoring & Thresholding

Description

Acceptance Criteria

Parts Mapping & Affected Components Identification

Description

Acceptance Criteria

Auto-Tagging & Structured Prefill

Description

Acceptance Criteria

Missing Views Detection & Capture Checklist

Description

Acceptance Criteria

Reviewer Annotations, Overrides & Audit Trail

Description

Acceptance Criteria

Proof Seal

Requirements

Cryptographic Timestamp Seal

Description

Acceptance Criteria

Location Integrity & Geofence Validation

Description

Acceptance Criteria

Content Hash & EXIF Embed

Description