Workforce Management Software

Shiftly

Goodbye Chaos, Hello Seamless Shifts

Shiftly automates shift scheduling for small restaurant and retail managers overwhelmed by constant roster changes. Its AI engine creates error-free schedules in seconds, slashing no-shows by 40%. Staff swap shifts instantly via mobile, saving managers up to 8 hours weekly and turning workforce chaos into simple, reliable team coverage.

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Shiftly

Product Details

Explore this AI-generated product idea in detail. Each aspect has been thoughtfully created to inspire your next venture.

Vision & Mission

Vision
To empower every small business to create happier teams and thriving operations through effortless, intelligent workforce scheduling worldwide.
Long Term Goal
By 2028, empower 100,000 small businesses worldwide to cut shift scheduling time by 70% and reduce labor costs by 50% through effortless, intelligent workforce automation.
Impact
Cuts weekly scheduling time by 70% for restaurant and retail managers while reducing no-shows by 40%, allowing small teams to reclaim 6-8 administrative hours per week and achieve more reliable shift coverage with fewer last-minute staffing disruptions.

Problem & Solution

Problem Statement
Small restaurant and retail managers waste hours each week manually building staff schedules and juggling last-minute changes, as existing scheduling tools are too complex, lack real-time flexibility, and don’t prevent costly no-shows or understaffing.
Solution Overview
Shiftly eliminates messy manual scheduling by instantly generating accurate, AI-powered shift rosters based on real employee availability. Managers save hours each week, while staff easily swap or claim open shifts through a sleek mobile app—taming last-minute changes and preventing costly no-shows.

Details & Audience

Description
Shiftly automates shift scheduling for small restaurant, retail, and service managers, replacing chaos with fast, error-free rosters. Owners and managers save hours weekly and sharply reduce costly no-shows. Its AI-driven engine predicts optimal staff assignments using real attendance data, while a sleek mobile interface lets teams swap shifts instantly—making workforce management simple, reliable, and seamless.
Target Audience
Small restaurant and retail managers (25-50) overwhelmed by manual scheduling, eager for quick, effortless automation.
Inspiration
On a packed Friday night, I watched a frazzled restaurant manager scribble shift changes on napkins while fielding panicked texts from staff about swaps and call-offs. The chaos was cutting into his ability to run the floor. That snapshot of stress and wasted time made it clear: managers like him needed a smarter, seamless way to handle scheduling—so Shiftly was born.

User Personas

Detailed profiles of the target users who would benefit most from this product.

B

Budget Balancer Beth

- Age 38, female, sole café proprietor - Single location, $300k annual revenue - 8 staff members - Bachelor's in Hospitality Management - Operates 12–14 hour days

Background

After starting as a barista, Beth battled rising labor costs and manual spreadsheet errors. She now demands cost-focused scheduling to avoid budget overruns.

Needs & Pain Points

Needs

1. cut labor costs without sacrificing coverage 2. automate schedule adjustments based on daily sales 3. reduce spreadsheet errors in payroll calculations

Pain Points

1. unpredictable labor expenses blowing budgets 2. manual spreadsheets causing costly scheduling errors 3. last-minute adjustments leading to payroll overruns

Psychographics

- Obsessive about trimming every labor dollar - Relies on data over intuition regularly - Risk-averse when approving extra shifts - Craves predictability and financial control

Channels

1. Google Search targeted 2. Facebook Groups small-biz 3. LinkedIn Ads professional 4. Email Newsletter tips 5. Instagram Ads foodservice

F

Franchise Flow Frank

- Age 45, male, franchise operations manager - Oversees 5 units, ~$1M annual revenue - Manages 50 staff members - MBA in Business Operations - Coordinates across 3 time zones

Background

Frank began as a store manager before becoming a regional franchise coordinator. He struggled merging schedules across spreadsheets, sparking his search for centralized scheduling.

Needs & Pain Points

Needs

1. unify multi-location schedules in one dashboard 2. enforce consistent swap policies centrally 3. forecast staffing needs across branches

Pain Points

1. fragmented schedules across spreadsheets causing confusion 2. inconsistent policy enforcement at branch level 3. time-consuming manual consolidation every week

Psychographics

- Champions brand consistency through clear processes - Thrives on multi-branch operational efficiency - Delegates tasks with confident authority - Demands seamless communication across teams

Channels

1. Slack real-time 2. Email weekly 3. LinkedIn professional 4. Google Search research 5. Webinar live

P

Peak-Season Sarah

- Age 32, female, resort operations manager - Manages 100 summer staff, 60% temps - $2M seasonal revenue - Diploma in Hospitality Management - Works 7 days a week in summer

Background

Promoted from front-desk supervisor, Sarah faced unpredictable guest-driven staffing spikes each summer. Manual reassignments fell short, so she turned to forecasting clarity.

Needs & Pain Points

Needs

1. adjust staffing instantly for guest surges 2. integrate temps seamlessly into rotations 3. predict labor demand by booking trends

Pain Points

1. sudden temp call-offs derailing coverage 2. manual forecasting causing over- and understaffing 3. poor communication with seasonal workers

Psychographics

- Thrives under seasonal staffing pressure - Values flexibility for temp workforce - Plans proactively based on forecasts - Prioritizes guest experience and coverage

Channels

1. Facebook Ads targeted 2. TripAdvisor forum 3. Instagram Ads hospitality 4. Email newsletter 5. Google Search seasonal

N

Nightshift Nora

- Age 27, female, 24/7 diner manager - Manages 20 staff across night shifts - $500k annual revenue, nighttime peak - Associate's in Culinary Arts - Lives on-site for emergencies

Background

Nora rose through evening server shifts to manage a 24/7 diner, facing frequent overnight no-shows. These midnight emergencies taught her to rely on instant mobile solutions.

Needs & Pain Points

Needs

1. receive instant no-show alerts on mobile 2. approve shift swaps quickly at night 3. secure backup staff for overnight gaps

Pain Points

1. 2am no-shows leaving shifts empty 2. delayed swap approvals costing revenue 3. unreliable backup staff leads to closures

Psychographics

- Seeks instant updates during overnight shifts - Relies on mobile tools intensely - Expects swift responses at 2am - Prioritizes staff reliability under pressure

Channels

1. SMS urgent 2. WhatsApp group 3. Mobile App push 4. Instagram DM 5. Facebook Messenger

T

Training Tim

- Age 40, male, training coordinator - Oversees programs across 8 restaurants - Manages 100 staff, 50% trainees - $3M annual training budget - Background in HR management

Background

Tim transitioned from HR roles to training coordinator, challenged to align workshops with busy shift patterns. Manual updates delayed training rollouts, leading him to seek integrated scheduling.

Needs & Pain Points

Needs

1. align training sessions with shifts automatically 2. track staff skill progress in schedules 3. avoid coverage gaps during workshops

Pain Points

1. overlapping training clashes with operational shifts 2. manual updates delaying training rollouts 3. lack of visibility into staff availability

Psychographics

- Passionate about structured staff development - Balances training needs with operations seamlessly - Trusts data-driven learning insights - Values clear scheduling transparency

Channels

1. LMS integrated 2. Email training 3. Calendar auto-sync 4. Mobile App reminders 5. Slack notifications

Product Features

Key capabilities that make this product valuable to its target users.

RiskRadar Notification

Receive automatic alerts two hours before each shift when the AI identifies a high probability of a no-show. This gives managers critical lead time to secure coverage, minimizing disruption and ensuring seamless operations.

Requirements

No-Show Risk Analysis
"As a manager, I want the system to analyze each shift’s no-show risk so that I can identify potential staffing issues before they occur."
Description

Implement an AI-driven module that evaluates each upcoming shift and calculates the probability of a no-show based on historical attendance data, shift patterns, and individual staff reliability scores. This component integrates with the existing scheduling engine to flag high-risk shifts automatically, enabling proactive intervention and reducing unexpected staffing gaps.

Acceptance Criteria
High-Risk Shift Alert Trigger
Given a shift is scheduled to start in two hours and its no-show probability is 50% or higher, When the AI module completes its analysis, Then the system sends an alert notification to the manager with shift details and risk score.
No Alert for Low-Risk Shifts
Given a shift is scheduled to start in two hours and its no-show probability is below 50%, When the AI module completes its analysis, Then the system does not generate or send any alert notification for that shift.
Mobile Notification Delivery
Given a high-risk shift alert is generated, When the manager’s mobile device is online, Then the notification appears in the Shiftly app within one minute of alert generation.
Historical Data Accuracy
Given updated attendance records from the past six months and individual staff reliability scores, When the AI module performs risk calculations, Then it uses these data sources to calculate probabilities and logs data sources used for audit.
Risk Calculation Performance
Given the daily roster contains up to 200 upcoming shifts, When the AI-driven risk analysis runs, Then it completes all probability calculations and flags high-risk shifts in under 30 seconds.
Pre-Shift Alert Scheduler
"As a manager, I want to receive alerts two hours before high-risk shifts so that I have time to arrange coverage if needed."
Description

Develop a scheduling service that triggers notifications exactly two hours before any shift identified as high-risk by the AI engine. The scheduler must account for time zones, shift start times, and configurable lead times to ensure alerts arrive with optimal advance notice for managers to take action.

Acceptance Criteria
High-Risk Shift Alert Timing
Given a shift identified as high-risk by the AI engine and scheduled to start at 10:00 AM, When the Pre-Shift Alert Scheduler runs, Then a notification is sent exactly two hours before the shift start time, at 8:00 AM local time.
Time Zone Calculation Accuracy
Given a shift in a different time zone from the manager’s default location, When the scheduler computes the notification send time, Then the alert is delivered at the correct local time two hours before the shift start.
Configurable Lead Time Adjustment
Given a manager updates the lead time setting to three hours, When a high-risk shift is scheduled, Then the notification is sent three hours before the shift start time instead of the default two hours.
Multiple Shift Reminder Handling
Given multiple high-risk shifts scheduled within a 24-hour period, When the scheduler triggers alerts, Then each shift generates exactly one notification at the configured lead time without duplication or omission.
No-Risk Shifts Exclusion
Given a shift not classified as high-risk by the AI engine, When the scheduler runs two hours before the shift, Then no alert is generated or sent for that shift.
Push & Email Notification Delivery
"As a manager, I want to receive notifications on my mobile device and via email so that I’m alerted even if I’m not actively using the app."
Description

Create a notification delivery system capable of sending alerts via both mobile push notifications and email. Ensure message templates are clear, include shift details and risk levels, and provide direct links to manage coverage. The system should retry failed deliveries and fallback to email if push fails.

Acceptance Criteria
Manager Receives Push Notification Two Hours Before High-Risk Shift
Given a shift starting in two hours with AI-identified high no-show risk, when the system triggers the alert, then the manager’s mobile device receives a push notification within one minute.
Dashboard Link in Notification Opens Shift Coverage Panel
Given the manager receives a notification, when they tap the 'Manage Coverage' link in the message, then the app navigates directly to the shift coverage panel for the specified shift.
Email Sent When Push Notification Fails
Given a push notification attempt fails after three retries, when the system detects the failure, then an email alert is sent to the manager’s registered address within five minutes.
Retry Mechanism for Failed Deliveries
Given an initial delivery failure for push or email, when the system detects the failure, then it retries up to three times at two-minute intervals before marking the attempt as failed.
Notification Template Contains Accurate Shift Details and Risk Level
Given a notification is generated, when it is sent via push or email, then the message includes the correct shift date, start time, location, risk level description, and a direct link to manage coverage.
Alert Confirmation Logging
"As a manager, I want to view a history of alerts and confirmations so that I can audit past notifications and ensure accountability."
Description

Build a logging mechanism that records every alert sent and tracks manager acknowledgments or rejections. This log should capture timestamps, delivery channels, acknowledgment status, and any follow-up actions taken. It integrates with the reporting dashboard for audit trails and performance metrics.

Acceptance Criteria
Manager Receives Risk Alert Two Hours Before Shift
Given the AI identifies a high no-show probability for an upcoming shift, When exactly two hours remain before the shift start, Then the system logs an alert record capturing timestamp, manager ID, shift ID, and delivery channel.
Manager Acknowledges Alert Notification
Given the manager taps ‘Acknowledge’ on the alert, When acknowledgment is submitted, Then the log updates with acknowledgment status set to ‘Acknowledged’ and timestamp of acknowledgment.
Manager Rejects Alert and Initiates Replacement
Given the manager selects ‘Reject’ and requests replacement, When the rejection is confirmed, Then the log records status ‘Rejected’, timestamp, and replacement request details.
Audit Log Visible in Reporting Dashboard
Given an admin views the reporting dashboard, When filtering logs by date or shift ID, Then all alert records display with timestamps, channels, statuses, and follow-up actions.
Follow-Up Actions Logged Post-Acknowledgment
Given the manager completes a follow-up action (e.g., assigns replacement), When the action is saved, Then the log entry appends follow-up action type, details, and timestamp.
Threshold & Notification Settings Panel
"As a manager, I want to adjust the alert threshold and notification preferences so that the system’s sensitivity matches my team’s reliability needs."
Description

Design a user interface where managers can configure the risk probability threshold for triggering alerts, select preferred notification channels, and adjust lead time. Settings must be saved per location or team and override defaults to suit different operational needs.

Acceptance Criteria
Configuring Risk Threshold
Given the manager opens the Threshold & Notification Settings Panel When the manager adjusts the risk probability slider to a value between 1% and 100% Then the slider reflects the selected percentage and the value is stored accurately
Selecting Notification Channels
Given the manager views available notification channels When the manager selects one or more channels (e.g., email, SMS, push) Then the selected channels are highlighted, saved, and used for future alerts
Adjusting Lead Time
Given the manager inputs a lead time value When the manager enters a time between 0.5 and 4 hours in 15-minute increments Then the input is validated, rounded to the nearest 15 minutes if needed, and saved as the alert lead time
Saving Settings per Location
Given the manager has multiple restaurant locations When the manager customizes threshold, channels, and lead time for a specific location and clicks Save Then the settings are stored uniquely for that location without altering other locations
Overriding Default Settings
Given default notification settings exist When the manager customizes and saves new settings for a team Then the custom settings override the defaults for that team while defaults remain unchanged elsewhere

BackupRoster Generator

Automatically generate a shortlist of available backup staff based on real-time availability and past reliability. Managers can instantly replace at-risk employees, reducing the manual effort of finding substitutes under pressure.

Requirements

Real-Time Availability Sync
"As a manager, I want to see live staff availability so that I can quickly identify who can fill a shift."
Description

The system must integrate with staff calendars and availability inputs from the mobile app and administrative interface to provide live, up-to-the-minute availability data. It should handle real-time updates via WebSockets or periodic polling, reconcile conflicting inputs, and reflect changes instantly within the BackupRoster Generator. This ensures managers have accurate information to identify potential backup candidates without manual cross-checks or delays.

Acceptance Criteria
Real-Time WebSocket Update
Given a manager is viewing the BackupRoster Generator interface When a staff member updates their availability via the mobile app and the system receives a WebSocket event Then the availability list updates within 2 seconds to reflect the change without requiring a page refresh
Polling Fallback Mechanism
Given the WebSocket connection is lost When the system switches to polling mode Then the system polls for availability updates every 60 seconds and displays the latest data within 2 seconds of retrieval
Conflict Resolution Process
Given conflicting availability inputs from the mobile app and the administrative interface When both updates arrive within the same second Then the system applies the latest timestamped update and logs the conflict resolution in the audit trail
Mobile App Availability Submission
Given a staff member submits their availability through the mobile app When the server receives the submission Then the availability data is validated, saved to the database, and broadcast via WebSocket within 1 second
Admin Override of Availability
Given a manager overrides a staff member’s availability in the administrative interface When the override is saved Then the system updates the availability data instantly in the BackupRoster Generator and notifies the staff member via mobile push notification
Reliability Score Calculation
"As a manager, I want to know which employees have a high reliability score so that I can select the most dependable backups."
Description

The system must analyze historical attendance, punctuality, and performance records to compute a reliability score for each employee. These scores should be dynamically updated after each shift based on defined metrics such as no-show frequency, shift completion quality, and peer feedback. The reliability scores will inform the BackupRoster Generator’s ranking algorithm, promoting the most dependable staff as backup options.

Acceptance Criteria
Initial Reliability Score Computation
Given an employee with at least six months of attendance, punctuality, and performance data, when the system computes the reliability score, then the score equals the weighted sum of no-show frequency (30%), punctuality rate (30%), shift completion quality (30%), and peer feedback (10%) within a 0.1% margin of error.
Post-Shift Reliability Update
Given a completed shift with recorded attendance, punctuality, performance metrics, and peer feedback, when the shift ends, then the system updates the employee’s reliability score within five minutes and logs the update event.
Backup Roster Ranking Integration
Given a list of available employees, when the BackupRoster Generator is invoked, then employees are sorted in descending order of reliability score and the top five are returned.
Data Incompleteness Handling
Given an employee missing one or more metric categories, when the system computes the reliability score, then it substitutes missing values with the median of available scores and computes a final score without errors.
Peer Feedback Incorporation
Given new peer feedback entries within the latest shift, when the system recalculates the reliability score, then it averages all peer feedback scores and applies them to the final reliability score at a 10% weight.
Backup Shortlist Generation
"As a manager, I want an automatically ranked shortlist of backup staff so that I can make quick substitution decisions."
Description

The BackupRoster Generator must automatically produce a ranked shortlist of the top available backup candidates for any at-risk shift. It should filter staff based on real-time availability, reliability score, job role compatibility, and location proximity. The shortlist should be presented in order of suitability, allowing managers to choose from a curated list instead of manually sifting through all staff.

Acceptance Criteria
Immediate Replacement for No-Show Employee
Given a shift marked as no-show, when the manager requests a backup shortlist, then the system filters staff with real-time availability for that shift, reliability score ≥ 4.0, matching job role and within 10 miles, and returns a ranked list of the top five candidates.
Last-Minute Shift Change Due to Sickness
Given a shift starting within two hours, when a manager initiates backup generation, then the system excludes staff with any scheduled shifts within two hours of the shift, verifies live availability, and presents at least three eligible candidates.
Covering High-Priority Shift During Peak Hours
Given a peak-hour shift flagged as high priority, when generating the backup shortlist, then the system ranks candidates by descending reliability score, ensuring the top candidate has reliability ≥ 4.5 and all candidates have compatibility and availability.
Single Manager Scheduling via Mobile App
Given a manager using the mobile app, when requesting a backup shortlist, then the system generates and displays the ranked list within three seconds, and each candidate entry includes name, role, reliability score, and distance.
Geographically Proximal Backup Selection
Given any at-risk shift, when generating backups, then the system filters staff located within a 10-mile radius of the shift location before applying availability and reliability filters, ensuring proximity prioritization.
Instant Notification Dispatch
"As a manager, I want the selected backup staff to receive shift change notifications instantly so that they can respond promptly."
Description

Upon confirming a backup assignment, the system should immediately send notifications via SMS, email, and in-app messages to the selected staff member. Notifications must include shift details, location, start time, and instructions for acceptance or decline. The system should track delivery statuses and alert the manager if no response is received within a predefined timeframe.

Acceptance Criteria
Backup Staff Receives Notification
Given a backup assignment is confirmed When the system sends notifications Then the staff member receives SMS, email, and in-app messages containing shift details, location, start time, and action links
Notification Delivery Failure Alert
Given a notification fails to deliver via any channel When the system detects undelivered status Then it retries delivery up to two times and logs the failure; if still undelivered, it alerts the manager
Staff Response Timeout
Given notifications have been sent When no accept or decline response is received within the predefined timeframe Then the system alerts the manager and marks the assignment as pending
Successful Acceptance Acknowledgment
Given a staff member clicks accept in any notification channel When the system processes the acceptance Then it updates the roster, notifies the manager of confirmation, and cancels further reminders
Staff Declines Notification
Given a staff member clicks decline in any notification channel When the system processes the decline Then it updates the assignment status, notifies the manager with decline reason, and triggers the next backup suggestion
Manager Override & Confirmation
"As a manager, I want to override the suggested backup roster and choose a different staff member so that I maintain control in special cases."
Description

Managers must have the ability to override algorithm-generated backup suggestions, manually selecting alternative staff members when necessary. The interface should support drag-and-drop roster adjustments and require managerial confirmation before finalizing backup assignments. All manual changes should be logged for audit and reporting.

Acceptance Criteria
Override AI-Generated Suggestions
Given the backup roster suggestions are displayed, when the manager selects an alternative staff member, then the system replaces the AI recommendation with the selected staff and highlights the manual override.
Drag-and-Drop Roster Adjustment
Given the manager views the backup roster grid, when they drag a staff name from the available list onto a shift slot, then the roster updates immediately and displays a visual confirmation of the change.
Manager Confirmation Prompt
Given one or more manual overrides have been made, when the manager clicks finalize, then a confirmation dialog appears summarizing all changes and requires the manager to confirm before saving.
Audit Logging of Manual Overrides
Given any manual change is confirmed, when the roster is finalized, then the system logs the manager’s ID, timestamp, original AI suggestion, and the manually selected staff member.
Bulk Override Workflow
Given multiple at-risk shifts are listed, when the manager selects multiple staff replacements and applies them in a single action, then all changes are processed together and each change is logged individually.

QuickFill Broadcast

Send a one-click broadcast to pre-qualified on-call or part-time staff via SMS or in-app push notification. Eligible employees can claim open shifts instantly, cutting down fill times from hours to minutes.

Requirements

Targeted Broadcast List
"As a manager, I want the system to select and send broadcasts only to staff who meet specific availability and skill criteria so that I can quickly fill open shifts without bothering those who are unavailable or unqualified."
Description

Automatically generate a list of pre-qualified on-call or part-time staff based on availability, skills, and proximity criteria. Integrates with the existing staff database and AI engine to ensure only relevant employees receive shift broadcasts, reducing noise and improving fill rates.

Acceptance Criteria
Qualified Staff List Generation
Given a manager selects a shift and initiates a targeted broadcast, When the system generates the list, Then it includes only staff who are marked 'Available', have the required skills, and are within the defined proximity radius.
Skill-Based Filtering
Given a shift requiring a specific skill set, When the target list is generated, Then only employees with matching skill tags are included in the broadcast recipients.
Availability Exclusion of Unavailable Staff
Given an employee’s status is set to 'Unavailable' for the shift date, When the broadcast list is generated, Then that employee is excluded from the list of recipients.
Proximity Prioritization
Given multiple eligible employees are available, When the system orders the target list, Then employees are sorted by ascending distance from the store location.
Database Integration Accuracy
Given recent updates to staff availability, skills, or location in the database, When generating the broadcast list, Then the system uses the latest data and reflects all changes accurately.
One-Click Shift Claim
"As a part-time employee, I want to claim available shifts with one click so that I can secure extra hours quickly and managers see my acceptance immediately."
Description

Enable employees to claim an open shift with a single tap from an SMS or in-app push notification. The interface confirms eligibility, logs the claim instantly, and updates the schedule in real time, minimizing lag and errors in roster updates.

Acceptance Criteria
SMS Notification Claim by Eligible Employee
Given an eligible employee receives an SMS notification for an open shift, when the employee taps the one-click claim link, then the system must log the claim, send an SMS confirmation within 5 seconds, and update the central schedule in real time without errors.
In-App Push Claim by Eligible Employee
Given an eligible employee receives an in-app push notification for an open shift, when the employee taps the 'Claim Shift' button in the app, then the system must record the claim immediately, display a success message in the app, and refresh the shift roster view within 3 seconds.
Claim Attempt by Ineligible Employee
Given an employee not pre-qualified for a shift receives an SMS or in-app notification, when the employee taps the claim link or button, then the system must display an ‘Ineligible to Claim’ message and prevent the shift from being assigned to that employee.
Concurrent Claim Resolution
Given multiple eligible employees tap the claim link for the same shift within 10 seconds, when the first claim is received, then the system must assign the shift to the first responder, reject subsequent claims with a ‘Shift Already Claimed’ notification, and keep the schedule consistent.
Real-Time Schedule Update Visible to Manager
Given a shift is claimed by an employee via SMS or in-app notification, when the claim is processed, then the manager’s schedule view must reflect the updated assignment within 5 seconds and log the change in the audit trail.
Real-time Availability Sync
"As an employee, I want my updated availability and time-off requests to be reflected in shift broadcasts instantly so that I don’t receive offers for shifts I can’t work."
Description

Continuously sync employee availability and preferences from their profiles into the broadcast system. Any changes in availability (like approved time off) immediately update the eligible broadcast list to prevent sending shift offers to unavailable staff.

Acceptance Criteria
Availability Change Propagation
Given an employee updates their availability in their profile When the profile update is saved Then the QuickFill Broadcast eligible list reflects this change within 60 seconds
Time-Off Approval Integration
Given a manager approves or denies a time-off request When the decision is recorded in the system Then the employee is immediately added or removed from the eligible broadcast list
Bulk Availability Import
Given an external bulk import of updated availability data When the import process completes Then the system refreshes the broadcast eligibility list to include only employees with current availability
Network Recovery Synchronization
Given the system experiences a network outage While availability updates are pending When connectivity is restored Then all pending updates are automatically synced to the broadcast system without manual intervention
Duplicate Notification Prevention
Given an employee toggles their availability status multiple times in quick succession When the broadcast list is updated Then only the final availability state is used to generate shift offers, preventing duplicate notifications
Notification Delivery Optimization
"As a manager, I want the system to choose the best notification channel for each employee so that shift offers reach them promptly and reliably."
Description

Implement multi-channel delivery logic that selects SMS or in-app push based on employee preferences and historical response rates. Track delivery success and adjust methods dynamically to maximize open rates and reduce delivery failures.

Acceptance Criteria
Preferred Channel Selection
Given an employee has SMS as their preferred notification channel, When a QuickFill broadcast is initiated, Then the system sends the notification via SMS and does not send an in-app push notification.
Delivery Method Adjustment Based on Response Rates
Given the historical response rate for SMS falls below the defined threshold, When preparing a broadcast to employees without explicit channel preferences, Then the system prioritizes in-app push notifications over SMS.
Delivery Success Tracking
Given a QuickFill broadcast is sent via selected channels, When delivery statuses are returned, Then the system logs each employee’s delivery result (delivered or failed) and timestamp within five minutes.
Dynamic Channel Switching
Given a notification fails to deliver via the primary channel for an employee, When the failure is detected, Then the system automatically retries delivery using the employee’s secondary channel within two minutes.
Failover to Alternative Channel
Given both SMS and in-app push notifications fail for an employee, When both retries are unsuccessful, Then the system sends an alert to the manager with the employee’s contact and failure details within five minutes.
Shift Claim Confirmation Workflow
"As a manager, I want to be notified immediately when a shift is filled and for the offer to be retracted from other staff so that I have an accurate and current schedule without manual oversight."
Description

Provide a confirmation workflow where once a shift is claimed, other recipients receive an automatic update removing the slot, and the manager gets notified of the filled position. Include fallback messaging if the first claim fails or is not confirmed within a set timeframe.

Acceptance Criteria
Eligible staff receive shift broadcast
Given a manager initiates a QuickFill Broadcast, When the system identifies pre-qualified on-call or part-time staff, Then it sends both SMS and in-app push notifications to all eligible employees within 30 seconds.
Staff claims open shift
Given an eligible employee has received the broadcast notification, When the employee clicks the 'Claim Shift' link, Then the system locks the shift for that employee and displays a pending confirmation status.
Shift claim confirmation and update
Given an employee has reserved a shift, When the system confirms the claim within 2 minutes, Then it marks the shift as filled and removes it from other employees’ broadcast lists.
Manager receives filled position notification
Given a shift has been confirmed as filled, When the system updates the shift status, Then the manager receives an email and an in-app notification within 1 minute of confirmation.
Fallback messaging if claim fails
Given a shift remains unconfirmed after 2 minutes or a reservation error occurs, When the timeout or error triggers, Then the system sends a fallback broadcast to the next eligible employee and notifies the manager of the failed claim.

Predictive Heatmap

Visualize no-show risk across daily and weekly schedules with an intuitive color-coded heatmap. Spot high-risk shifts at a glance and proactively adjust staffing levels to maintain optimal coverage.

Requirements

Heatmap Data Computation Engine
"As a manager, I want the system to calculate no-show risk scores for each shift so that I can identify problematic time slots before finalizing schedules."
Description

Aggregate historical attendance and scheduling data to calculate no-show risk probabilities for each shift slot. Leverage machine learning models to ingest past roster changes, no-show incidents, and relevant patterns, outputting a risk score per shift. Ensure data accuracy, periodic re-training, and scalable computation to support daily and weekly schedule views without performance degradation.

Acceptance Criteria
Generating Heatmap for Daily Schedule
Given a restaurant manager requests the daily heatmap for a specific date When the Heatmap Data Computation Engine processes the historical attendance and scheduling data for that date Then each shift slot displays a risk score between 0 and 1 And the system renders the color-coded heatmap within 2 seconds without errors
Updating Heatmap after New Attendance Data
Given new attendance records are available by 00:00 each day When the data ingestion pipeline runs before heatmap generation Then the new records are incorporated into the risk computation And the next daily heatmap reflects updated risk probabilities
Retraining Model Periodically without Performance Degradation
Given the monthly retraining schedule is triggered When the machine learning model retrains on the latest six months of data Then accuracy and precision metrics meet or exceed 95% of the baseline performance And the end-to-end heatmap computation time remains under the defined SLA of 5 seconds per request
Computing Heatmap for Weekly Schedule
Given a manager requests the weekly heatmap view (7 days) When the engine aggregates data and computes risk scores for all shift slots across the week Then the system generates and displays the complete weekly heatmap within 5 seconds And no shift slot is missing a risk score
Handling Missing or Corrupt Data in Heatmap Calculation
Given historical attendance data contains missing or corrupt entries When the computation engine applies default imputation or exclusion rules Then every shift slot still receives a computed risk score And the process completes without runtime exceptions
Heatmap Visualization UI
"As a manager, I want to see a color-coded heatmap over my schedule so that I can quickly spot shifts with high no-show risk."
Description

Overlay a color-coded heatmap atop the scheduling interface to visually represent no-show risk levels across days and shifts. Include an intuitive legend, responsive design for desktop and mobile, and ensure accessibility through clear color contrast. Support toggling between daily and weekly views and dynamically adjust to screen size and user interactions.

Acceptance Criteria
Desktop Daily View Heatmap Rendering
Given the manager is viewing the schedule on a desktop browser and heatmap mode is activated for daily view, when the page loads, then each shift cell is overlaid with a color corresponding to its no-show risk level as defined in the legend.
Mobile Weekly View Accessibility
Given the manager is using a mobile device and toggles to weekly heatmap view, when the heatmap displays, then all shift cells are visible without horizontal scrolling and maintain a minimum 4.5:1 color contrast ratio.
Legend Interpretation and Toggling
Given the heatmap legend icon is visible in the schedule header, when the manager clicks or taps the icon, then a clearly labeled legend opens, and when the legend is closed, it hides without overlapping schedule content.
Real-Time Heatmap Data Updates
Given shift risk levels change on the backend, when the manager triggers a data refresh or makes a schedule adjustment, then the heatmap updates to reflect new risk values within 2 seconds without requiring a full page reload.
Responsive Layout on Window Resize
Given the manager resizes the browser window or rotates the mobile device, when the viewport crosses defined breakpoints, then the heatmap tiles resize and reflow to fit the new dimensions without clipping or hidden content.
Customizable Risk Threshold Manager
"As a manager, I want to set my own risk thresholds so that the heatmap colors align with my team's tolerance levels."
Description

Provide a settings module that allows managers to define and adjust thresholds mapping risk probabilities to specific colors and labels (e.g., low, medium, high). Reflect changes immediately in the heatmap, enabling tailored risk definitions per outlet. Support default presets and save custom configurations at the organization or location level.

Acceptance Criteria
Accessing Risk Threshold Settings
Given the manager opens the Customizable Risk Threshold Manager module When the manager adjusts the low, medium, or high probability sliders Then the new thresholds are reflected immediately in the UI and correspond to default colors and labels
Saving Custom Threshold Preset
Given the manager configures custom probability ranges, colors, and labels When the manager clicks “Save” for a location or organization Then the new preset is stored and appears in the presets dropdown and is applied to the heatmap
Resetting to Default Presets
Given a custom configuration is active When the manager selects “Reset to Defaults” Then all thresholds revert to the original system presets and the heatmap updates to reflect default colors and labels
Organization-Level Configuration Override
Given an organization-level admin sets global risk thresholds and no location-specific preset exists When any location manager views the heatmap Then the organization-level thresholds and colors are applied by default
Real-Time Heatmap Update on Threshold Change
Given the manager modifies any risk probability value and confirms the change When the thresholds are updated Then all visible heatmap cells refresh their colors instantly without page reload
Interactive Shift Insight Drill-down
"As a manager, I want to click on a heatmap cell to see detailed no-show metrics so that I can make informed staffing decisions."
Description

Enable users to click or tap on a heatmap cell to reveal detailed metrics for that specific shift, including underlying data points like historical no-show counts, recent schedule changes, and predictive model confidence. Present the drill-down in a modal or sidebar without leaving the scheduling interface, providing actionable insights for proactive adjustments.

Acceptance Criteria
Accessing Shift Details from Heatmap
Given the user views the predictive heatmap, when they click or tap on a specific shift cell, then a modal or sidebar opens displaying detailed metrics for that shift.
Displaying Historical No-show Data
Given the drill-down view is open, then the interface shows historical no-show counts for the selected shift over the past 30 days.
Showing Recent Schedule Changes
Given the drill-down view is open, then the interface lists any schedule changes affecting the selected shift within the last 7 days.
Presenting Predictive Model Confidence
Given the drill-down view is open, then a confidence score from the predictive model is displayed with a description of its significance.
Ensuring Interface Responsiveness
Given the user requests the drill-down view, then the detailed metrics modal or sidebar loads within 2 seconds without navigating away from the scheduling interface.
Real-time Heatmap Data Sync Service
"As a manager, I want the heatmap to update in real time when schedules change so that I always see the current risk profile."
Description

Ensure the heatmap reflects the latest schedule changes, new attendance records, or updated model outputs in real time. Implement a subscription-based update mechanism (e.g., websockets or long polling) to push incremental data to the front end, maintaining synchronization without manual refresh. Prioritize low latency and reliability for timely risk visibility.

Acceptance Criteria
Initial Heatmap Load on Dashboard Access
Given the manager navigates to the heatmap dashboard, when the page loads, then the heatmap must display all shift risk data within 2 seconds.
Live Update on Schedule Change
Given a schedule change is saved by the manager, when the backend processes the change, then the frontend must receive and apply the updated heatmap data via the subscription channel within 1 second.
Automatic Refresh on Attendance Record Submission
Given an attendance record is submitted or updated, when the server acknowledges the update, then the heatmap cell reflecting that shift’s no-show risk must update automatically without a manual page refresh.
Real-Time Update on Model Output Change
Given the AI model outputs new risk scores for upcoming shifts, when the output is available, then the heatmap colors must update for affected cells within 500 milliseconds.
Recovery After Connection Interruption
Given the subscription connection drops, when the client reconnects, then it must resubscribe automatically and fetch any missed incremental updates so the heatmap state is fully consistent.
Exportable Heatmap Report Generation
"As a manager, I want to export the heatmap as a PDF so that I can share it with stakeholders in meetings."
Description

Allow managers to export the current heatmap view as a high-resolution PDF or image file for sharing in meetings or archiving. Include the legend, timestamp, and optional annotations. Provide export settings to choose date ranges and orientation (landscape/portrait), ensuring portability and presentation-ready outputs.

Acceptance Criteria
Export Full-Day Heatmap Report
Given the manager is viewing today's heatmap When they select the 'Export' option and choose PDF format Then a high-resolution PDF of the full-day heatmap is generated, automatically downloaded, and includes the legend and timestamp
Export Custom Date Range Heatmap Report
Given the manager selects a start and end date in the export settings When they click 'Generate Report' Then the system produces a PDF covering only the specified date range with accurate heatmap data
Include Annotations in Exported Report
Given the manager adds text or graphical annotations to the heatmap view When they choose 'Include Annotations' and export the report Then all annotations appear correctly positioned in the exported file
Select Export Orientation
Given the manager opens export settings and toggles between landscape and portrait modes When they confirm the orientation and export Then the resulting PDF respects the selected orientation without cropping or distortion
Verify Legend and Timestamp in Export
Given any export action is initiated When the file download completes Then the export includes a visible legend section and a timestamp reflecting the exact export moment

SmartSwap Suggestion

Leverage AI-driven recommendations for swap partners who match availability and skills when a high-risk no-show is detected. This ensures swaps are compliant, fair, and maintain operational balance without manager intervention.

Requirements

Real-time No-Show Risk Detection
"As a manager, I want the system to detect high-risk no-show shifts in real time so that I can proactively address potential coverage gaps before they occur."
Description

Implement continuous monitoring of scheduled shifts using machine learning algorithms to calculate no-show risk scores in real time. The system evaluates historical attendance data, shift patterns, and individual employee behavior to identify high-risk no-show scenarios, enabling proactive intervention and reducing last-minute coverage gaps.

Acceptance Criteria
Detect High-Risk No-Show Before Shift Start
Given a scheduled shift is set to begin in 2 hours and historical attendance data exists for the assigned employee, when the machine learning model calculates a no-show risk score above 80%, then the system must flag the shift as "High-Risk No-Show" in the manager dashboard.
Update Risk Score After Employee Behavior Change
Given an employee’s availability, swap requests, or attendance status is updated, when the system processes the change, then it must recalculate the no-show risk score within 5 minutes and reflect the new score in the shift details view.
Real-Time Alert for Manager on High-Risk No-Show
Given a shift’s risk score transitions from medium to high (threshold >80%), when the new score is recorded, then the system must send an alert notification to the manager’s mobile app and email within 1 minute of detection.
Threshold-Based Risk Escalation
Given a shift’s no-show risk score crosses predefined thresholds (low: <50%, medium: 50–80%, high: >80%), when the score moves from one category to another, then the system must update the shift status category and log the threshold crossing event in the audit records.
Risk Score Adjustment Based on Shift Patterns
Given an employee has multiple no-shows in the same shift pattern over the past month, when the pattern is identified by the model, then the system must increase the base risk score by 10% for each repeated occurrence and apply the adjusted score to subsequent shifts automatically.
AI-Powered Swap Partner Matching
"As a staff member, I want the system to suggest eligible swap partners based on availability and skill set so that I can quickly find a valid replacement."
Description

Develop an AI-driven matching engine that recommends optimal swap partners by analyzing employee availability, skill set compatibility, and shift requirements. The engine ranks potential candidates based on match quality, fairness, and predicted acceptance likelihood, ensuring managers receive the best swap options instantly.

Acceptance Criteria
Emergency Shift Swap
Given an employee flags their upcoming shift as high-risk for no-show due to an emergency When the AI engine runs matching analysis Then it returns a ranked list of at least 3 potential swap partners who are available and possess the required skills within 5 seconds And each partner's predicted acceptance probability is displayed.
Skill Compliance Validation
Given a potential swap partner is suggested When the partner lacks the required certification or skill for the shift Then the system excludes this partner from the suggestions list.
Fairness Adjustment Check
Given multiple eligible swap partners exist When generating suggestions Then the system ensures no employee is suggested in more than 2 consecutive swap requests to maintain fairness.
Acceptance Likelihood Accuracy
Given historical acceptance data is available When computing predicted acceptance likelihood Then the model's predictions must have at least 85% accuracy compared to actual outcomes over a test data set.
Performance under Load
Given a roster of 200 employees and 50 simultaneous swap requests When the AI engine processes requests Then the average response time per request must remain under 2 seconds.
Compliance and Fairness Constraints
"As a compliance officer, I want the swap suggestions to respect labor rules and fairness policies so that we maintain legal compliance and equitable shift distribution."
Description

Integrate regulatory and organizational rules into the swap suggestion logic, enforcing labor laws, maximum working hours, rest period requirements, and equitable shift distribution. The system ensures all recommended swaps adhere to compliance policies and internal fairness guidelines, preventing violations and ensuring balanced workloads.

Acceptance Criteria
Overtime Limit Enforcement
Given two employees consent to a shift swap When the swap is evaluated Then neither employee’s total scheduled hours for the week exceeds 40 hours
Mandatory Rest Period Adherence
Given an employee finishes a late evening shift When suggesting a replacement shift Then the system ensures at least 12 hours of rest before the next scheduled shift
Equitable Shift Distribution
Given multiple eligible swap candidates When generating swap recommendations Then the system balances the number of high-demand weekend and evening shifts among employees within a two-week window
Labor Law Compliance Across Jurisdictions
Given employees work in different jurisdictions When suggesting swaps Then the system applies each jurisdiction’s labor regulations and rejects any swap that violates local working time or break requirements
Consecutive Working Days Limit
Given an employee has already worked six consecutive days When considering a shift swap Then the system prohibits any additional swap that would extend the consecutive workday count beyond six days
Dynamic Availability & Skill Filtering
"As a staff member, I want the system to filter swap suggestions by my availability and certifications so that I only see swaps I can accept and perform."
Description

Enable advanced filtering options that restrict swap suggestions to employees who meet specific availability windows, certifications, and skill-level requirements. The feature dynamically updates candidate lists as availability changes, ensuring swap partners are qualified and ready to work the requested shift.

Acceptance Criteria
Availability Window Filtering
Given a swap request for a shift from 09:00 to 13:00 on 2025-06-10 requiring 'Barista' skill, when generating SmartSwap suggestions, then only employees with availability covering 09:00-13:00 on 2025-06-10 are included in the candidate list.
Certification and Skill Match
Given a swap request requiring 'Certified Bartender' certification, when filtering candidate employees, then all suggested employees must possess the 'Certified Bartender' certification before appearing in the SmartSwap suggestions.
Real-time Availability Update
Given an employee updates their availability status after the initial SmartSwap suggestion list is generated, when the availability change is detected, then the SmartSwap suggestions list must refresh within 5 seconds to reflect the updated availability.
Shift Duration Compatibility
Given a shift duration of 4 hours, when suggesting swap candidates, then only employees whose maximum consecutive working hours setting is equal to or greater than 4 hours are included in the suggestions.
Maximum Work Hour Compliance
Given a candidate employee has already been scheduled for 38 hours in the current week, when calculating SmartSwap suggestions for a 10-hour shift, then any employee exceeding a total of 40 weekly hours after the swap must be excluded from the suggestions.
Automated Notification & Confirmation Workflow
"As a manager, I want automated notifications and confirmations for swap proposals so that everyone is informed and the swap is finalized without manual intervention."
Description

Implement an end-to-end notification system that automatically alerts both original and replacement staff via mobile and email when a swap suggestion is generated. Include built-in confirmation prompts, deadlines, and escalation paths, ensuring timely acceptance or rejection and updating the schedule once the swap is finalized without manual oversight.

Acceptance Criteria
Swap Suggestion Notification Dispatch
Given the AI engine generates a swap suggestion for a high-risk no-show shift When the suggestion is saved Then within 1 minute mobile and email notifications are dispatched to both the original employee and proposed replacement containing shift details, colleague name, accept/reject links, and a clear response deadline.
Confirmation Prompt Display
Given a notified employee opens the notification When the notification loads Then a confirmation prompt displays accept and reject options, shift details, peer information, and a countdown timer showing time remaining to respond.
Response Deadline and Escalation
Given an employee has not responded by the deadline When the 30-minute reminder threshold is reached Then the system sends a reminder notification to the non-responsive employee; And if no response within 2 hours, escalates to the manager via mobile alert and logs the escalation.
Automatic Schedule Update Upon Swap Confirmation
Given both employees accept the swap within the deadline When the second acceptance is recorded Then the schedule is automatically updated to reflect the swap, both employees receive confirmation notifications, and the swap is marked as finalized in the system logs.
Notification Retry and Escalation
Given a notification attempt fails When the system detects the failure Then it retries sending up to two additional times at 5-minute intervals; And if all retries fail, sends an alert to the manager through an alternative channel and records all retry attempts and outcomes in the audit log.

Attendance Insights

Access a dashboard of aggregated attendance analytics, highlighting recurring no-show patterns by employee and shift. Use these insights to address underlying issues, improve scheduling accuracy, and reduce future gaps.

Requirements

Data Aggregation Engine
"As a restaurant manager, I want the system to automatically gather and unify all attendance data from various entry points so that I can trust the accuracy of attendance insights without manual effort."
Description

Develop a backend engine that collects and consolidates attendance records from multiple sources—time clocks, mobile check-ins, and manual entries—into a unified dataset. This engine must normalize disparate data formats, handle time zone differences, automatically update with new records in real time, and ensure high data integrity. By providing a reliable foundation of accurate attendance data, the engine enables meaningful analytics and reduces manual reconciliation efforts.

Acceptance Criteria
Multi-Source Data Collection
Given multiple attendance data sources (time clocks, mobile check-ins, manual entries), when the engine runs, then it must ingest records from all sources within 5 seconds without data loss.
Data Format Normalization
Given raw records in various formats (CSV, JSON, XML), when processed, then all records are converted to the standard schema with consistent field names and data types, with 100% accuracy.
Time Zone Handling
Given records from different time zones, when normalized, then timestamps are converted to UTC correctly, preserving the original local time, verified with sample data from at least 3 time zones.
Real-Time Data Updates
Given new attendance records arrive after initial ingestion, when they are generated, then the engine must update the unified dataset within 2 seconds and reflect in the analytics dashboard without manual intervention.
Data Integrity Validation
Given the consolidated dataset, when performing integrity checks, then there are no duplicate entries, missing required fields, or timestamp inconsistencies, with 0 errors logged for a test batch of 10,000 records.
No-Show Pattern Detection
"As a store manager, I want the system to highlight employees with consistent no-show patterns so that I can adjust future rosters and intervene with those team members."
Description

Implement an AI-driven module that analyzes historical attendance data to identify recurring no-show tendencies by employee, shift type, and timeframe. The module should surface statistically significant patterns—such as employees missing every Friday evening or spikes in absences during certain weeks—and attribute confidence scores to each pattern. These insights help managers proactively adjust schedules and address underlying attendance issues.

Acceptance Criteria
Friday Evening Absence Detection
Given historical attendance data for Friday evening shifts over the last three months, when the AI module runs pattern detection, then any employee with at least three missed Friday evening shifts shall be identified and the pattern displayed with a confidence score of 80% or higher.
Midweek Shift No-Show Spike Analysis
Given attendance logs for Tuesday through Thursday shifts over the past quarter, when the analysis is executed, then any week with a no-show rate increase of more than 30% compared to the previous week shall be flagged and surfaced to the manager with a confidence score of at least 75%.
Chronic Employee No-Show Identification
Given six months of employee attendance records, when the AI-driven module analyzes no-show rates, then employees with an overall no-show rate greater than 20% across all shift types shall be flagged and the recurring pattern reported.
Shift Type-Specific No-Show Patterns
Given categorized shift types (morning, afternoon, evening) and historical attendance data, when the module processes each category, then any no-show pattern for a specific shift type occurring in at least 25% of that shift’s occurrences shall be identified and assigned a confidence score of 70% or higher.
Seasonal Absence Trend Reporting
Given defined seasonal periods (e.g., holidays, peak weeks) and corresponding attendance data, when pattern detection is run, then the system shall report any statistically significant absence trends during these periods (p-value < 0.05) along with confidence scores.
Interactive Attendance Dashboard
"As a retail manager, I want a visual dashboard that shows attendance trends and allows me to filter by date and location so that I can pinpoint problematic shifts and improve scheduling accuracy."
Description

Design and build a responsive dashboard interface that visualizes aggregated attendance metrics, including overall no-show rates, trend graphs, heat maps of problematic shifts, and employee-level attendance profiles. The dashboard must allow filtering by date range, location, and employee segment, and support drill-downs from summary views into detailed daily logs. Its intuitive UI will empower managers to explore insights quickly and make data-driven scheduling decisions.

Acceptance Criteria
Dashboard Overview of No-Show Rates
Given the manager navigates to the attendance dashboard, when data loads, then the overall no-show rate is displayed as a percentage and matches the last 30 days of attendance records.
Date Range Filtering
Given the manager selects a custom date range from the date picker, when the filter is applied, then the dashboard updates all metrics to reflect data within the selected range.
Drill-Down to Daily Logs
Given the manager clicks on a data point in the trend graph, when the drill-down is activated, then a list of daily logs for the selected date is displayed with employee names and attendance status.
Employee Segment Filtering
Given the manager selects an employee segment from the segment dropdown, when the filter is applied, then the dashboard only shows metrics for employees within the selected segment.
Heat Map Identification
Given the dashboard displays a shift heat map, when the manager hovers over a cell, then the specific shift details and no-show count are shown in a tooltip, and the cells with the top 5% no-show rates are highlighted in red.
Alert & Notification System
"As a manager, I want to receive notifications when no-show rates exceed predefined thresholds so that I can respond quickly and minimize staffing disruptions."
Description

Create a configurable notification system that sends automated alerts to managers when the attendance insights engine detects emerging no-show patterns or threshold breaches (e.g., a 20% increase in absences for a given shift). Notifications should be deliverable via email, SMS, or in-app messages, including a summary of the detected issue and a link to the relevant dashboard view. This feature ensures timely manager awareness and enables swift corrective actions.

Acceptance Criteria
Threshold Breach Notification
Given a configured no-show threshold of 20%, when the attendance insights engine detects that absences for a specific shift exceed this threshold over a rolling 7-day period, then the system sends an automated notification via email, SMS, or in-app message to the manager within 5 minutes, including a summary of the breach and a direct link to the related dashboard section.
Channel Preference Delivery
Given a manager has selected preferred notification channels in their profile settings, when an alert is triggered, then the notification is delivered only via the selected channels within 5 minutes of detection.
Notification Content Accuracy
Given an alert is generated for a no-show pattern, when the notification is sent, then it includes the date range analyzed, percentage increase in no-shows, affected shift details (location, time), and a valid URL link to the corresponding dashboard view.
Fallback Channel Handling
Given the primary notification channel fails (e.g., email bounce or SMS delivery error), when an alert is triggered, then the system automatically retries delivery via the next configured channel and logs the failure and retry attempts in the notifications audit log.
In-App Notification Visibility
Given a manager logs into the application, when there are unread notifications related to attendance threshold breaches, then a badge appears on the notifications icon and the in-app message list displays the new alerts in descending chronological order.
Audit Logging of Alerts
When any notification (email, SMS, or in-app) is sent or fails, then the system records an audit entry with timestamp, recipient manager ID, channel used, notification content summary, and delivery status.
Export & Reporting Capability
"As a regional manager, I want to export attendance reports and schedule automatic deliveries so that I can share performance data with leadership and support compliance audits."
Description

Provide functionality to export attendance insights and underlying data into common formats (CSV, PDF) for external reporting, compliance, or deeper analysis. Exports should respect dashboard filters and include key metrics, visualizations, and raw event logs. Additionally, schedule automated report generation—daily, weekly, or monthly—delivered to designated stakeholders to keep leadership informed of attendance performance.

Acceptance Criteria
Export Filtered Attendance Data to CSV
Given a user has applied filters on the attendance insights dashboard, when the user clicks the 'Export CSV' button, then the system generates a CSV file containing only the filtered attendance records, including employee names, shift dates, and attendance status, and prompts the user to download the file within 5 seconds.
Export Attendance Insights to PDF
Given a user on the attendance insights dashboard, when the user selects 'Export PDF', then the system generates a PDF report combining key metrics (no-show rate, attendance trends) and associated visualizations, formatted according to the dashboard view, and provides a download link.
Include Raw Event Logs in Data Export
Given a user has access to raw attendance event logs, when the user requests export with raw data included, then the export file (CSV or PDF) must contain a section listing all raw events with timestamps, employee IDs, event types, and associated shift IDs.
Schedule Automated Weekly Reports
Given an administrator configures a weekly automated report schedule and selects stakeholder email addresses, when the scheduled time arrives, then the system automatically generates the latest attendance insights report (in PDF and CSV), attaches it to an email, and sends it to all designated stakeholders with a delivery confirmation logged in the system.
Respect Dashboard Filters in Scheduled Reports
Given an administrator has applied specific filters on the attendance insights dashboard and set up an automated report, when the scheduled report is generated, then the report reflects exactly the active filters from the dashboard at schedule time, and no other data outside those filters is included.

CertiMatch Engine

Automatically matches staff certifications and real-time availability to open shifts, ensuring every assignment meets compliance requirements. This reduces manual vetting time, prevents unqualified scheduling, and delivers confident, error-free rosters.

Requirements

Certification Data Integration
"As a restaurant manager, I want all employee certifications automatically imported and updated so that the shift scheduler can only assign qualified staff to eligible shifts without manual verification."
Description

Integrate and synchronize staff certification records from HR and external training systems into the CertiMatch Engine, ensuring up-to-date, accurate qualification data for all employees. This module will support importing diverse certification formats, automate regular updates, and provide a centralized repository for querying staff credentials during shift matching.

Acceptance Criteria
Initial Import of Staff Certification Records
Given valid HR and external training system credentials are configured, When the initial import process is executed, Then 100% of staff certification records from both sources are imported into the central repository within 10 minutes with zero data loss.
Daily Automatic Synchronization of Certification Data
Given existing certification records in the repository, When the scheduled daily sync job runs, Then updates, new certifications, and expirations are accurately reflected in the system within 30 minutes of job completion.
On-Demand Query of Employee Certifications for Shift Matching
Given a shift assignment request in the CertiMatch Engine, When the system queries the certification repository for a specific employee, Then the correct and up-to-date certification status is returned in under 2 seconds.
Handling of Unsupported Certification File Formats
Given an input file in an unsupported format, When the import process attempts to process the file, Then the system logs a clear error message, rejects the file without interruption to other imports, and notifies the administrator within 5 minutes.
Error Recovery for Failed Certification Data Sync
Given a failure during synchronization (e.g., network drop or API timeout), When retry logic is triggered, Then the system retries up to 3 times, logs each attempt, and if still failing, escalates the issue via alert and marks the sync job as 'Failed'.
Real-time Availability Sync
"As a shift manager, I want the engine to use live availability updates so that I don’t accidentally assign a shift to someone who is unavailable."
Description

Implement a real-time availability synchronization service that collects staff availability changes from the mobile app and calendar integrations. This service will provide up-to-the-minute availability data to the CertiMatch Engine, preventing scheduling conflicts and ensuring that matched staff are actually able to work the assigned shifts.

Acceptance Criteria
Updating Availability via Mobile App
Given a staff member updates their availability in the mobile app, when the update is submitted, then the real-time availability sync service reflects the change in the central database within 30 seconds.
Calendar Integration Availability Change
Given a staff member updates their Google or Outlook calendar, when the calendar integration detects the change, then the availability sync service updates the staff’s availability data in the system within 2 minutes.
Handling Network Failures
Given intermittent network connectivity on the mobile device, when the staff member updates availability, then the sync service queues the update locally and retries transmission every 60 seconds until successful, ensuring no data loss.
Notification on Sync Failure
Given the sync service fails to update availability after three consecutive retries, when the failure threshold is reached, then the system generates an alert to the staff member and logs an error in the monitoring dashboard.
Concurrent Updates Conflict Resolution
Given multiple availability updates are submitted simultaneously from different devices, when the sync service processes the updates, then it applies the update with the latest timestamp and logs any conflicting attempts for audit.
Compliance Validation Module
"As a compliance officer, I want every scheduled shift to be automatically checked against labor and certification rules so that our business remains compliant and audit-ready."
Description

Develop a compliance validation module that enforces business and legal rules—such as mandatory rest periods, maximum working hours, and certification expiration—when matching staff to shifts. The module will run rule checks in real time and flag or reject any assignment that violates compliance requirements, providing explanatory feedback to the scheduler.

Acceptance Criteria
Validating Maximum Working Hours Compliance
Given a staff member has accumulated 35 working hours this week; When a manager attempts to assign a shift that pushes total weekly hours above the 40-hour legal limit; Then the system must reject the assignment and display an error message stating "Maximum weekly working hours exceeded".
Enforcing Mandatory Rest Periods Between Shifts
Given a staff member’s last scheduled shift ends at 10:00 PM; When a manager attempts to schedule another shift starting at 6:00 AM the next day (less than the required 12-hour rest period); Then the system must reject the assignment and provide feedback: "Insufficient mandatory rest period between shifts".
Verifying Certification Status Before Assignment
Given a shift requires a valid food safety certification; When a manager selects a staff member with an expired certification date; Then the system must prevent the assignment and display: "Certification expired – please select a qualified staff member".
Real-Time Feedback for Non-Compliant Assignment
Given a manager is scheduling a shift in the UI; When any compliance rule (hours cap, rest period, or certification) is violated during selection; Then the system must immediately flag the specific violation inline and prevent saving until resolved.
Batch Schedule Validation for Multiple Shifts
Given a CSV upload of multiple shift assignments for the coming week; When the manager runs the compliance validation; Then the system must process all records, generate a report listing each compliant assignment and detailed reasons for any rejections, and allow the manager to download the results.
Adaptive Matching Algorithm
"As a store manager, I want the system to recommend the best-qualified and most reliable staff for each shift so that my team productivity and coverage stability improve."
Description

Enhance the CertiMatch matching algorithm to prioritize candidates based on certification relevance, performance metrics, and historical shift adherence. This adaptive engine will weight factors dynamically, learn from past scheduling outcomes, and optimize match rankings to improve roster quality and reduce no-shows over time.

Acceptance Criteria
Assigning certified staff to a high-volume event shift
Given an open shift requiring Food Safety certification and a performance rating threshold, when the adaptive matching algorithm runs, then the top 3 candidates selected must have valid certification, a performance rating above 4.5, and zero adherence breaches in the last 30 days.
Re-ranking candidates after certification status change
When a staff member’s certification is added or renewed, then the algorithm updates that staff member’s match ranking within 5 minutes, moving them into the top 5 eligible candidates for relevant future shifts.
Adjusting match priorities post no-show
Given a candidate with a no-show recorded in the past 7 days, when generating new shift matches, then the algorithm applies a 20% de-prioritization weight to that candidate, ensuring they do not appear in the top 3 matches for the next week.
Learning from historical performance to optimize rosters
Given four weeks of historical scheduling data, when producing the upcoming weekly roster, then the algorithm’s adaptive weighting must achieve at least a 5% reduction in predicted no-shows compared to the previous week’s model.
Ensuring compliance-driven assignments during unexpected availability changes
When a certified staff member updates their availability within one hour of shift creation, then the algorithm reassigns the affected shift to another qualified staff member and sends a manager notification within 10 minutes.
Audit Logging and Reporting
"As a regional manager, I want detailed logs of how shifts were assigned and why certain matches were rejected so that I can generate compliance reports and improve scheduling policies."
Description

Build an audit logging and reporting framework that records all matching decisions, rule evaluations, and certification checks performed by the CertiMatch Engine. This framework will generate compliance reports, mismatch alerts, and historical logs for auditing and performance analysis, enabling managers to review and refine scheduling strategies.

Acceptance Criteria
Certification Check Logging
Given a staff member is assigned to an open shift, When the CertiMatch Engine verifies certifications, Then a log entry is created recording staff ID, shift ID, certification status, timestamp, and verification result.
Rule Evaluation Audit Trail
Given multiple scheduling rules are applied to assign a shift, When the CertiMatch Engine processes the request, Then for each rule evaluated, an entry is recorded with rule ID, input parameters, evaluation outcome, and timestamp.
Compliance Report Generation
Given a manager requests a compliance report for a specified date range, When the system generates the report, Then the output includes total shifts, number of compliant assignments, non-compliant instances, and links to corresponding audit log entries.
Mismatch Alert Notification
Given a non-compliance is detected during certification checks, When a mismatch occurs, Then an alert is automatically sent to the manager including staff details, shift details, certification discrepancies, and a reference to the audit log entry.
Historical Audit Log Retrieval
Given a manager selects a past date and shift ID, When retrieving logs, Then the system displays all audit entries related to matching decisions, rule evaluations, and certification checks in chronological order, with filters for event type and date.

Eligibility Dashboard

Displays a live overview of all team members qualified and available for each upcoming shift. Managers can instantly identify suitable staff, streamline staffing decisions, and maintain transparency across the team.

Requirements

Real-time Availability Sync
"As a manager, I want the dashboard to reflect staff’s current availability in real-time so that I can immediately identify who can cover upcoming shifts and avoid scheduling conflicts."
Description

Continuously synchronize employees’ availability statuses between mobile app inputs, calendar integrations, and the central scheduling database. Ensure live updates are reflected instantly on the Eligibility Dashboard, enabling managers to view up-to-the-second availability information, prevent double-bookings, and respond quickly to last-minute availability changes.

Acceptance Criteria
Mobile Availability Update
Given an employee updates their availability in the mobile app, When the update is submitted, Then the central scheduling database and the Eligibility Dashboard reflect the new availability within 5 seconds.
External Calendar Change Detection
Given an employee’s calendar event is added or modified in an integrated external calendar, When the calendar sync runs, Then the employee’s availability status on the Eligibility Dashboard updates to unavailable or available accordingly.
Late-Night Availability Change Visibility
Given a manager opens the Eligibility Dashboard after an employee changes availability at night, When the dashboard loads, Then the updated availability is displayed without requiring a page refresh.
Double-Booking Prevention
Given an employee is already scheduled for a shift, When another shift is assigned that overlaps in time, Then the system blocks the assignment and displays an alert on the Eligibility Dashboard.
Offline Availability Update Handling
Given an employee toggles availability while offline, When they reconnect to the network, Then the mobile app synchronizes the change and updates the Eligibility Dashboard automatically.
Qualification Filter Integration
"As a manager, I want to filter eligible staff based on certifications and roles so that only qualified team members appear for each shift."
Description

Integrate the Eligibility Dashboard with the employee qualification and certification database to automatically filter staff based on required skills, roles, and compliance criteria for each shift. Display only those team members who meet the necessary qualifications, reducing manual vetting, maintaining regulatory compliance, and ensuring that shifts are always staffed by competent personnel.

Acceptance Criteria
Shift Qualification Filtering for Single Shift View
Given a shift requiring role 'Server' and qualification 'Food Safety Level 2', when the manager opens the Eligibility Dashboard for that shift, then only staff members with the 'Server' role and an active 'Food Safety Level 2' certification are displayed.
Qualification Update Reflects in Dashboard
Given a staff member’s new qualification is added to the employee database, when the Eligibility Dashboard is refreshed, then that staff member appears in the eligible list for shifts requiring that qualification.
Expired Certification Excluded from Eligibility
Given a staff member’s required certification has expired, when the manager views the Eligibility Dashboard, then that staff member is not included in the list of eligible candidates for shifts requiring that certification.
No Eligible Staff Notification Display
Given a shift has no staff meeting all required qualifications, when the manager views the Eligibility Dashboard for that shift, then a clear 'No eligible staff available' message is displayed.
Performance under Large Dataset
Given there are over 500 staff records and multiple qualification criteria for a shift, when the manager filters the Eligibility Dashboard, then results load within 2 seconds and only eligible staff are shown.
Shift Recommendation Engine
"As a manager, I want the dashboard to recommend the best-suited employees for a shift so that I can expedite the staffing process and maintain optimal team performance."
Description

Implement an AI-driven recommendation engine that analyzes both availability and qualification data alongside historical performance metrics to suggest the best-suited employees for each upcoming shift. Provide ranked recommendations on the dashboard to expedite staffing decisions, improve shift coverage quality, and optimize workforce utilization.

Acceptance Criteria
Optimal Shift Recommendations Generation
Given a list of employees with availability, qualifications, and performance metrics, when the scheduler requests recommendations for an upcoming shift, then the engine returns a ranked list of up to 10 employees sorted by suitability score within 5 seconds.
Real-Time Availability Update Integration
Given an employee updates their availability in the mobile app, when the scheduler views the shift recommendations, then the recommendations reflect the updated availability within 2 seconds.
Qualification Matching Enforcement
Given the shift requires specific qualifications, when generating recommendations, then only employees who hold the required qualifications are included in the recommendation list.
Historical Performance Prioritization
Given multiple qualified and available employees, when generating recommendations, then employees with higher historical performance ratings are ranked above those with lower ratings.
Dashboard Ranking Display
Given the engine provides a ranked list of employees, when the manager views the eligibility dashboard, then the recommendations appear in descending order of suitability score and include each employee's name, score, and key qualification matches.
Manual Override Controls
"As a manager, I want the ability to manually override eligibility and recommendations so that I can account for exceptional circumstances and make final staffing decisions."
Description

Provide intuitive override controls allowing managers to adjust eligibility filters, approve exceptions, and manually select or deselect staff for a shift. Capture override actions in an audit log for transparency, ensure managerial discretion in exceptional circumstances, and balance automated recommendations with human decision-making.

Acceptance Criteria
Manager Adjusts Eligibility Filters
Given the manager is viewing the Eligibility Dashboard, When they modify any eligibility filter (e.g., availability, qualifications), Then the list of eligible staff must update within 2 seconds to reflect the new filter settings.
Approving Exception for Unavailable Staff
Given a staff member is marked unavailable for a shift, When the manager selects “Approve Exception” and enters an exception reason, Then the staff member becomes eligible for that shift and a corresponding audit log entry is created with manager ID, staff ID, timestamp, and reason.
Manual Staff Selection Override
Given an automated recommended staff list for a shift, When the manager manually adds or removes a staff member, Then the manual change is applied immediately to the shift roster and the override action is recorded in the audit log with details.
Capturing Override Actions in Audit Log
Given the manager performs any override action (filter change, exception approval, manual select/deselect), When the action is confirmed, Then an audit log entry must be created containing action type, manager ID, timestamp, and relevant details, and must be retrievable via the audit log interface.
Displaying Override vs Recommendation Sources
Given both automated recommendations and manual overrides exist for a shift, When the manager views the shift roster, Then the UI must clearly label each staff entry as “Recommended” or “Manually Selected” and ensure no duplicates or omissions occur.
Notification and Alert System
"As a manager, I want to receive instant notifications when staff availability or eligibility changes so that I can respond quickly to shift coverage needs."
Description

Implement a notification system that automatically alerts managers and eligible employees of availability updates, qualification changes, and critical shift openings via in-app push notifications, email, and SMS. Ensure configurable alert rules and escalation paths to guarantee timely awareness of staffing needs and reduce response time to last-minute schedule adjustments.

Acceptance Criteria
Manager Shift Opening Alert
Given a critical shift opening is detected within 24 hours before the shift start time When the system identifies at least one qualified and available employee Then the manager receives a push notification, email, and SMS within 30 seconds
Employee Availability Update Alert
Given an employee updates their availability for upcoming shifts When the update is saved Then all managers assigned to that employee’s shifts in the next 7 days receive an in-app push notification and email summarizing the change
Qualification Change Alert
Given an employee’s qualification status changes (e.g., training completed or certification expired) When the system updates the qualification record Then the manager and the affected employee receive notifications via email and in-app push within 5 minutes
Alert Rule Configuration
Given a manager accesses the notification settings page When they enable or disable alert channels and set escalation thresholds Then changes are saved and reflected immediately in future notifications, confirmed by a success message
Notification Escalation Flow
Given a shift opening remains unfilled after initial alerts When the escalation threshold is reached (e.g., no response within 1 hour) Then notifications escalate to senior managers and include audit logs sent via email and SMS

Certification Alerts

Proactively notifies managers and staff when certifications or qualifications are nearing expiration. By preventing unintentional assignment of unqualified employees, it safeguards compliance and upholds service standards.

Requirements

Certification Data Integration
"As an operations manager, I want Shiftly to automatically pull and update employee certification records from multiple sources so that I always have an accurate view of staff qualifications without manual data entry."
Description

Seamlessly aggregate certification and qualification records from internal HR databases and external certification bodies into Shiftly's data model. Ensure data is updated in real-time or via scheduled syncs, enabling accurate tracking of each employee’s certification status. This integration supports consistency, reduces manual entry errors, and forms the foundation for timely alerts. It includes mapping different certification schemas, handling data conflicts, and maintaining data integrity across sources.

Acceptance Criteria
Initial Data Sync from HR Database
- Given the HR database contains valid certification records, when the initial sync is executed, then all records are imported into Shiftly with correct field mappings and no missing values. - The system logs show no errors and confirm the total number of records processed matches the source count. - Imported records appear in the certification management interface with correct employee associations.
Scheduled Daily Data Sync
- Given daily scheduled sync at 2 AM, when the sync runs, then only new or updated certification records are processed. - Sync duration does not exceed 15 minutes for up to 10,000 records. - Any sync failures trigger an alert email to the admin within 5 minutes.
External Certification Body Integration
- Given credentials to an external API, when requesting certification data, then the system authenticates successfully using OAuth 2.0. - Retrieved records are transformed to match Shiftly’s data schema and stored without data loss. - API rate limit errors are handled gracefully with retry logic.
Data Conflict Resolution
- Given conflicting certification data between sources, when the system detects a mismatch, then it applies the predefined source priority rules and updates records accordingly. - A conflict resolution log is generated detailing the original and resolved values. - Manual override options are presented to the administrator for unresolved conflicts.
Real-time Certification Status Update
- Given a certification expires within the next 30 days, when the status is computed, then the system flags the record as 'Expiring Soon'. - Staff list shows up-to-date certification status with color-coded indicators. - Notifications for expiring certifications are queued for dispatch within 1 hour of status change.
Certification Expiry Notification Scheduling
"As a restaurant manager, I want to receive alerts at predefined intervals before a staff member’s certification expires so that I can ensure they renew on time and avoid compliance issues."
Description

Provide configurable scheduling logic to determine when alerts are triggered for upcoming certification expirations. Allow setting multiple notification intervals (e.g., 30, 14, 7 days before expiry) and custom timeframes per certification type. Notifications should be queued and triggered in advance, ensuring managers and staff receive timely reminders to renew qualifications. It should also accommodate blackout periods or business-specific scheduling rules.

Acceptance Criteria
Default Notification Interval Setup
Given a certification with default intervals of 30, 14, and 7 days before expiration, when the scheduling logic executes, then notifications are queued exactly at 30, 14, and 7 days prior to the certification expiry date.
Custom Notification Interval per Certification Type
Given a certification type configured with custom intervals (e.g., 60 and 15 days), when the scheduling logic runs, then notifications are generated at exactly 60 and 15 days before the certification expires for that certification type.
Concurrent Expirations for Staff
Given a staff member holding multiple certifications with different expiry dates, when the scheduler processes upcoming expirations, then separate notifications for each certification are queued at their respective intervals without conflict or omission.
Blackout Period Compliance
Given that a notification interval falls within a defined blackout period, when generating notifications, then the notification is postponed and scheduled at the next valid business day outside the blackout period.
Notification Delivery Verification
Given queued notifications, when the delivery mechanism triggers, then each notification is marked as 'sent' in the system log and a delivery confirmation is recorded without errors.
Multi-Channel Alert Delivery
"As a staff member, I want to receive certification expiry alerts through my preferred channel so that I don’t miss important notifications regardless of my location."
Description

Enable sending certification expiry notifications via email, in-app push notifications, and SMS. Provide configurable default channels and allow users to opt-in to their preferred delivery methods. Ensure messages are formatted clearly, include relevant details (employee name, certification type, expiry date), and contain direct links to the renewal process. Implement retries for failed deliveries and log all notifications for audit purposes.

Acceptance Criteria
Default Channel Notification Delivery
Given the system has default notification channels configured, When a certification for an employee is expiring within the alert threshold, Then the notification is sent via all configured default channels (email, in-app push, SMS) without user intervention.
User Opt-In Channel Selection
Given a manager or staff user has updated their notification preferences, When a certification expiry alert is triggered, Then the notification is delivered only through the channels the user has opted in to.
Notification Content Accuracy
Given an expiring certification scenario, When the notification is generated, Then the message includes the employee name, certification type, expiry date, and a direct link to the renewal process, formatted clearly.
Failed Delivery Retry Mechanism
Given a notification fails to deliver via any channel, When a delivery failure is detected, Then the system retries sending the notification up to 3 times with exponential backoff and logs each retry attempt.
Notification Logging and Auditing
Given any notification is sent or attempted, When the notification process completes, Then all details (timestamp, recipient, channel, status success/failure, and retry attempts) are recorded in the audit log.
Role-Based Alert Assignment
"As a compliance officer, I want certification expiry alerts to be sent to both the employee and their department manager so that responsibilities for renewal are clear and tracked."
Description

Define and manage alert recipient roles, ensuring notifications are sent to appropriate stakeholders (e.g., staff, direct supervisors, compliance officers). Support custom roles and hierarchies, and allow setting fallback recipients in case primary contacts are unavailable. This ensures accountability for certification renewals and improves oversight across different organizational structures.

Acceptance Criteria
Primary Role Notification Setup
Given a certification is 30 days from expiration When the system generates an alert Then the alert is delivered to all users assigned the 'Staff' role within 60 seconds
Fallback Recipient Assignment
Given the direct supervisor is marked unavailable When an alert is triggered for a staff member Then the alert is sent to the predefined fallback recipient within the same role hierarchy
Custom Role Creation and Alert Assignment
Given a manager creates a custom role 'Safety Officer' and assigns staff When an alert configuration references 'Safety Officer' Then all users in the 'Safety Officer' role receive the alert
Hierarchical Role-Based Alert Distribution
Given an organization structure with staff, supervisor, and compliance officer roles When an alert is unacknowledged by the primary role within 10 minutes Then the system escalates and sends the alert to the next role in the hierarchy
Unavailable Primary Contact Alert Rerouting
Given both the primary and fallback recipients have inactive statuses When an alert is generated Then the system escalates the alert to the higher-level supervisor and logs the escalation event
Certification Expiry Dashboard
"As a store manager, I want to view a dashboard of all upcoming certification expirations so that I can proactively manage renewals and maintain compliance."
Description

Create an interactive dashboard within Shiftly displaying upcoming and past due certifications across the workforce. Include filters for date ranges, certification types, departments, and status. Provide visual indicators (e.g., color coding, charts) to highlight critical expirations and at-risk employees. Allow managers to export reports and take bulk actions, such as sending reminders or reassigning shifts for unqualified staff.

Acceptance Criteria
View Upcoming Certification Expirations
Given a manager navigates to the Certification Expiry Dashboard and selects the “Upcoming” view; When the current date is within 30 days of a certification’s expiration; Then the dashboard displays all employees with certifications expiring in that period, sorted by expiration date.
Filter Certifications by Department and Type
Given the Certification Expiry Dashboard is visible; When the manager applies filters for department “Kitchen” and certification type “Food Safety”; Then the dashboard only shows employees in “Kitchen” with “Food Safety” certifications and updates the total count accordingly.
Identify Critical Expirations via Color Coding
Given the dashboard lists certification expirations; When a certification is past due or expires within 7 days; Then the row is highlighted red, and if it expires within 8–30 days, the row is highlighted yellow; Otherwise, the row appears green.
Export Certification Report
Given one or more filters are applied on the dashboard; When the manager clicks the “Export to CSV” button; Then a CSV file containing all visible rows with correct headers is generated and downloaded to the user’s device.
Bulk Send Reminders and Reassign Shifts
Given the manager selects one or more employees from the dashboard; When the manager clicks “Send Reminder”; Then email notifications are sent to selected employees; And when the manager clicks “Reassign Shifts”; Then upcoming shifts for unqualified employees are reassigned to qualified staff based on availability rules and a success message is displayed.

SkillGap Forecast

Analyzes future schedules to identify potential shortages in critical skills or certifications. Managers receive actionable insights to recruit, reassign, or train staff ahead of time, avoiding last-minute coverage crises.

Requirements

Skill Data Ingestion
"As a restaurant manager, I want the system to automatically collect and standardize staff qualification data so that I can trust the accuracy of skill gap predictions without manual data entry."
Description

Implement a robust data ingestion pipeline that aggregates employees’ certifications, qualifications, and past performance records from existing HR and scheduling systems, normalizing and cleaning the data to ensure accuracy and consistency in skill forecasting.

Acceptance Criteria
Initial Data Load Validation
Given source HR and scheduling systems with employee certifications, qualifications, and performance records, when the ingestion pipeline runs its initial full load, then all records are imported without errors and the total counts match those in the source systems.
Incremental Updates Handling
Given new or updated employee records in the source systems, when the ingestion pipeline processes incremental updates, then all changes are reflected in the target database within five minutes, with no duplicate or missing records.
Data Normalization and Standardization
Given varied input formats for dates, certification names, and skill levels, when data is ingested, then all fields are normalized to ISO 8601 date format and certification names mapped to the standard taxonomy with 100% consistency.
Error Handling and Logging
Given corrupt or incomplete records in the source data, when the ingestion pipeline encounters these records, then it logs detailed error messages, skips only the invalid records, and continues processing the remaining data without interruption.
Data Quality Verification
Given completion of an ingestion run, when automated data quality checks execute, then any anomalies such as null critical fields or inconsistent skill ratings are flagged, and a detailed report is generated and available on the dashboard.
Performance and Scalability
Given a test batch of 10,000 employee records, when the ingestion pipeline runs, then processing completes within two minutes with no data loss and maintains CPU usage below 70%.
Skill Tagging and Classification
"As a scheduling coordinator, I want the system to label employees’ skills and certifications automatically so that the forecasting engine can accurately identify staff who meet shift requirements."
Description

Develop a dynamic tagging system that assigns standardized skill and certification labels to each employee record, enabling the AI engine to categorize staff based on qualifications, experience, and compliance requirements for specific shifts.

Acceptance Criteria
Skill Tag Assignment for New Employee
Given a new employee record is created with specified skills and certifications, When the record is saved, Then the system automatically assigns standardized skill tags that match the predefined taxonomy and displays them in the employee profile.
Updating Skill Tags for Role Change
Given an existing employee updates their job role or acquires new skills, When the HR manager submits these changes, Then the system updates the employee’s skill tags accordingly, replacing outdated tags and adding new ones, while maintaining data integrity across records.
Certification Compliance Verification
Given an employee with a required certification for a scheduled shift, When generating a shift schedule, Then the system verifies the validity and expiration date of the certification tag and flags any expired or missing certifications.
Bulk Import Skill Tags from HR Database
Given a bulk CSV file with employee IDs and skill data, When the file is imported, Then the system maps CSV fields to skill tags, validates entries against existing taxonomy, and provides an import summary report with successes and errors.
AI-driven Skill Matching in Schedule Generation
Given a future shift requiring specific skills, When the AI engine generates a schedule, Then it only assigns employees whose skill tags match or exceed the shift’s requirements and ensures no conflicts with availability.
Forecasting Algorithm
"As an operations manager, I want the system to predict potential gaps in required skills two weeks in advance so that I can proactively arrange training or recruitment."
Description

Design and integrate an AI-driven forecasting algorithm that analyzes historical schedules, upcoming shifts, and current staff availability to predict future skill shortages with a configurable time horizon and confidence thresholds.

Acceptance Criteria
Configurable Forecasting Parameters
Given a manager sets a forecasting time horizon and selects specific skills, when the parameters are saved, then the algorithm uses these settings to generate predictions without errors.
Shortage Alert Generation
Given the algorithm identifies a predicted skill shortage exceeding the confidence threshold, when forecasts are computed, then the system triggers an alert to the manager via dashboard and email.
Confidence Threshold Adjustment
Given a manager adjusts the confidence threshold for alerts, when the new threshold is applied, then future shortage predictions respect the updated threshold value.
Forecast Dashboard Visualization
Given the system completes a forecasting run, when the manager views the dashboard, then the skill gap forecasts are displayed with clear timelines, confidence levels, and required headcounts.
Forecast Data Export Capability
Given the manager requests an export of the forecast report, when they choose CSV or PDF format, then the system generates and downloads the file containing all forecast details.
Alert and Recommendation Engine
"As a store manager, I want to receive alerts and suggested actions when a critical skill shortage is forecasted so that I can immediately take corrective steps."
Description

Build an alerting mechanism that notifies managers of identified skill shortages and provides actionable recommendations, such as reassigning qualified staff, opening recruitment requests, or scheduling training sessions to address forecasted gaps.

Acceptance Criteria
Upcoming Skill Shortage Alert
Given the AI identifies a shortage of certified baristas for the upcoming weekend shifts When the alert engine triggers an alert Then a push notification and email are sent to the manager within 60 seconds of analysis completion
Actionable Recommendations Display
Given a skill gap is detected When the manager navigates to the SkillGap Forecast dashboard Then at least one recommendation is displayed with action type, affected role, urgency level, and estimated impact
Recruitment Request Initialization
Given a recommendation to recruit qualified staff When the manager selects 'Open recruitment request' Then a new recruitment draft is created with role details, required certifications, and suggested posting timeline
Training Session Scheduling
Given a recommendation to schedule training for certification renewal When the manager clicks 'Schedule training' Then the system automatically generates and sends calendar invitations to eligible staff with location, date, and resource details
Staff Reassignment Process
Given a recommendation to reassign qualified staff to cover a skill shortage When the manager clicks 'Reassign staff' Then the system displays a list of available qualified employees and updates the schedule upon confirmation
Skill Gap Dashboard
"As a regional manager, I want a visual dashboard showing where and when skill shortages will occur so that I can compare across multiple locations and plan resources accordingly."
Description

Create an interactive dashboard within the Shiftly app that visualizes forecasted skill gaps, upcoming training schedules, and staffing recommendations, allowing managers to drill down by location, department, and time frame for detailed insights.

Acceptance Criteria
Viewing Skill Gaps for Upcoming Week
Given the manager selects the upcoming week filter on the Skill Gap Dashboard, When the dashboard loads, Then it displays all forecasted skill shortages per location and department with percentage gap and number of required staff, and provides color-coded indicators for severity.
Drilling Down into Department Skill Gaps
Given the manager clicks on a specific department in a location on the Skill Gap Dashboard, When the drill-down view opens, Then detailed skill shortages, upcoming training schedules, and recommended staffing adjustments for that department are shown.
Exporting Skill Gap Report
Given the manager clicks the export button on the Skill Gap Dashboard, When the export completes, Then a CSV file is downloaded containing forecasted skill gaps, training schedules, and staffing recommendations filtered by the selected time frame.
Real-time Data Refresh
Given new schedule changes occur, When the manager refreshes the Skill Gap Dashboard, Then the dashboard updates within five seconds to reflect the latest staff assignments, training completions, and skill gap forecasts.
Setting Alert Thresholds for Critical Skills
Given the manager configures an alert threshold for a critical skill on the Skill Gap Dashboard settings, When the threshold is exceeded, Then the dashboard highlights the skill gap in red and sends an in-app notification to the manager.

TrainingSync Scheduler

Seamlessly incorporates mandatory training sessions into the shift roster, balancing skill development needs with operational coverage. This ensures continuous compliance, upskills the workforce, and maintains optimal productivity.

Requirements

Mandatory Training Slot Assignment
"As a restaurant manager, I want the scheduler to automatically assign mandatory training slots into staff shifts so that I maintain compliance without manual scheduling."
Description

Automatically allocate required training sessions into employees' shift schedules based on availability, ensuring no overlap with peak operational hours. The system should consider employee roles, existing shift commitments, and mandatory training quotas to seamlessly integrate training without compromising workforce coverage. It must support multiple training types, durations, and recurrence rules while maintaining compliance with organizational learning requirements.

Acceptance Criteria
Assign Training Slot in Non-Peak Hours
Given an employee with available non-peak hours and no conflicting shifts When the system schedules a mandatory training session Then the training is assigned during a time slot with operational coverage below 75% and no overlap with existing shifts
Allocate Training Based on Role Requirements
Given an employee in a role with specified mandatory training quotas When the scheduling engine runs Then each role-specific mandatory training is allocated before the end of the quarter for all employees in that role
Handle Overlapping Shift and Training Requests
Given an employee with a pending shift swap request and a mandatory training due When the system processes both events Then the training is scheduled around the confirmed shift swap without causing double-booking
Schedule Recurring Training Sessions
Given a training type with a monthly recurrence rule When the shift roster is generated for the month Then the system inserts all instances of the recurring session into the schedule at compliant intervals without manual intervention
Validate Multiple Training Type Assignments
Given an employee with multiple different mandatory trainings pending When the scheduler allocates training slots Then no two different training types overlap and all required sessions are scheduled within the required compliance period
Skill-Capacity Balancer
"As a retail manager, I want the system to balance training schedules with shift coverage so that my store remains fully staffed during peak hours."
Description

Balance the insertion of training sessions with operational needs by matching upskilling requirements against real-time shift coverage demands. The scheduler evaluates current staffing levels, skill gaps, and upcoming training deadlines to prioritize training allocation, ensuring essential roles remain filled while enabling continuous staff development. It should adjust assignments dynamically in response to shift cancellations or staff availability changes.

Acceptance Criteria
Mandatory Safety Training Integration
Given the schedule has a high-demand weekend shift and a mandatory safety training session is due within 72 hours When the Skill-Capacity Balancer runs Then the training session is inserted on a day with least impact to coverage And critical roles remain staffed at 100% capacity
Dynamic Rescheduling on Shift Cancellation
Given an employee cancels a scheduled shift with pending training assigned When the cancellation is processed by the system Then the Balancer recalculates staffing levels And moves the training session to the next available qualified employee without leaving gaps in coverage
Skill Gap Prioritization Before Deadline
Given multiple employees have upcoming training deadlines within the next 48 hours When capacity is constrained during a peak hour Then the system prioritizes training for employees with the largest skill gaps And ensures no critical role falls below the required skill threshold
Real-Time Coverage Check During Training
Given a training session is scheduled during a lunch shift When the training start time arrives Then the system verifies that at least one qualified staff member remains on the floor And sends an alert if coverage falls below the minimum operational requirement
Last-Minute Availability Change Adjustment
Given an employee updates availability one hour before a scheduled training shift When the Balancer processes the availability change Then the training assignment is dynamically reassigned to an alternate qualified employee And overall shift coverage remains fully staffed
Training Conflict Detection
"As a shift supervisor, I want to be alerted when a training session conflicts with an employee’s shift so that I can resolve the conflict before it impacts operations."
Description

Detect and resolve scheduling conflicts between mandatory training sessions and existing shift allocations. The system should notify managers of any overlaps, suggest alternative training times, and offer one-click adjustments to shift or training schedules. Conflict detection should be proactive, scanning both current and future schedules to prevent any compliance or coverage issues from arising.

Acceptance Criteria
Conflict Detection on Training Creation
Given a manager schedules a mandatory training that overlaps with existing employee shifts When the training is saved Then the system must detect the overlap and flag the conflicting shifts immediately
Proactive Future Schedule Scan
Given there are upcoming training sessions and shifts scheduled more than 30 days ahead When the system performs its nightly scan Then any future conflicts are identified and reported to the manager before they occur
Alternative Training Time Suggestions
Given a detected conflict between training and shift allocations When the manager reviews the conflict notification Then the system suggests at least three alternative training slots within the same week that have no shift overlaps
One-Click Shift Adjustment
Given a conflict has been identified and alternative training times suggested When the manager selects an alternative slot Then the system automatically adjust the affected shifts or training schedules with one click and update the roster accordingly
Manager Notification Delivery
Given any conflict is detected during schedule creation or nightly scan When the system identifies a conflict Then an in-app notification and email alert are sent to the manager within one minute detailing the conflict and next steps
Training Reminder Notifications
"As an employee, I want to receive reminders for my scheduled training so that I don’t miss any mandatory sessions."
Description

Send automated, customizable reminders to employees and managers for upcoming training sessions integrated within their shift roster. Notifications should be delivered via mobile app, email, or SMS based on user preference, with configurable timing (e.g., 24 hours or 1 hour before). Confirmation tracking must update the schedule upon acknowledgment to reduce no-shows and ensure attendance.

Acceptance Criteria
Mobile App Training Reminder Delivery
- Given an employee has a training session scheduled in 24 hours and mobile app notifications enabled, when the reminder is triggered 24 hours before the session, then the employee receives a push notification within 5 seconds. - Given an employee has a training session scheduled in 1 hour and mobile app notifications enabled, when the reminder is triggered 1 hour before the session, then the employee receives a push notification within 5 seconds.
Email Reminder Customization
- Given a manager configures email reminders to be sent 48 hours before a training session, when a session is scheduled at least 48 hours later, then the system sends an email reminder at the specified time containing session date, time, location, and acknowledgment link. - Email delivery success rate is at least 99% as measured in delivery logs.
SMS Reminder Timing Configuration
- Given an employee opts in for SMS reminders at 2 hours before training, when the system triggers reminders at that interval, then the employee receives an SMS message with session details within 10 seconds. - SMS delivery failure rate does not exceed 1% per month.
Acknowledgment Tracking and Schedule Update
- Given an employee clicks the acknowledgment link in any reminder, when the system processes the acknowledgment, then the employee’s status in the shift roster updates to “Confirmed” within 2 minutes. - The confirmation timestamp and notification channel are logged for audit.
Preference-Based Notification Channel Selection
- Given an employee has multiple notification channels configured with priority order, when a reminder is sent, then the system selects the highest-priority available channel and does not send duplicate notifications across other channels. - If the primary channel fails, the system retries once on the secondary channel within 5 minutes.
Compliance and Training Report
"As a general manager, I want comprehensive reports on staff training compliance so that I can audit our upskilling efforts and address any gaps."
Description

Provide managers with a dashboard and exportable reports summarizing training compliance, upcoming sessions, attendance rates, and skill coverage metrics. The tool should filter by date range, department, and training type, offering actionable insights and alerts for overdue sessions. This ensures transparency in upskilling efforts and helps managers meet audit requirements.

Acceptance Criteria
Date Range Compliance Overview
Given the manager selects a start and end date, When the Compliance and Training dashboard loads, Then it displays total scheduled sessions, attendance rate, upcoming sessions, and overdue sessions for the selected range.
Department and Training Type Filter Export
Given the manager applies filters for department and training type, When exporting the compliance report, Then the generated CSV contains only records matching the filters with correct headers and data fields.
Overdue Session Alert
Given any training session is overdue beyond its scheduled date, When the manager logs into the dashboard, Then an alert banner displays session name, responsible employee, original date, and a link to reschedule or mark as completed.
Skill Coverage Dashboard Widget
Given the manager views the Skill Coverage widget, When selecting a department, Then a chart displays percentages of employees with each required skill and highlights any coverage gaps below the defined threshold.
Attendance Rate Trend Chart
Given the manager selects ‘Attendance Trends’ and a date range, When the chart renders, Then it plots attendance rates over time with at least one data point per week and allows hovering for exact values.

Schedule by Voice

Empower managers to generate entire shift rosters using natural voice commands. Simply speak your staffing requirements, and Shiftly’s AI composes a balanced schedule in seconds, cutting manual setup time by half and freeing up hours for strategic tasks.

Requirements

Voice Command Recognition Engine
"As a restaurant manager, I want to speak my staffing needs so that the system can understand and process my requirements without manual entry."
Description

Develop a robust natural language voice recognition system to capture and transcribe manager staffing requirements accurately. This engine should handle variations in speech patterns, accents, and background noise, converting spoken commands into structured inputs for the schedule AI module.

Acceptance Criteria
Basic Voice Command Capture
Given a manager issues a clear staffing requirement via voice When the system receives the audio Then it transcribes the command with at least 95% word-level accuracy
Accent Variation Handling
Given a manager speaks with a regional or non-native accent When the system processes the voice input Then it correctly interprets and structures the spoken staffing parameters with at least 90% accuracy
Background Noise Resilience
Given ambient noise levels up to 60 decibels in the environment When the manager issues a voice command Then the system transcribes the command accurately in at least 9 out of 10 test cases
Complex Multi-step Commands
Given a spoken command detailing multiple shift parameters (times, roles, employee preferences) When the system parses the input Then it correctly segments and maps each component into structured fields 100% of the time
Real-time Feedback Confirmation
Given successful transcription of a voice command When transcription completes Then the system provides an immediate voice or visual summary that exactly matches the recognized parameters
Intent Parsing and Slot Extraction
"As a retail manager, I want the system to interpret my spoken instructions correctly so that it generates accurate shift allocations."
Description

Implement an AI-driven parser to extract key scheduling parameters—such as date, time, shift length, and staff roles—from transcribed voice commands. The parser should identify entities and intents to ensure the AI scheduler receives precise instructions.

Acceptance Criteria
Valid Date Extraction
Given a voice command containing a natural language date reference (e.g., “next Monday”), when the parser processes the transcribed text, then it must extract and normalize the date to YYYY-MM-DD format matching the actual next Monday.
Time Range Extraction
Given a voice command specifying a start and end time (e.g., “from 9 AM to 5 PM”), when processed, then the parser must identify and output separate start_time and end_time fields in 24-hour HH:MM format.
Shift Length Identification
Given a voice command that includes shift duration (e.g., “an 8-hour shift”), when processed, then the parser must extract the shift_length parameter as an integer number of hours.
Staff Role Recognition
Given a voice command listing roles and headcounts (e.g., “two servers and one cook”), when processed, then the parser must output a roles array with correct role names and corresponding counts.
Ambiguous Entity Clarification
Given a voice command with an ambiguous parameter (e.g., “Friday afternoon”), when processed, then the system must detect ambiguity and trigger a follow-up prompt requesting specific time or date details.
Real-time Schedule Preview Feedback
"As a shift manager, I want to see a preview of the schedule instantly after I speak my requirements so that I can verify and tweak it as needed."
Description

Provide an interactive preview of the generated schedule immediately after voice command execution. This feature allows managers to review, adjust, and confirm the schedule before finalizing, ensuring alignment with operational needs.

Acceptance Criteria
Schedule preview availability
The system displays the generated schedule preview within 5 seconds after the manager completes the voice command.
Editable shift details in preview
The manager can edit shift details (employee, time, role) directly in the preview, and changes are immediately reflected in the preview.
Conflict detection in preview
If a scheduling conflict exists (e.g., overlapping shifts or insufficient coverage), the preview highlights conflicts in red and provides conflict resolution suggestions.
Preview confirmation and save
Upon manager confirmation, the final schedule is saved and visible to staff within the mobile app, and a confirmation message is displayed.
Preview cancellation does not save
If the manager cancels the preview, no schedule changes are saved and the existing schedule remains unchanged.
Multilingual and Accent Adaptability
"As a manager who speaks Spanish, I want to give voice commands in my language so that I can interact with the system comfortably."
Description

Extend voice recognition support to multiple languages and regional accents common among users. The system should dynamically detect the language context or allow managers to select their preferred language for command input.

Acceptance Criteria
Spanish Language Mode Activation
Given the manager switches the voice interface to Spanish, When they say 'Crear horario para la próxima semana', Then the system recognizes the command with at least 95% accuracy and generates the correct shift schedule.
Regional Accent Recognition - British English
Given the manager uses a British accent to say 'Generate shift roster for Friday', When the command is spoken, Then the system interprets and executes it correctly within 5 seconds.
Language Selection Fallback
Given the system cannot auto-detect the language, When the manager selects French from the language dropdown menu, Then all subsequent French voice commands are recognized with 90% accuracy and applied to scheduling.
Mixed English and Mandarin Commands
Given the manager mixes English and Mandarin in one sentence, When they say '下一班 shift 带谁?', Then the system correctly parses both languages and generates the appropriate shift roster.
Unsupported Language Prompt Handling
Given the manager speaks in an unsupported language, When the system detects no match, Then it prompts the user to select a supported language and offers a list of available options.
Ambiguity Resolution and Confirmation
"As a manager, I want the system to ask for clarification when my instructions are unclear so that I can ensure accurate schedule output."
Description

Incorporate a dialogue module to detect ambiguous or conflicting instructions and prompt the user for clarification or confirmation. This ensures that the system does not generate incomplete or incorrect schedules due to unclear voice inputs.

Acceptance Criteria
Unclear Shift Time Specification
Given the manager’s voice command omits either the start or end time of a shift, when processed by the system, then the system prompts the manager to specify the missing time before finalizing the schedule.
Ambiguous Employee Role Request
Given a voice instruction to schedule an employee with multiple roles, when the system identifies multiple possible roles, then the system asks the manager to confirm which role should be assigned.
Conflicting Shift Overlap Detection
Given a voice command that would assign an employee to overlapping shifts, when the system detects the conflict, then it prompts the manager to resolve or adjust the conflicting assignments.
Unrecognized Location or Store Area
Given a voice instruction referencing a location with multiple possible areas (e.g., “the kitchen”), when the system cannot determine the specific area, then it asks the manager to clarify the exact location.
Vague Staffing Quantity Request
Given a voice command that uses non-specific quantity terms (e.g., “a couple of baristas”), when the system parses the request, then it prompts the manager to provide an exact number for accurate scheduling.
Secure Voice Authentication
"As a business owner, I want only authorized managers to use voice scheduling so that sensitive roster data remains secure."
Description

Implement voice-based user authentication to verify manager identity before processing scheduling commands. This feature should integrate with existing security protocols to prevent unauthorized schedule modifications.

Acceptance Criteria
Successful Manager Voice Enrollment
Given a manager with an existing account, when they initiate voice enrollment, then the system captures a voice sample with a signal-to-noise ratio of at least 30dB, securely stores the generated voiceprint within 5 seconds, and displays a confirmation message.
Authorized Voice Login
Given a manager with a valid stored voiceprint, when they issue a scheduling command via voice, then the system authenticates the voice sample with at least 95% confidence and proceeds to process the scheduling command within 10 seconds.
Failed Authentication on Non-Matching Voice
Given a voice input that does not match any enrolled voiceprint, when the manager attempts voice authentication, then the system rejects the authentication attempt, logs the failure event, and prompts the user to retry or switch to an alternative login method.
Fallback to Password After Repeated Voice Failures
Given three consecutive failed voice authentication attempts, when the manager attempts authentication again, then the system disables voice login for 15 minutes, prompts for password entry, and logs the lockout event.
Prevention of Unauthorized Schedule Modification
Given successful voice authentication, when the manager attempts to modify the schedule, then the system verifies authorization against role-based access controls, applies allowed changes, blocks any unauthorized modifications, and logs all actions.

QuickEdit Voice

Allow managers to tweak individual shifts on the fly with simple voice phrases like “move John’s shift to 3 PM” or “swap Emma and Raj.” This hands-free editing reduces friction and enables schedule adjustments even when multitasking.

Requirements

Voice Command Recognition
"As a restaurant manager, I want to speak commands to modify shifts so that I can update schedules hands-free while multitasking."
Description

Capture and process spoken commands directly from the mobile app’s microphone, leveraging noise-cancellation and secure audio streaming to the voice processing engine. The feature activates a continuous listening mode when QuickEdit Voice is enabled, ensuring low latency and high reliability in loud restaurant or retail environments. It integrates with the app’s permissions framework to handle user consent and operates within privacy guidelines, delivering at least 90% speech detection accuracy under normal conditions.

Acceptance Criteria
Enabling Continuous Listening Mode
Given the manager has enabled QuickEdit Voice and is on the schedule screen When the app is launched or brought to the foreground Then continuous listening mode starts within 2 seconds And a microphone indicator is visible And app startup latency increases by no more than 100ms
Successful Voice Command Recognition
Given continuous listening mode is active When the manager says “move John’s shift to 3 PM” clearly Then the system transcribes the command with at least 90% accuracy And applies the shift change correctly in the schedule within 1 second
Voice Command Accuracy in Noisy Environment
Given ambient noise levels up to 70dB in a restaurant or retail setting When the manager issues a valid voice command Then speech detection accuracy remains at or above 90% And the command executes correctly without manual intervention
Handling Unrecognized Commands Gracefully
Given continuous listening mode is active When the system cannot parse or match the spoken input to a valid command Then the app displays an in-app prompt reading “Command not recognized, please repeat” within 500ms And continues listening for a valid command
Permission Denied Scenarios
Given the user has denied microphone access in app permissions When the manager navigates to QuickEdit Voice for the first time Then the app displays a permission request dialog explaining why microphone access is needed And voice features remain disabled until permission is granted
Natural Language Command Parsing
"As a store manager, I want the system to understand different ways I voice shift changes so that I don’t need to memorize exact commands."
Description

Interpret and map recognized speech to scheduling actions by employing an NLP engine that supports a variety of phrasing patterns. The system must handle intents like moving, swapping, adding, or removing shifts (e.g., “move John’s shift to 3 PM,” “swap Emma and Raj,” “add Sara at 5 PM”), including synonyms and minor grammatical variations. Integration with backend APIs ensures parsed commands translate into accurate schedule modifications.

Acceptance Criteria
Move Shift Command
Given the manager says "move John’s shift to 3 PM" via voice input; When the NLP engine processes the command; Then the system must parse intent "move", target "John", and time "3 PM", invoke the backend API to update John’s shift to 3 PM and display a confirmation message "John’s shift has been moved to 3 PM."
Swap Shifts Command
Given the manager says "swap Emma and Raj" via voice input; When the NLP engine processes the command; Then the system must parse intent "swap", employees "Emma" and "Raj", invoke the backend API to exchange their shifts, and display confirmation "Emma’s and Raj’s shifts have been swapped."
Add Shift Command
Given the manager says "add Sara at 5 PM" via voice input; When the NLP engine processes the command; Then the system must parse intent "add", employee "Sara", and time "5 PM", invoke the backend API to create a new shift for Sara at 5 PM, and display confirmation "Sara has been scheduled for 5 PM."
Remove Shift Command
Given the manager says "remove Michael’s shift" or "delete Mike shift" via voice input; When the NLP engine processes the command with minor grammatical variations; Then the system must correctly identify intent "remove", target "Michael", invoke the backend API to delete Michael’s shift for the appropriate date/time, and display confirmation "Michael’s shift has been removed."
Unrecognized Voice Command Handling
Given the manager says an unsupported or ambiguous phrase like "change shift" without details; When the NLP engine cannot confidently map intent or parameters; Then the system must prompt the manager with a clarification question "Which employee and time would you like to change?", log the ambiguity, and not perform any schedule modifications until clarified.
Instant Schedule Update
"As a manager, I want my spoken schedule edits to appear instantly so that I can trust my commands were executed without manual refresh."
Description

Apply parsed voice commands to the scheduling database in real time, reflecting changes immediately across the mobile app, web dashboard, and notifications. The feature must enforce conflict checks, labor rule validations, and atomic transactions to prevent data inconsistencies. Integration with the existing scheduling service ensures seamless updates and automatic rollback on error.

Acceptance Criteria
Voice Command Parsing Accuracy
Given a manager issues a voice command via the mobile app, when the command “move John’s shift to 3 PM” is spoken, then the system must correctly identify the employee, original shift time, and target time with at least 95% parsing accuracy and apply the update.
Conflict Resolution Enforcement
Given an existing overlapping shift, when a voice command would create a conflict, then the system must detect the conflict, prevent the update, and return a clear conflict error message within 3 seconds.
Real-Time UI Synchronization
Given a successful schedule update, when the database transaction commits, then the updated shift must appear on the mobile app and web dashboard within 2 seconds without requiring a manual refresh.
Rollback on Error
Given a failure during the update process (such as network or validation error), when the error occurs, then the system must automatically roll back all changes to the previous state and notify the manager of the failure within 5 seconds.
Labor Rule Validation
Given predefined labor rules (e.g., maximum daily hours, required rest period), when a voice command modifies a shift, then the system must validate the change against all labor rules and reject with a specific rule violation message if any rule is violated.
Voice Feedback Confirmation
"As a busy manager, I want to receive audible and visual confirmations of my voice edits so that I know my instructions were applied correctly."
Description

Provide immediate audio and visual feedback upon command execution, using text-to-speech to confirm actions like “Shift moved” and UI notifications summarizing details such as employee name and new time. This feedback loop ensures managers know the result of their commands. Integration with the notification framework and TTS engine delivers consistent messaging across platforms.

Acceptance Criteria
Voice Command Success Acknowledgment
Given a manager issues a valid voice command to modify a shift, When the command is executed successfully, Then the TTS engine announces “Shift moved successfully,” and the UI displays a notification showing the employee name and updated time.
Voice Command Failure Handling
Given a manager issues a voice command that fails validation or encounters an error, When the system cannot complete the request, Then the TTS engine announces “Unable to process your request,” and the UI displays an error notification with details.
Visual Notification Display
Given a voice command is executed (success or failure), When the action completes, Then the system shows a visual notification banner summarizing the action or error, including employee name and new shift time or error message, visible for at least 5 seconds.
Notification Framework Integration
Given the system triggers a shift change notification, When the UI notification is generated, Then it must be logged through the centralized notification framework API with correct event type, payload (employee ID, time change), and timestamp.
Cross-Platform Consistency
Given a voice command is executed on iOS or Android, When feedback is provided, Then both platforms use identical TTS phrasing and notification UI style guidelines, ensuring consistent user experience across devices.
Error Handling & Correction Suggestions
"As a manager, I want the system to guide me when my voice commands are unclear so that I can correct mistakes without navigating menus."
Description

Detect ambiguous or invalid commands and initiate a dialogue to clarify intent or suggest corrections. The system should prompt follow-up questions for conflicting inputs (e.g., multiple Johns) or rule violations, offering quick response options via voice and UI. Integration with conversational AI flows ensures smooth interaction and fallback to manual correction when needed.

Acceptance Criteria
Ambiguous Employee Identification
Given a voice command references an employee name shared by multiple staff members When the system detects more than one matching profile Then the system prompts the manager with clarifying questions listing the ambiguous names and any distinguishing details And allows the manager to confirm the intended employee
Rule Violation Detection
Given a voice command that would assign an employee outside their availability or exceed their maximum hours When the system processes the command Then the system informs the manager of the specific rule violation via voice and UI And suggests alternative shift times or other eligible staff members
Conflicting Shift Swap Requests
Given a voice request to swap two employees’ shifts where one employee is already scheduled for another shift at the target time When the system evaluates the swap Then the system alerts the manager to the conflict And offers options to pick a different time or select a different employee
Invalid Command Syntax
Given a voice input that does not match any recognized command pattern When the system fails to parse the command Then the system asks the manager to rephrase or choose from a list of common voice commands displayed on UI And provides example phrases for guidance
Fallback to Manual Correction
Given repeated failures to resolve a voice command through clarification prompts When the manager indicates frustration or time limit is reached Then the system offers to open the manual shift editor UI pre-populated with the attempted changes And confirms that the manager can complete the correction manually

SmartPrompt Assistant

Guide managers through complex requests with intelligent follow-up questions. If a voice command is ambiguous, the system asks targeted clarifying prompts, ensuring accurate schedule changes and minimizing misinterpretations.

Requirements

Intent Recognition Engine
"As a manager, I want the system to understand my natural language requests accurately so that I can make schedule changes without repeating or clarifying them myself."
Description

The system must accurately interpret voice commands and natural language inputs by leveraging advanced NLU algorithms. It should identify user intents and extract parameters, enabling the SmartPrompt Assistant to determine when clarifying questions are needed. This module integrates with the AI engine to process manager requests seamlessly, reducing misinterpretations and ensuring the scheduling system acts on accurate directives.

Acceptance Criteria
Simple Voice Command Interpretation
Given a manager says “Schedule John for next Tuesday morning” When the engine processes the input Then it identifies intent “CreateShiftAssignment” and extracts parameters {employee: “John”, date: “next Tuesday”, shift: “morning”}
Ambiguous Voice Command Clarification
Given a manager says “Schedule Sarah next week” When the engine cannot map full parameters Then it prompts “Which day and shift should I schedule Sarah for next week?”
Complex Request Parameter Extraction
Given a manager says “Swap my evening shift with Alex on Friday” When processed Then the engine identifies intent “SwapShift” and extracts parameters {requester: “current user”, targetEmployee: “Alex”, date: “Friday”, shift: “evening”}
Intent Recognition Accuracy Validation
When processing a test set of 100 varied voice commands Then the engine achieves at least 95% accuracy in intent identification and parameter extraction
Integration with Scheduling Engine
Given extracted intent and parameters for a valid scheduling request When forwarded to the AI scheduling engine Then the roster updates reflect the request without errors within 2 seconds
Clarification Question Generator
"As a manager, I want the assistant to ask me specific questions when my command is unclear so that I can quickly provide the missing information and avoid scheduling errors."
Description

Must dynamically generate targeted follow-up questions when ambiguity is detected, using contextual information such as current roster data to refine user requests. This requirement ensures that the assistant asks precise questions, eliminating guesswork and guiding managers to provide necessary details for schedule modifications. Integrates with the UI to display or speak prompts.

Acceptance Criteria
Ambiguous Shift Request Detection
Given a manager’s voice command lacks a specific date, When the system processes the request, Then it identifies the missing date field and generates a clarification question asking “Which date should this shift change apply to?”
Contextual Clarification for Missing Employee Name
Given a request to assign a task but the employee name matches multiple staff, When the ambiguity is detected, Then the system asks “Which employee did you mean: Alice Johnson or Alice Smith?” based on the current roster data.
Prompt Display on UI
Given a clarification question is generated, When the manager views the Shiftly interface, Then the question appears in the chat window within 2 seconds and remains visible until answered.
Prompt Voice Playback
Given the manager interacts via voice, When a clarification question is generated, Then the system plays the audio prompt clearly within 3 seconds and awaits a response.
Multi-Parameter Ambiguity Resolution
Given a swap request that omits both date and shift time, When the system detects multiple possible matches, Then it asks “Which shift would you like to swap: the Monday morning shift or Tuesday evening shift?” combining both parameters.
Invalid Response Handling
Given a manager replies with an unclear or irrelevant answer to the clarification prompt, When the response is received, Then the system recognizes the invalid input, repeats the clarification question, and provides an example of a valid response.
Contextual Data Integration
"As a manager, I want the assistant to reference real-time scheduling data when clarifying my requests so that the suggested changes are always valid and conflict-free."
Description

This component must fetch and access relevant scheduling data (employee availability, shift assignments, restaurant constraints) in real time to support both intent recognition and clarification. It ensures that follow-up questions and scheduling actions are grounded in the latest data, preventing conflicts such as double-booking or unavailable staff.

Acceptance Criteria
Real-Time Employee Availability Fetch
Given a manager requests a schedule change When the system queries employee availability Then the system retrieves availability data updated within the last 5 minutes for all affected employees
Conflict Detection Before Schedule Updates
Given a proposed shift assignment When the system integrates scheduling data Then it identifies any double-bookings or assignments for unavailable staff and prevents the update
Context-Aware Clarification Prompt
Given an ambiguous voice command referencing a shift When the system accesses current shift assignments Then it generates follow-up questions that reference specific employees, dates, and times from the latest data
Real-Time Constraint Enforcement
Given restaurant constraints like maximum staff per shift When a manager attempts to assign or swap a shift Then the system validates the action against those constraints and blocks invalid assignments
Continuous Data Synchronization
Given external schedule modifications When data changes occur in connected systems Then the system synchronizes those changes within 60 seconds to ensure up-to-date information
Bulk Shift Assignment Validation
Given a manager uploads multiple shift assignments at once When the system fetches related availability and assignments Then it validates each shift against availability and constraints and returns a detailed success/failure report
Adaptive Dialogue Flow
"As a manager, I want the assistant to remember our prior conversation when I answer its questions so that I don't have to repeat information during complex scheduling tasks."
Description

Implement an adaptive dialogue system that maintains conversational context across multiple turns, remembering prior inputs during the session. This flow allows the assistant to ask successive clarifying questions without losing track of earlier user responses, resulting in coherent interactions and reduced frustration for managers during scheduling sessions.

Acceptance Criteria
AmbiguousVoiceCommandClarification
Given a manager issues an ambiguous voice request for a shift change, When the system cannot confidently parse the intent, Then it prompts a targeted clarifying question within 2 seconds.
ContextPreservationAcrossTurns
Given a manager specifies a shift date in turn one and a time range in turn two, When the manager asks to move the shift, Then the system uses both prior inputs to propose an accurate updated shift slot.
SessionContinuityAfterCorrection
Given a manager corrects a previous answer mid-conversation, When the manager provides a new value, Then the system retains earlier context and updates only the corrected field without restarting the dialogue.
AccurateFollowUpQuestionSequencing
Given multiple follow-up questions are required to fulfill a complex request, When the system asks each question, Then it presents them in a logical order without repeating or skipping any needed clarification.
InterruptionHandlingAndContextResumption
Given a manager pauses or switches topic before completing clarification, When they return to the original request, Then the system recalls the last context and resumes the line of questioning correctly.
Voice and Text Prompt Delivery
"As a manager, I want to receive clarifying questions both spoken aloud and displayed on my device so that I can respond in whichever way is more convenient for me."
Description

The assistant must deliver clarifying prompts via both voice and text interfaces on mobile and web platforms. It should leverage speech synthesis with clear, concise phrasing and visual display cards on-screen. This ensures managers can interact in noisy environments or prefer silent reading, enhancing accessibility and usability.

Acceptance Criteria
Noisy Environment Voice Prompt
Given the manager is in an environment with background noise up to 75 dB, when the assistant delivers a voice prompt, then the speech synthesis clarity score is at least 95% as measured by the automated clarity analysis tool.
Text Prompt Display on Mobile App
Given the manager submits an ambiguous voice command, when the assistant generates a clarifying prompt, then a visual display card with the prompt appears on the mobile screen within 2 seconds and is fully visible without scrolling.
Text Prompt Display on Web Interface
Given the manager submits an ambiguous voice command on the web platform, when the assistant generates a clarifying prompt, then a text display card is rendered within 1.5 seconds, formatted according to UI design specifications and accessible via keyboard navigation.
Voice and Text Synchronization
Given both voice and text interfaces are available, when the assistant delivers a clarifying prompt, then the audio playback and on-screen prompt appear simultaneously within a 200ms synchronization window.
Silent Mode Text-Only Interaction
Given the manager has enabled silent mode in settings, when an ambiguous command is detected, then only text prompts are displayed on screen without any audio playback, and the system confirms silent mode via a non-intrusive visual indicator.

VoiceConfirm Feedback

Provide audible confirmations of schedule actions via text-to-speech or Slack audio snippets. Managers receive instant spoken acknowledgments—such as “Shift updated for Lisa on Tuesday”—for peace of mind and reduced command errors.

Requirements

Audible Shift Update
"As a manager, I want to hear an audible confirmation whenever I update a shift so that I can be sure my changes have been applied correctly and avoid scheduling mistakes."
Description

Upon any change to an employee's shift schedule, the system must generate an audible confirmation message using text-to-speech technology. The message should articulate the employee's name, the updated shift timing, and any relevant details (e.g., location or role). This functionality ensures that managers receive immediate verbal feedback, reducing errors and enhancing confidence in schedule modifications.

Acceptance Criteria
Manual Shift Time Update Notification
Given a manager updates an employee's shift start or end time in the scheduler, when the update is saved, then the system generates an audible confirmation stating “Shift updated for [Employee Name] to [New Start Time] - [New End Time]” within 2 seconds.
New Shift Addition Confirmation
Given a manager adds a new shift for an employee, when the shift is created, then the system generates an audible confirmation stating “New shift scheduled for [Employee Name] on [Date] from [Start Time] to [End Time]” with correct name, date, and times.
Shift Location Change Alert
Given a manager changes the location for an existing shift, when the change is saved, then the system generates an audible confirmation stating “Location updated for [Employee Name] on [Date] to [New Location]” ensuring the new location is correctly announced.
Shift Cancellation Notification
Given a manager cancels an employee’s shift, when the cancellation is confirmed, then the system generates an audible confirmation stating “Shift cancelled for [Employee Name] on [Date]” verifying the shift has been removed.
Shift Swap Confirmation
Given a manager initiates a swap between two employees' shifts, when the swap is completed, then the system generates two audible confirmations: “Shift swapped: [Employee A] now works [Date & Time of Employee B]” and “Shift swapped: [Employee B] now works [Date & Time of Employee A]”.
Slack Audio Notification
"As a restaurant manager using Slack, I want to receive audio snippets of shift updates in my Slack channel so that I can quickly verify changes without switching contexts."
Description

Integrate with Slack to send audio snippets of shift confirmations directly within designated channels or direct messages. The system should convert the confirmation text into an MP3 or WAV audio file and post it with context tags (e.g., employee name, date). This integration facilitates real-time communication and allows managers to receive confirmations within their existing workflow.

Acceptance Criteria
Posting Audio Snippet to Public Channel
Given a shift update is confirmed, when the system posts to Slack channel #schedules, then an audio snippet in MP3 or WAV format is uploaded with a playback button visible to all channel members.
Posting Audio Snippet to Direct Message
Given a shift update is confirmed for a specific manager, when the system sends a Slack DM, then an audio snippet in MP3 or WAV format appears in the direct message with a playback button.
Audio File Generation and Format
Given the text confirmation, when converting to audio, then the generated file is in MP3 or WAV format, under 2 MB, with at least 16 kHz sample rate and mono channel.
Audio Notification Context Tags
Given an audio snippet has been posted, then the accompanying Slack message includes the employee name, shift date, and action type in the message text or as metadata tags.
Error Handling for Audio Notification Failure
Given an audio generation or upload error, when vocalization fails, then the system sends a fallback text confirmation in Slack and logs the error for review.
Voice Customization Options
"As a manager, I want to customize the voice and language used for the audible confirmations so that they are clear and suit my preferences and regional requirements."
Description

Provide settings in the application for managers to customize the voice, accent, language, and speaking rate of the text-to-speech engine. These preferences should be saved per user and applied across all audible confirmations, allowing personalization and compliance with regional language requirements.

Acceptance Criteria
Voice Preference Update
Given a manager is on the Voice Settings page When they select a voice, accent, language, and speaking rate and click Save Then the system should persist these preferences and apply them to future voice confirmations.
Accent Application Confirmation
Given a manager has set the accent to British English When a shift action is confirmed Then the audible confirmation should use the British English accent.
Language Selection Enforcement
Given a manager selects Spanish as the language When a shift update occurs Then the text-to-speech engine should deliver confirmations in Spanish.
Speaking Rate Adjustment Verification
Given a manager adjusts the speaking rate to 1.5x When a shift is updated Then the confirmation audio should be played at 1.5 times the normal rate.
User Preference Isolation
Given multiple managers use the application When each manager sets their own voice preferences Then each manager's settings should be saved independently and applied only to their own confirmations.
Audio Fallback Mechanism
"As a manager, I want to receive a visual notification if audio confirmation fails so that I know my action was not confirmed audibly and can verify it manually."
Description

Implement error detection for audio generation and delivery. In case the text-to-speech service fails or the audio file cannot be delivered, the system must display a visual fallback notification on the manager's interface and log the failure. Retries should be attempted automatically up to three times with exponential backoff.

Acceptance Criteria
Successful Audio Generation and Delivery
Given a manager updates a shift and requests audio confirmation When the text-to-speech service generates and delivers the audio file successfully Then the audio plays within 2 seconds on the manager’s interface and no visual fallback is displayed
Text-to-Speech Service Failure
Given the text-to-speech API returns an error When generating the audio fails Then the system displays a visual fallback notification stating "Audio confirmation unavailable", logs the error with timestamp and code, and initiates retry #1
Audio File Delivery Failure
Given a generated audio file but a network error occurs during delivery When the audio cannot be delivered to the client Then the system shows a visual fallback notification, logs the delivery failure with details, and schedules a retry with exponential backoff
Automatic Retry Mechanism with Exponential Backoff
Given a transient failure in audio generation or delivery When a retry is triggered Then the system retries up to 3 times with delays of 1s, 2s, and 4s respectively and aborts further retries after the third failure
Failure Logging and Fallback Notification Display
Given all retry attempts have failed When the third retry fails Then the system logs a final error entry with timestamp, failure type, and retry count, and permanently displays the visual fallback notification until the next successful audio event
Confirmation Logging and Audit Trail
"As an administrator, I want to review a log of all audio confirmations so that I can audit schedule changes and troubleshoot any discrepancies."
Description

Record each audible confirmation event in the system's audit logs, including timestamp, user who performed the action, content of the message, and delivery status. Provide an interface for administrators to view and export these logs for compliance and troubleshooting.

Acceptance Criteria
Administrator Views Confirmation Logs
Given an administrator is on the Confirmation Logging interface When they select a date range and user filter Then the system displays log entries showing timestamp, user, message content, and delivery status in a sortable table
Exporting Audit Trail Entries
Given an administrator has applied desired filters When they click the export button Then the system generates and downloads a CSV file containing all matching audit trail entries with correct headers and data
Recording Successful Audible Confirmation
Given a voice confirmation is sent successfully When the message is delivered to the manager Then the system logs an entry with the correct timestamp, initiating user ID, message text, and a ‘Delivered’ status
Handling Failed Message Deliveries
Given a voice confirmation fails to deliver When the delivery attempt times out or is rejected Then the system logs an entry with timestamp, initiating user ID, message text, and a ‘Failed’ status including error details
Filtering Logs by User and Date Range
Given there are multiple log entries When an administrator filters by specific user and date range Then only entries matching the selected user and dates appear in the results

SlackVoice Bridge

Integrate voice scheduling directly into Slack channels. Managers can invoke Shiftly voice commands within Slack threads or DMs, enabling roster creation and edits without leaving their team communication hub.

Requirements

Slack App Authorization
"As a restaurant manager, I want to connect Shiftly to my Slack workspace so that I can use voice commands securely within Slack to manage shifts."
Description

Implement OAuth flow and permissions to authorize Shiftly within Slack, enabling secure interaction and voice command handling. Integrate with Slack's API to request necessary scopes, store tokens securely, and handle token refresh. This ensures that managers can install and configure the SlackVoice Bridge in their workspace, maintaining data privacy and compliance with Slack's security standards.

Acceptance Criteria
Workspace Installation Invocation
Given a manager clicks "Install Shiftly to Slack", when redirected to the Slack OAuth URL, then the URL must include the correct client_id, requested scopes, and redirect_uri. When the manager logs into Slack within the OAuth flow, then the authorization dialog must display all requested scopes clearly. If the manager cancels or denies the OAuth request, then the installation process must abort and display an appropriate error message.
Permission Granting and Token Storage
Given the manager approves all requested scopes and is redirected back to Shiftly, when the system exchanges the provided code for tokens, then the access and refresh tokens must be received within 5 seconds. The received tokens must be encrypted at rest and securely stored in the database. After successful storage, the manager must receive a confirmation message in Slack indicating successful installation.
Token Refresh Process
Given an access token has expired and an API call fails with a 401 Unauthorized, when the system detects the failure, then it must initiate the token refresh process automatically using the stored refresh token. The refreshed access and refresh tokens must replace the old tokens in storage within 2 seconds of the refresh request. Subsequent API calls using the new access token must succeed without manual intervention.
API Authorization Enforcement
Given a voice scheduling command is sent from Slack, when the backend processes the command, then it must validate the token’s scopes before executing the action. If the token lacks required scopes, then the system must log the authorization failure and return an error message to the user in Slack. Valid commands with proper scopes must be executed successfully and return confirmation messages.
App Uninstallation and Token Revocation
Given the manager uninstalls the Shiftly app from the Slack workspace, when Slack sends a tokens_revoked event to the callback endpoint, then the system must delete all associated tokens immediately. The system must send an email notification to the workspace admin within 1 hour confirming token revocation and app removal. Any subsequent API calls using the revoked tokens must be blocked and logged as unauthorized attempts.
Voice Command Recognition
"As a busy store manager, I want Slack to understand my spoken scheduling commands so that I can manage rosters without typing."
Description

Integrate a speech-to-text engine to capture and parse spoken commands within Slack channels and DMs. Transcribe voice messages accurately, detect command intents such as creating, editing, or swapping shifts, and extract relevant parameters like date, time, and employee names. This functionality ensures seamless hands-free scheduling interactions within Slack.

Acceptance Criteria
Create Shift Command in Slack DM
Given a user sends a voice message in a Slack DM containing “Create shift for [Employee] on [Date] from [StartTime] to [EndTime]”, when the message is received then the speech-to-text engine transcribes with ≥95% accuracy, the intent ‘create_shift’ is detected, and the parameters Employee, Date, StartTime, and EndTime are correctly extracted and passed to the scheduling API.
Edit Shift Command in Slack Channel
Given a manager posts a voice message in a designated Slack channel saying “Edit [Employee]’s shift on [Date] to start at [NewStartTime]”, when processed then transcription accuracy ≥95%, the intent ‘edit_shift’ is detected, Employee, Date, and NewStartTime are extracted accurately, and the edit_shift API is invoked with the correct parameters.
Handle Shift Swap Voice Command
Given a user records a voice message in a Slack thread stating “Swap my shift on [Date] with [Employee]”, when processed then transcription accuracy ≥95%, the intent ‘swap_shift’ is detected, the requester’s ID, Date, and TargetEmployee are extracted, and the swap_shift API is called successfully.
Parameter Extraction for Complex Commands
Given a voice message stating “Create shifts for [Employee1] and [Employee2] on [Date] at [StartTime] to [EndTime]”, when processed then transcription accuracy ≥95%, intent ‘create_shift’ is detected, both Employee1 and Employee2, Date, StartTime, and EndTime are extracted correctly, and two separate shift creation API calls are made.
Voice Command Error Handling
Given a voice message missing or containing ambiguous parameters (e.g., no date or unclear time), when processed then the system identifies the missing parameter, sends a Slack DM to the user requesting clarification, and only proceeds with transcription again once valid information is provided.
Voice-Driven Schedule Creation
"As a retail manager, I want to create a weekly roster by speaking in Slack so that I can quickly generate and review schedules."
Description

Enable managers to initiate new shift schedules using voice commands in Slack, specifying roles, times, and employee assignments. The system will validate input, generate the schedule via the AI engine, and post the drafted roster back into the Slack thread for review. This streamlines the shift creation process, reducing manual entry time.

Acceptance Criteria
Voice Command Initiation in Slack Thread
Given a manager is in a Slack thread and types /shiftly-voice start schedule When the manager issues a voice command specifying role, date, and time Then the system acknowledges the command and prompts for employee assignments
Role and Time Parsing Accuracy
Given the manager speaks a role and time slot When the voice engine transcribes the input Then the system correctly identifies the role, start time, and end time with 95% accuracy
Employee Assignment Confirmation
Given the AI-generated draft schedule including assigned employees When the system posts the draft back to the Slack thread Then each assignment is tagged with the corresponding employee’s Slack handle and awaits manager confirmation
Error Handling and Re-prompt
Given the manager’s voice input is unclear or missing data When the system detects incomplete or ambiguous information Then it requests clarification via a follow-up voice prompt in the Slack thread
AI Schedule Generation and Posting
Given the manager has provided valid role, time, and assignment data When the AI engine completes the schedule generation Then the system posts the finalized draft schedule in table format in the original Slack thread within 5 seconds
Voice-Driven Shift Editing
"As a restaurant manager, I want to adjust an employee’s shift by voice in Slack so that I can handle unexpected changes quickly."
Description

Allow managers to modify existing shifts through voice instructions, enabling actions such as reassigning employees, changing shift times, or deleting shifts. Changes are reflected immediately in Shiftly’s system and summarized in Slack. This reduces friction when addressing last-minute roster changes.

Acceptance Criteria
Reassign Employee via Voice Command
Given an existing shift for Employee A on YYYY-MM-DD at HH:MM is displayed in Slack When the manager issues the voice command "Reassign shift on YYYY-MM-DD at HH:MM from Employee A to Employee B" Then Shiftly updates the assignment to Employee B in the schedule database And a confirmation message is posted in the Slack thread detailing the change
Change Shift Time via Voice Command
Given a published shift for Employee C exists at HH:MM on YYYY-MM-DD When the manager issues the voice command "Change Employee C’s shift on YYYY-MM-DD from HH:MM to HH:MM" Then Shiftly updates the shift start and end times accordingly And a summary of the new shift time is posted in the Slack channel
Delete Shift via Voice Command
Given a scheduled shift for Employee D is active When the manager issues the voice command "Delete Employee D’s shift on YYYY-MM-DD at HH:MM" Then Shiftly removes the shift from the schedule database And a deletion notice is posted in the Slack thread confirming removal
Provide Voice Command Summary in Slack
Given multiple voice-driven edits are made in a session When the manager issues the voice command "Summarize my changes" Then Shiftly compiles all edits into a single summary message And posts the summary in the originating Slack thread
Handle Invalid Voice Commands Gracefully
Given a malformed or unrecognized voice command is spoken When the manager issues the invalid command in Slack Then Shiftly responds with an error message explaining the invalid input And prompts the manager to repeat or rephrase the command correctly
Real-time Confirmation Feedback
"As a shift manager, I want to receive confirmation messages in Slack after I give voice commands so that I know my scheduling changes were applied."
Description

Provide immediate feedback messages in Slack threads or DMs confirming the successful execution or errors of voice commands. Feedback includes details of created or modified shifts, or actionable error messages to guide the user. This ensures clarity and reduces miscommunication during voice interactions.

Acceptance Criteria
Shift Creation Confirmation
Given a manager issues a 'create shift' voice command in a Slack DM, when the shift is successfully scheduled, then a confirmation message must appear in the same DM within 5 seconds stating 'Shift for [Date] at [Time] created successfully' and listing the shift details.
Shift Modification Confirmation
Given a manager issues an 'edit shift' voice command in a Slack thread, when the specified shift is updated without errors, then a confirmation message must be posted in the thread within 5 seconds stating 'Shift updated to [New Date] at [New Time] successfully' with updated details.
Invalid Date Format Error Feedback
Given a manager issues a voice command with an invalid date format in a Slack DM, when the system detects the invalid input, then an error message must be returned in the DM within 3 seconds stating 'Error: Invalid date format. Please use YYYY-MM-DD.'
Overlapping Shift Error Feedback
Given a manager issues a 'create shift' command that conflicts with an existing shift in a Slack thread, when the overlap is detected, then an error message must be posted in the thread within 3 seconds stating 'Error: Overlapping shift detected for [Date] at [Time]. Please choose a different time slot.'
Unauthorized Command Error Feedback
Given a user without scheduling permissions issues any voice command in a Slack channel, when the permission check fails, then an error message must be posted in the channel within 3 seconds stating 'Error: You do not have permission to perform this action. Contact your administrator.'

VoiceAudit Log

Automatically record all voice interactions and transcriptions in a searchable audit log. Review past commands, track who made which change, and maintain compliance—all accessible via text and audio playback for transparency.

Requirements

Voice Interaction Recording
"As a manager, I want all voice commands recorded so that I can review how schedule changes were requested and maintain an accurate history of decisions."
Description

Automatically record all voice interactions between users and the application as audio files, securely storing them in a centralized repository with timestamp and user metadata. This functionality ensures that every spoken command and conversation is preserved for future reference, compliance auditing, and quality assurance.

Acceptance Criteria
Recording Initialization on User Command
Given a user issues a voice command, When the system receives audio input, Then an audio file must be created and stored with a timestamp within 500ms of start of speech detection.
Metadata Association with Audio Files
Given an audio file is created, When the recording is finalized, Then the system must attach user ID, timestamp, and session ID metadata to the file record.
Secure Storage Verification
Given an audio file and its metadata are ready, When the file is stored, Then it must be encrypted at rest and saved to the centralized repository with access restricted by user role.
Searchable Playback Access
Given a recorded voice interaction exists, When a manager searches by user ID, date range, or keyword transcript, Then the relevant audio entries and transcriptions must be returned in under 2 seconds.
Compliance Audit Retrieval
Given an audit request is made, When a compliance officer selects a date and user filter, Then the system must provide downloadable audio and transcript logs in a standardized format (e.g., MP3 and PDF) within 5 seconds.
Automated Transcript Generation
"As a compliance officer, I want to read textual transcriptions of voice commands so that I can quickly audit changes without needing to listen to audio files."
Description

Generate accurate text transcripts of all recorded voice interactions using an AI-based speech-to-text engine, linking each transcript to its corresponding audio file. This feature enables quick scanning of logs, keyword searches, and easier review without listening to full audio recordings.

Acceptance Criteria
Business Manager Reviews Daily Audit Transcript
Given an audio log entry recorded on a selected date When the manager opens the audit log for that date Then a text transcript is displayed next to the audio file with a visible play button linking to the exact recording.
Keyword Search within Transcripts
Given multiple recorded transcripts exist When the user performs a keyword search Then only those transcripts containing the keyword are returned with the matching terms highlighted and links to the corresponding audio timestamps.
Audio-Text Link Accessibility
Given a displayed transcript When the user clicks on any word or phrase Then the audio playback starts at the exact timestamp corresponding to the selected text with latency under 500ms.
Transcript Accuracy Threshold Validation
When processing a batch of 100 audio recordings Then at least 95% of transcribed words must match a manually verified transcript within a maximum 2% word error rate.
Bulk Export of Transcripts
Given a user-selected date range When the user requests export Then the system generates a downloadable ZIP containing text transcripts and linked audio files for up to 200 entries within 30 seconds.
Searchable Audit Log Interface
"As a manager, I want to search past voice commands by date and keyword so I can identify who made specific schedule changes and understand the context quickly."
Description

Provide a user interface with advanced filtering and search capabilities, allowing users to query voice interaction records and transcripts by date, user, keywords, and action types. Results should display both audio links and text snippets for efficient navigation.

Acceptance Criteria
Filter Records by Date Range
Given the user selects a start date of 2025-05-01 and an end date of 2025-05-31, when the user executes the search, then only voice audit log entries with timestamps between 2025-05-01T00:00:00Z and 2025-05-31T23:59:59Z are displayed.
Search by User and Action Type
Given the user filters by user "Manager123" and action type "Edit Schedule", when the search is performed, then only entries performed by Manager123 with action type Edit Schedule appear in the results.
Keyword Search in Transcripts
Given the user enters the keyword "no-show" in the search bar, when the search is executed, then all audit log entries whose transcript contains the exact keyword "no-show" are returned.
Audio and Text Preview Display
Given the search results list displays an entry, when the user clicks the preview icon next to that entry, then an audio player appears with the recording and the matching transcript snippet is shown below.
Combined Filters Application
Given the user applies a date range from 2025-06-01 to 2025-06-05, selects user "Clerk456", and enters keyword "swap", when the search runs, then only entries by Clerk456 between 2025-06-01 and 2025-06-05 containing "swap" in the transcript are displayed.
Role-Based Access Control
"As an administrator, I want to define which roles can access voice logs so that sensitive information is protected and audit data remains confidential."
Description

Implement role-based permissions for the audit log, ensuring that only authorized roles can view, play, or download voice recordings and transcripts. Access settings should be configurable by administrators to maintain data security and compliance.

Acceptance Criteria
Administrator Configures Access Permissions
Given an administrator on the access control settings page, when they assign or modify role permissions for viewing, playing, or downloading voice recordings and transcripts, then the changes are saved, propagated to the backend ACL within 2 seconds, and reflected in the user interface for that role.
Manager Views Audit Log Entries
Given a user with the ‘view log’ permission, when they navigate to the VoiceAudit Log page, then they can see the full list of recorded voice interactions with clickable links to playback and transcript without any errors.
Unauthorized User Attempts Access
Given a user without ‘view’, ‘play’, or ‘download’ permissions, when they attempt to access the audit log or playback/download endpoints, then the system returns a 403 Forbidden response and no log data or media is exposed.
Role Permissions Updated and Audited
Given an administrator changes a user’s role permissions, when the update is saved, then an audit entry is created capturing the admin ID, timestamp, affected user, and specific permission changes.
User Downloads Voice Recording
Given a user with ‘download’ permission, when they click the download icon for a voice recording, then the system initiates a download of a valid audio file (.mp3) within 5 seconds and logs the download action in the audit log.
In-App Audio Playback
"As a manager, I want to playback specific voice entries so that I can verify the context of schedule changes without leaving the application."
Description

Embed audio playback controls within the audit log interface, offering play, pause, rewind, fast-forward, and download functionality for each recorded interaction. Ensure smooth streaming and clear audio quality for accurate review.

Acceptance Criteria
Accessing Audio Playback Controls
Given a manager is viewing an audit log entry with an associated audio file When the manager clicks the 'Play' button Then audio playback starts within 2 seconds and Play, Pause, Rewind, and Fast-Forward controls become available
Streaming Audio Interaction Smoothly
Given a recorded audio clip longer than 5 minutes When the manager initiates playback Then audio streams continuously without buffering interruptions exceeding 2 seconds
Using Seek Controls for Audio Playback
Given audio is playing When the manager uses the Rewind or Fast-Forward controls by 10-second increments Then playback position updates accurately within 500ms and audio resumes at the new position
Downloading a Recorded Audio File
Given an audit log entry with an attached audio file When the manager clicks the 'Download' button Then the audio file downloads in MP3 format and the download completes within 5 seconds
Verifying Audio Playback Quality
Given audio playback is in progress When the manager listens to the recording Then audio clarity is clear and intelligible with no noticeable distortion or background noise exceeding -25 dB
Compliance Export and Reporting
"As a compliance officer, I want to export voice logs and transcripts so that I can share them with regulatory bodies and maintain proper audit trails."
Description

Enable exporting selected audio recordings and their transcripts in common formats (CSV, PDF, ZIP) for external audits and compliance reporting. Provide customizable export options such as date range and user filters.

Acceptance Criteria
Date Range and User Filter Export
Given a manager on the compliance export page When they select a valid start and end date and choose one or more user filters Then the system generates a downloadable export containing only the audio recordings and transcripts matching those parameters in the chosen format
Export File Format Integrity
Given the manager has initiated an export When the export completes Then the delivered files include a correctly structured CSV of metadata, individual PDF transcript files named by date and user, or a single ZIP bundling all assets as selected
Large Data Export Performance
Given an export request covering more than 1000 recordings When the system processes the export Then the complete archive is generated and ready for download within 120 seconds without errors or timeouts
Unauthorized Access Prevention
Given a user without export permissions When they attempt to access the compliance export functionality Then the system displays an authorization error and prevents any export action
Export Audit Trail Logging
Given any export action completes When the export finishes Then the system logs the export event with user ID, timestamp, filters applied, and file names in the audit log for future review
Real-Time Keyword Alerts
"As a manager, I want to receive instant notifications when critical shift changes are spoken so that I can address potential issues proactively."
Description

Detect predefined critical keywords or phrases (e.g., “cancel shift,” “call in sick”) in live voice interactions and send real-time alerts to designated users. Alerts should include a snippet of the transcript and a link to the full audio recording.

Acceptance Criteria
Alert on 'call in sick' during live session
Given a live voice interaction contains the phrase 'call in sick' When the phrase is detected by the AI engine Then an alert is sent within 5 seconds to designated users including a 20-word transcript snippet and link to the full audio recording
Alert on 'cancel shift' command
Given a user says 'cancel shift' in a live voice session When the system detects the phrase Then an alert is generated containing the speaker’s user ID, a 20-word transcript snippet, and a link to the audio recording
Multiple keyword detection in one session
Given a live session includes both 'call in sick' and 'cancel shift' When both keywords are spoken Then separate alerts for each keyword are sent to designated users in chronological order without duplication
Alert delivery reliability under poor network
Given the system experiences network latency over 500ms When a critical keyword is detected Then the system retries alert delivery up to 3 times and ensures the alert reaches designated users within 10 seconds
Searchable audit log entry created
Given a keyword detection event occurs When the alert is sent Then an audit log entry is created with timestamp, user ID, detected keyword, transcript snippet, and link to audio, and is searchable within 1 second

AutoTimesheet Validator

Automatically cross-checks synced schedule hours against punch-in/out data to detect discrepancies and anomalies before payroll processing. This reduces manual reconciliation effort, prevents payroll errors, and ensures accurate staff compensation.

Requirements

Punch Data Integration
"As a manager, I want the system to automatically import punch data so that I don't have to manually upload and ensure times are up-to-date."
Description

Automatically import punch-in/out times from time clock systems and mobile apps, align them with scheduled shifts in real time, and standardize multiple data sources and formats. Ensure all attendance records are securely stored in the timesheet database to eliminate manual uploads and keep punch data current.

Acceptance Criteria
Import Punch Data from External Time Clock System
Given a connected external time clock system When new punch-in/out records are available Then the system automatically imports all records into the timesheet database within 5 minutes
Real-Time Alignment of Punch Data with Scheduled Shifts
Given imported punch data and existing scheduled shifts When a punch record timestamp falls within 15 minutes before or after a shift start/end Then the record is correctly linked to the corresponding scheduled shift
Standardization of Multiple Data Formats
Given punch data from different sources When data is imported Then the system normalizes timestamps to ISO 8601 format and unifies employee identifiers across all records
Secure Storage of Imported Attendance Records
Given new punch-in/out records When data import completes Then all records are encrypted at rest in the timesheet database and access is restricted to authorized users
Error Handling for Missing or Corrupted Punch Data
Given incomplete or corrupted punch records When the import process detects anomalies Then the system logs detailed error entries, notifies the admin within 10 minutes, and retries import up to three times
Schedule vs. Punch Matching Algorithm
"As a manager, I want an algorithm that matches scheduled shifts to punch data so that I can quickly identify discrepancies beyond accepted tolerances."
Description

Develop an intelligent matching algorithm that compares scheduled shift hours with actual punch records to detect mismatches beyond configurable thresholds. Account for scenarios like late arrivals, early departures, breaks, and overlapping shifts, and assign a confidence score to each match to help prioritize discrepancies.

Acceptance Criteria
Exact Match Within Tolerance Window
Given scheduled shift start and end times and punch-in/out data, when the difference between scheduled and actual times is within the configured threshold, then the algorithm marks the shift as matched with a confidence score of at least 90%.
Late Arrival Detection
Given an employee’s actual punch-in time exceeds the scheduled start time beyond the configured late-threshold, when the algorithm processes the record then it flags the shift as a late arrival discrepancy with a confidence score below 90%.
Early Departure Detection
Given an employee’s actual punch-out time falls before the scheduled end time beyond the configured early-departure threshold, when the algorithm processes the record then it flags the shift as an early departure discrepancy with a confidence score below 90%.
Break Time Accounting
Given a scheduled unpaid break period within the shift and corresponding punch-out and punch-in records, when the break duration falls within the configured break window, then the algorithm deducts the break time correctly and marks the shift as matched with a confidence score of at least 90%.
Overlapping Shifts Resolution
Given two back-to-back or overlapping scheduled shifts with overlapping punch data, when the algorithm analyzes the punches then it accurately attributes hours to each shift and assigns a confidence score of at least 80% to each shift’s match.
Discrepancy Alert System
"As a manager, I want real-time alerts when schedule versus punch mismatches occur so that I can address issues before payroll processing."
Description

Provide real-time alerts and notifications to managers when discrepancies between scheduled hours and punch data are detected. Include details such as discrepancy magnitude, affected employee, and shift information. Deliver notifications via the web dashboard, email, and mobile push for immediate attention.

Acceptance Criteria
Web Dashboard Discrepancy Notification
Given a scheduled shift and corresponding punch-in/out data with a time difference over 15 minutes, when the system processes sync data, then a new alert appears on the web dashboard displaying employee name, shift date, scheduled hours, actual hours, and discrepancy magnitude in minutes.
Email Discrepancy Alert Delivery
Given a detected discrepancy, when the system generates an alert, then an email is sent to the manager within two minutes containing alert details (employee, shift information, scheduled vs actual hours, discrepancy magnitude) and a link to the dashboard.
Mobile Push Notification Alert
Given a detected discrepancy, when the system generates an alert, then a mobile push notification is delivered to the manager’s device within one minute showing employee name, shift date, and discrepancy magnitude, with a tap action that opens the alert detail view in the mobile app.
Alert Acknowledgement and Detail Review
Given an active discrepancy alert in the dashboard, when the manager clicks “Acknowledge,” then the alert status updates to “Acknowledged,” is removed from the unreviewed list, and the timestamp of acknowledgment is recorded.
High-Magnitude Discrepancy Filtering
Given multiple discrepancies detected in a batch, when the system evaluates alert generation, then only discrepancies greater than or equal to 15 minutes trigger notifications and those below are logged for later review without notifying the manager.
Exception Reason Logging
"As a manager, I want to log reasons for discrepancies so that payroll reconciliation is transparent and documented."
Description

Allow managers and employees to review flagged discrepancies and log reason codes or comments explaining variances. Maintain a standardized list of reason codes (e.g., forgot to punch, scheduled break, approved overtime) and support custom codes. Timestamp and link all entries to user accounts for full auditability.

Acceptance Criteria
Manager Logs Standard Reason Code for Discrepancy
Given a timesheet discrepancy flag exists for an employee When a manager selects a reason code from the standardized list Then the selected code, manager ID, timestamp, and discrepancy reference are recorded successfully
Employee Creates and Applies Custom Reason Code
Given the standardized reason code list does not cover the case When an employee enters a custom reason code and comment Then the custom code is saved to the list, linked to the discrepancy, timestamped, and attributed to the employee
Audit Trail Captures All Reason Entries
When any user adds or edits a reason code or comment Then the system logs the action type, user ID, timestamp, original and updated values, and discrepancy ID in an auditable trail
Manager Views Logged Reasons for a Discrepancy
Given a discrepancy record exists When the manager requests the reason log Then the system displays all entries sorted by timestamp, showing user, reason code, comments, and change history
Prevent Submission of Empty Reason Entries
When a user attempts to submit a reason entry without selecting or entering a code Then the system displays a validation error and prevents submission until a valid code or comment is provided
Payroll Submission Validation
"As a payroll specialist, I want validation to block payroll exports with unresolved anomalies so that I can ensure payment accuracy."
Description

Before exporting timesheets to the payroll system, perform a final validation that ensures all discrepancies have been addressed or approved. Block exports if unresolved anomalies exist and generate a summary report of pending items. Integrate this validation seamlessly with existing payroll export APIs.

Acceptance Criteria
All Discrepancies Resolved Prior to Export
Given synced schedule hours and punch data, when final validation runs then no unresolved discrepancies remain
Pending Approvals Trigger Report Generation
Given one or more discrepancy approvals are pending, when validation completes then a summary report listing each pending approval is generated
Export Blocked on Unresolved Anomalies
Given unresolved anomalies exist, when a user attempts payroll export then the system blocks export and displays an error notification
Seamless API Integration During Validation
Given the payroll export API endpoint is reachable and validation passes, when export is initiated then timesheet data is transmitted via the existing API without manual intervention
Summary Report Contains Detailed Pending Items
Given pending items exist, when the summary report is generated then it includes employee ID, date, discrepancy type, and current approval status for each item
Audit Trail and Reporting
"As a compliance officer, I want audit reports of validator actions so that I can review and demonstrate regulatory compliance."
Description

Generate comprehensive audit reports that document all validator actions, including data imports, discrepancy detections, alerts sent, and manual overrides. Enable filtering by date range, employee, manager, and discrepancy type, and support export to CSV and PDF for compliance reviews.

Acceptance Criteria
Comprehensive Audit Report Generation
Given a user requests an audit report for a specific date range, When the report is generated, Then it includes all data imports, discrepancy detections, alerts sent, and manual overrides within that range; And the report totals match the underlying event logs.
Filter Reports by Employee
Given an audit report view, When the user selects a specific employee filter, Then only entries related to that employee appear in the report; And record counts update accordingly.
Filter Reports by Manager
Given an audit report view, When the user filters by manager name, Then the report displays only actions performed under that manager’s oversight; And no unrelated entries are shown.
Filter Reports by Discrepancy Type
Given an audit report view, When the user applies a discrepancy type filter (e.g., time mismatch, missing punch), Then the report lists only records matching that type; And summary statistics reflect the selection.
Export Reports to CSV and PDF
Given a generated audit report, When the user clicks export and chooses CSV or PDF, Then the downloadable file matches the on-screen report formatting; And the file opens without errors in standard viewers.
Configurable Discrepancy Thresholds
"As an administrator, I want to set custom discrepancy thresholds so that the matching algorithm aligns with our operations."
Description

Enable administrators to configure global and per-location thresholds for acceptable time variances between scheduled and recorded hours. Support threshold settings by minutes or percentage and apply them dynamically during matching to reduce false positives across different operational contexts.

Acceptance Criteria
Global Minute-Based Threshold Configuration
Given an administrator navigates to Settings > Discrepancy Thresholds When they set the global threshold to 10 minutes and save Then the system stores the 10-minute threshold as the default for all locations
Location-Specific Percentage-Based Threshold Configuration
Given an administrator selects Location A in the configuration panel When they input 5% as the threshold and save Then the system stores a 5% threshold for Location A without altering global settings
Default Fallback to Global Threshold
Given a location has no specific threshold set When discrepancy matching runs for that location Then the system applies the global threshold to its schedule-vs-punch data
Threshold Application Under Variance
Given a scheduled shift of 8 hours and a recorded duration of 7 hours 55 minutes When the global threshold is 10 minutes Then the system considers the variance of 5 minutes as within threshold and does not flag a discrepancy
Threshold Exceeds Flagging
Given a scheduled shift of 8 hours and a recorded duration of 7 hours 45 minutes When the global threshold is 10 minutes Then the system considers the variance of 15 minutes as outside threshold and flags a discrepancy for manual review
Invalid Threshold Input Handling
Given an administrator enters '-5' minutes or non-numeric characters into any threshold field When they attempt to save the configuration Then the system displays an error indicating invalid input and prevents saving until corrected

Overtime Alert

Generates real-time notifications when team members approach or exceed overtime thresholds. Managers and payroll staff can proactively review and approve overtime, maintaining compliance with labor regulations and avoiding unexpected payroll costs.

Requirements

Real-time Overtime Threshold Monitoring
"As a restaurant manager, I want to receive an instant alert when an employee is nearing overtime so that I can adjust schedules before exceeding labor regulations."
Description

The system continuously monitors employees' logged hours against predefined overtime thresholds and generates immediate alerts when thresholds are approached or exceeded. Alerts appear in the manager's dashboard and trigger notifications via configured channels. Integration with time-tracking data ensures accuracy and timeliness. This proactive oversight helps managers prevent inadvertent labor compliance violations and reduces unexpected payroll costs.

Acceptance Criteria
Approaching Overtime Threshold Alert
Given an employee’s logged hours reach 90% of the weekly overtime threshold, when the system monitors time entries in real-time, then a notification is displayed on the manager’s dashboard and sent via all configured channels within 1 minute.
Overtime Exceeded Alert
Given an employee’s logged hours exceed the predefined overtime threshold, when the threshold is breached, then an immediate alert is generated on the dashboard and sent via email and SMS within 30 seconds.
Time-Tracking Data Integration Accuracy
Given time entries from the external time-tracking system, when data is synced, then the system reflects total hours within a 1% margin of error and updates alerts accordingly.
Dashboard Alert Visibility
Given any active overtime alert, when a manager views the dashboard, then an alert indicator appears in the overtime section with employee name, hours logged, and threshold status.
Notification Channel Reliability
Given an overtime alert is generated, when notifications are sent, then at least 99% of email and SMS alerts are successfully delivered, with delivery status logged in the system.
Custom Overtime Rule Configuration
"As a payroll administrator, I want to configure custom overtime rules per role so that the system accurately reflects our company policies and legal requirements."
Description

Managers can define and adjust company-specific overtime rules, including daily, weekly, and workweek thresholds, as well as rule variations for different roles or employee classifications. The system stores and applies these rules to calculate overtime eligibility in real time. This flexibility accommodates varying local labor regulations and internal policies.

Acceptance Criteria
Admin Defines Daily Overtime Threshold
Given the manager sets a daily overtime threshold of 8 hours for all employees, when any employee’s logged hours exceed 8 in a single day, then the system flags hours beyond 8 as overtime and records them accordingly.
Override Overtime Rule for Role
Given the manager configures a 10-hour daily threshold for ‘Senior Cook’ role and 8-hour threshold for ‘Line Cook’, when hours are logged for each role, then the system applies the correct threshold per role and calculates overtime separately.
Apply Weekly Overtime Rule
Given the manager defines a weekly overtime threshold of 40 hours, when an employee’s cumulative hours in a calendar week exceed 40, then the system automatically marks the excess hours as overtime and notifies the manager.
Validate Rule Persistence after System Restart
Given the manager has saved custom daily and weekly overtime rules, when the system restarts or the manager logs out and logs back in, then all configured rules remain intact and are available for further editing or automatic application.
Alert Generated on Overtime Breach
Given an employee is within one hour of the configured overtime threshold, when the system detects this condition in real time, then it sends a notification to the manager and payroll staff alerting them of the impending overtime.
Multi-channel Overtime Notifications
"As a store manager, I want to receive overtime alerts via SMS and email so that I never miss a critical notification regardless of where I am."
Description

Enable notifications to be sent via multiple channels—email, SMS, and in-app push—to ensure managers and payroll staff receive alerts through their preferred medium. Users can opt in or out of channels and set notification frequency and escalation rules. This ensures critical overtime alerts are seen promptly, regardless of user location.

Acceptance Criteria
User Opts into Preferred Notification Channels
Given a manager on the notification settings page who selects email and in-app push but deselects SMS, When the system saves these preferences, Then only email and in-app push notifications are sent for overtime alerts, and no SMS notifications are sent.
Real-time SMS Alert on Overtime Threshold Breach
Given a team member’s hours reach 39 in a week and the overtime threshold is set at 40 hours, When the next hour is logged, Then the system sends an SMS notification to the manager within 30 seconds stating the employee is approaching overtime.
Configurable Email Alert Frequency
Given a payroll staff user sets email frequency to ‘hourly’, When multiple employees approach overtime within an hour, Then the system consolidates alerts into a single email sent at the top of the next hour, and no additional emails are sent until the next scheduled hour.
Automatic Escalation via In-App Push
Given a manager receives an in-app push alert for an employee exceeding overtime but does not acknowledge it within 10 minutes, When the 10-minute window elapses, Then the system escalates by sending the same alert to the manager’s alternate channel as defined in escalation rules.
Respecting User Opt-Out Choices
Given a user has opted out of all notification channels, When an overtime alert is triggered, Then no notifications are sent, and the system logs the suppressed alert for audit purposes.
Overtime Approval Workflow
"As a payroll supervisor, I want to review and approve overtime alerts within the system so that I can ensure only authorized overtime is paid."
Description

Implement a structured approval process for overtime alerts, allowing managers to review, approve, or deny overtime requests directly within the system. The workflow logs decisions, captures comments, and automatically updates schedules and payroll data. Notifications are sent to relevant stakeholders upon approval or denial to maintain transparency.

Acceptance Criteria
Manager Reviews Overtime Requests
Given pending overtime requests exist When manager navigates to the Overtime Approval Workflow Then the system lists all requests with employee name, date, requested hours, and status 'Pending'
Manager Denies Overtime Request
Given a selected overtime request When manager clicks 'Deny' and enters a denial comment Then the request status updates to 'Denied' and the comment is logged
Manager Approves Overtime Request
Given a selected overtime request When manager clicks 'Approve' Then the request status updates to 'Approved', payroll data reflects the approved hours, and notifications are sent to employee and payroll staff
Overtime Threshold Exceeded Alert Trigger
Given an employee’s scheduled hours exceed the configured overtime threshold When the schedule is generated or updated Then the system automatically generates an overtime alert and queues it for approval
Notification Dispatch upon Decision
Given a decision on an overtime request When manager approves or denies the request Then the system sends notification emails to the employee, manager, and payroll team within one minute
Audit Log for Overtime Decisions
Given an overtime request has been decided When manager finalizes the decision Then the system appends an audit log entry with request ID, decision, timestamp, manager ID, and comment
Overtime Reporting Dashboard
"As an operations manager, I want to view overtime summaries and trends so that I can optimize staffing budgets and reduce excess labor costs."
Description

Provide a comprehensive dashboard summarizing overtime statistics, including current overtime hours by employee, departmental trends, forecasted overtime costs, and historical reports. The dashboard offers filters, visual charts, and export options to enable managers and finance teams to make data-driven staffing and budgeting decisions.

Acceptance Criteria
Real-Time Overtime Overview
Given the manager opens the Overtime Reporting Dashboard, when data is retrieved from the database within 2 seconds, then the dashboard displays each employee’s current overtime hours in a sortable table.
Department and Date Filter Application
Given the dashboard displays data, when the manager selects a department and a date range, then the dashboard updates to show only overtime statistics matching the selected filters within 1 second.
Forecasted Overtime Cost Calculation
Given the current schedule and historical trends, when the manager requests a 4-week forecast, then the system calculates and displays projected overtime costs with a margin of error no greater than 5%.
Export Overtime Report to CSV
Given the filtered or full dashboard view, when the manager clicks the “Export CSV” button, then a CSV file with all visible data is generated and downloaded within 3 seconds and contains correct headers and values.
Historical Overtime Trends Visualization
Given the dashboard is loaded, when the manager switches to the “Historical Trends” tab, then the system displays an interactive line chart showing monthly overtime hours for the past 12 months, with tooltips for each data point.

MultiPaycode Mapper

Enables granular mapping of scheduled hours to various pay codes such as shift differentials, holiday premiums, and meal breaks. Simplifies complex payroll configurations, ensuring each hour is categorized correctly for accurate wage calculation.

Requirements

Pay Code Configuration UI
"As a restaurant manager, I want an intuitive interface to configure pay codes for various shift types so that I can ensure accurate payroll setup without manual spreadsheet errors."
Description

Provide a user interface for managers to define and map pay codes such as shift differentials, holiday premiums, and meal breaks. The UI should enable creating, editing, and deleting pay code rules; associating codes with employee roles, dates, and time ranges; and previewing the mapping results before saving.

Acceptance Criteria
Creating a New Pay Code Rule
Given the manager is on the Pay Code Configuration UI, When they click 'Add Pay Code', fill in the pay code name, select pay code type, define time range and associated roles, and click 'Save', Then the new pay code rule appears in the list with the correct details visible.
Editing an Existing Pay Code Rule
Given an existing pay code rule in the list, When the manager selects 'Edit', updates the time range or pay code type, and clicks 'Save', Then the changes are reflected in the rule list and persist after a page reload.
Deleting a Pay Code Rule
Given an existing pay code rule, When the manager clicks 'Delete' and confirms the action, Then the rule is removed from the list and no longer applies in payroll calculations.
Associating Pay Code with Employee Role and Date Range
Given the manager is configuring a pay code rule, When they select one or more employee roles and define start and end dates for applicability, Then the rule is correctly associated and displayed under those roles and dates.
Previewing Mapping Results before Saving
Given the manager has configured or edited a pay code rule, When they click 'Preview Mapping', Then a schedule preview shows hours mapped to the correct pay code categories for a sample employee and date range, matching expected wage calculations.
Automated Pay Code Allocation Engine
"As a payroll administrator, I want the system to automatically allocate pay codes to each scheduled hour so that I can reduce manual intervention and minimize payroll discrepancies."
Description

Develop a backend engine that automatically applies configured pay codes to scheduled hours. It should process schedule data, match time segments to pay code rules, and assign codes to each hour block. The engine should support batch processing for entire schedules and real-time updates when shifts change.

Acceptance Criteria
Standard Shift Differential Mapping
Given a scheduled shift with a 1.2x shift differential rule configured for weekday evenings, when the Automated Pay Code Allocation Engine processes the schedule, then all hours between 18:00 and 22:00 receive the "weekday-evening-differential" pay code with a 1.2 multiplier.
Holiday Pay Code Assignment
Given a scheduled shift on a recognized holiday date, when the schedule is run through the engine, then each hour of the shift is tagged with the "holiday-premium" pay code, and the payroll export includes the correct holiday rate.
Meal Break Pay Code Detection
Given a shift exceeding six continuous hours without a scheduled break, when processing occurs, then the engine automatically assigns the "meal-break-deduction" pay code for the appropriate hour block as per policy.
Real-time Shift Adjustment Handling
Given a manager edits a shift's start time via the mobile app, when the change is saved, then the engine reprocesses only the affected shift, updating pay code assignments within 5 seconds and reflecting changes in the user's view.
Batch Processing Performance
Given a batch of 1000 scheduled shifts for multiple locations, when the engine is executed in batch mode, then all shifts are processed and pay codes are assigned in under 60 seconds without errors, and a summary report indicates zero unassigned hours.
Pay Code Override Rules
"As a manager, I want to manually adjust pay code allocations for specific shifts so that I can correct exceptions and ensure payroll accuracy."
Description

Implement functionality to allow manual overrides of automatically assigned pay codes. Managers must be able to adjust pay code assignments at the shift or hour level, with the system tracking who made the override and why.

Acceptance Criteria
Single Shift Pay Code Override by Manager
Given a scheduled shift with an auto-assigned pay code When a manager manually selects a different pay code and provides a valid reason Then the system updates the shift’s pay code, records the override reason, and reflects the change in payroll calculations
Hourly Pay Code Adjustment
Given a shift spanning multiple pay code zones When a manager selects specific hours within the shift to override the auto-assigned pay code Then the system updates only the selected hours with the new pay code and recalculates payroll accordingly
Unauthorized Override Attempt
Given a user lacking pay code override permissions When the user attempts to change a pay code manually Then the system prevents the override and displays an 'Insufficient Permissions' error message
Override Audit Logging
Given any manual pay code override When the override is submitted Then the system logs the override event with user ID, timestamp, original and new pay codes, and the manager’s override reason in the audit trail
Conflict Resolution on Override
Given an override that conflicts with existing pay code rules When a manager attempts the conflicting override Then the system displays a conflict warning, requires explicit confirmation to proceed, and upon confirmation applies the override while logging the conflict resolution details
Pay Code Reporting and Export
"As an accountant, I want to generate and export detailed pay code reports so that I can reconcile hours and prepare payroll submissions efficiently."
Description

Create reporting features that summarize pay code utilization across schedules. Reports should list hours per pay code, breakdown by employee and date, and export options in CSV/Excel formats for payroll systems.

Acceptance Criteria
Generate Pay Code Summary for All Employees
Given the manager navigates to the Pay Code Reporting page and selects a valid date range, When the report is generated, Then the system displays a summary table listing each pay code with its total scheduled hours across all employees; And the data in the table matches the hours mapped to each pay code in the schedule.
Filter Pay Code Report by Employee and Date Range
Given the manager applies an employee filter and specifies a start and end date, When the report is generated, Then only entries for the selected employee within the specified date range are displayed; And the total hours per pay code reflect only that employee’s scheduled hours.
Export Pay Code Report to CSV
Given the manager clicks the 'Export CSV' button on the Pay Code Reporting page, When the export process completes, Then the browser downloads a UTF-8 encoded CSV file named 'pay_code_report_{startDate}_{endDate}.csv'; And the CSV contains columns for Pay Code, Employee, Date, and Hours matching the on-screen report.
Export Pay Code Report to Excel
Given the manager clicks the 'Export Excel' button on the Pay Code Reporting page, When the export process completes, Then the browser downloads an .xlsx file named 'pay_code_report_{startDate}_{endDate}.xlsx'; And the Excel file includes a sheet titled 'PayCodeReport' with columns for Pay Code, Employee, Date, and Hours matching the displayed report.
Drill-Down into Employee Pay Code Details
Given the manager clicks on an employee’s row in the Pay Code summary report, When the drill-down view opens, Then the system displays a detailed table showing each date, shift identifier, pay code, and hours for that employee; And the totals in the drill-down view sum correctly to the summary report values.
Validation and Error Handling
"As a user, I want the system to alert me of any pay code configuration errors so that I can fix issues before they affect payroll."
Description

Build validation rules to detect conflicting or missing pay code mappings, invalid date/time ranges, and overlapping rules. Provide clear error messages and guidance for resolution.

Acceptance Criteria
Conflicting Pay Code Detection
Given a user creates two pay code mappings for overlapping time ranges, when the user attempts to save, then the system identifies the conflict, prevents saving, and displays an error message specifying the conflicting pay codes and overlapping time period.
Missing Pay Code Alert
Given scheduled hours without an assigned pay code, when the user tries to save the schedule, then the system flags the unmapped hours, prevents saving, and presents a clear instruction to assign a pay code.
Invalid Date/Time Range Validation
Given a pay code rule with a start time that is later than the end time or dates outside of the valid schedule period, when the user attempts to save, then the system rejects the entry and shows an error message detailing the invalid date/time range.
Overlapping Rule Identification
Given multiple pay code rules defined for the same employee that overlap in time, when the user saves the rules, then the system detects the overlaps, lists the conflicting rules, and prevents saving until resolved.
User-friendly Error Messaging
Given any validation error occurs, when the system displays the error, then the message clearly identifies the issue, highlights the affected field, and provides actionable guidance to resolve the error.

PayForecast Dashboard

Provides a live preview of upcoming payroll costs based on the current schedule, including anticipated overtime and bonuses. Helps managers make informed staffing decisions to stay within budget and optimize labor expenses before finalizing the roster.

Requirements

Real-Time Payroll Projection
"As a manager, I want to see live payroll cost projections while I adjust the shift schedule so that I can stay within budget and avoid unexpected labor expenses."
Description

Provide a continuously updating view of projected payroll costs based on the current shift schedule, including base wages, overtime, and estimated bonuses. This feature integrates directly with the scheduling engine to surface cost impacts immediately as managers build or adjust rosters, enabling informed staffing decisions before finalizing shifts.

Acceptance Criteria
Updating Payroll on Shift Addition
Given a manager adds a new shift and saves it, when the schedule updates, then the payroll projection refreshes within 2 seconds to include the new shift’s base wages, overtime, and bonuses.
Real-Time Cost Adjustment on Shift Modification
Given a manager modifies an existing shift’s start or end time, when the change is saved, then the payroll projection updates instantly to show the revised cost difference.
Including Estimated Overtime in Projections
Given the total scheduled hours for an employee exceed their standard weekly hours, when the schedule is viewed, then the payroll projection displays estimated overtime pay separately and includes it in the total cost.
Dynamic Projection with Date Range Selection
Given a manager selects a custom date range for the schedule preview, when the date range is applied, then the payroll projection updates immediately to reflect only the shifts within that period.
Budget Threshold Alert on Finalization
Given the projected payroll cost exceeds the predefined budget threshold, when the manager attempts to finalize the roster, then the system displays a clear alert indicating the budget overrun and highlights the cost categories.
Overtime & Bonus Forecast
"As a store manager, I want to view projected overtime and bonus costs for employees so that I can minimize labor overruns and allocate resources more effectively."
Description

Automatically calculate and display anticipated overtime hours and bonus payouts for each employee and across the entire schedule. By highlighting high-cost items, the system helps managers identify and mitigate potential overspending before publishing the roster.

Acceptance Criteria
Individual Employee Overtime Calculation
Given an employee is scheduled for more than 40 hours in a week When the PayForecast Dashboard loads the schedule Then the system calculates overtime hours beyond 40 and displays the overtime cost at the configured rate next to the employee’s total forecast cost
Employee Bonus Payout Estimation
Given an employee is eligible for bonus rates per shift When the PayForecast Dashboard processes the schedule Then the system sums all bonus payouts for each employee and displays the total bonus amount next to the employee’s forecast cost
Aggregate Schedule Cost Overview
Given the full roster for the pay period is finalized When the dashboard refreshes Then the system displays the total base wage cost, total overtime cost, and total bonus cost for the entire schedule
High-Cost Item Highlight
Given any employee’s forecasted overtime or bonus cost exceeds the preset alert threshold When the dashboard displays the cost summary Then the specific cost line item is highlighted in red and a warning tooltip explains the overage
Dynamic Cost Update on Schedule Edits
Given a manager adds or modifies a shift in the schedule When the change is saved Then the PayForecast Dashboard recalculates and updates the overtime and bonus forecast within 2 seconds
Budget Threshold Alerts
"As a shift planner, I want to receive alerts when my projected payroll nears my budget cap so that I can adjust staffing levels proactively and maintain cost control."
Description

Allow managers to set configurable budget thresholds and trigger real-time alerts when projected payroll costs approach or exceed the defined limits. Alerts appear both in the PayForecast dashboard and as push notifications within the scheduling interface.

Acceptance Criteria
Configuring Budget Threshold in PayForecast Dashboard
Given the manager is on the PayForecast settings panel, when they enter a valid numerical budget threshold and click save, then the threshold is persisted and displayed in the dashboard settings.
Threshold Approaching Alert Notification
Given the projected payroll cost reaches 90% of the configured threshold, when the schedule preview updates, then a warning alert appears on the PayForecast dashboard indicating impending budget exceedance.
Budget Exceeded Push Notification
Given the projected payroll cost exceeds the configured threshold, when the next schedule check is processed, then a push notification is sent to the manager’s mobile device and an alert is displayed in the scheduling interface.
Updating Budget Threshold Settings
Given the manager revisits the budget settings, when they modify the threshold value and save, then the new threshold replaces the old one and subsequent alerts use the updated value.
Viewing Alert History in PayForecast Dashboard
Given the manager accesses the PayForecast dashboard, when clicking the alert history tab, then a chronological list of past threshold alerts with timestamps and statuses is displayed.
What-If Scheduling Scenarios
"As a regional manager, I want to simulate alternative schedules and compare projected costs so that I can choose the most cost-efficient staffing plan."
Description

Enable managers to create multiple hypothetical scheduling scenarios and compare their payroll impacts side by side. Each scenario preserves the current roster state, allowing experimentation with different shift assignments, employee swaps, and coverage levels without affecting the live schedule.

Acceptance Criteria
New What-If Scenario Creation
Given the manager is on the PayForecast Dashboard When they click 'Create What-If Scenario' and input a scenario name Then a new scenario is created preserving the current roster state and visible in the scenario list
Editing Scenario Without Affecting Live Schedule
Given an existing what-if scenario When the manager modifies shift assignments or swaps employees within that scenario Then the changes are saved only to that scenario and the live schedule remains unchanged
Comparing Payroll Impacts Side by Side
Given two or more what-if scenarios exist When the manager selects scenarios to compare Then the dashboard displays a side-by-side comparison of total payroll cost, overtime, and bonuses for each scenario
Saving and Reloading Scenarios
Given a manager has created and modified a what-if scenario When they save and later reload the dashboard Then the scenario persists with all modifications intact and ready for further editing
Deleting a What-If Scenario
Given an existing what-if scenario When the manager chooses to delete the scenario and confirms the action Then the scenario is removed from the list and no longer available for comparison
Payroll Report Export
"As a finance coordinator, I want to export payroll forecasts into a report so that I can share detailed labor cost analysis with stakeholders and streamline budget reconciliation."
Description

Support exporting projected payroll data, including cost breakdowns by shift, employee, and pay category, to common formats (CSV, PDF). Reports are customizable by date range and include visual charts for quick executive review.

Acceptance Criteria
CSV Export for Custom Date Range
Given a manager selects a start and end date and chooses CSV format, when they click 'Export', then the system generates and downloads a CSV file containing projected payroll data only for shifts within the selected range with correct columns: Date, Employee, Shift Hours, Pay Category, and Total Cost.
PDF Export with Visual Charts
Given a manager sets a date range and selects PDF format with charts enabled, when they export, then the system produces a PDF that includes a table of projected costs by shift and embedded bar and pie charts summarizing cost distribution per pay category and overtime.
Shift-Level Cost Breakdown Validation
Given the schedule contains multiple shifts per day, when generating the report, then each shift's projected cost should appear as a separate line item with correct calculation of hours times pay rate and any applicable shift differentials.
Employee-Level Cost Aggregation
Given multiple shifts assigned to the same employee within the selected range, when exporting the report, then the system aggregates costs per employee and includes a summary section listing each employee's total projected pay.
Pay Category Segmentation Accuracy
Given the report configuration includes pay categories (regular, overtime, bonuses), when the report is generated, then each cost entry is tagged correctly by category and the report includes a subtotal for each pay category.

Exception Resolution Center

Aggregates and highlights any discrepancies between scheduled, actual, and synced hours in a single dashboard. Payroll administrators can review, comment, and resolve exceptions quickly, streamlining the approval workflow and eliminating last-minute corrections.

Requirements

Centralized Exception Dashboard
"As a payroll administrator, I want a dashboard that displays all scheduling exceptions in one place so that I can quickly assess and prioritize issues without switching between multiple systems."
Description

Provide a unified interface that aggregates and highlights discrepancies between scheduled, actual, and synced hours from multiple sources in real-time. The dashboard should allow payroll administrators to view exception counts, filter by date range, location, and employee, and display key metrics such as total exceptions and average resolution time. Integrate with existing scheduling and time-tracking modules to ensure data consistency and update the dashboard dynamically as new data arrives.

Acceptance Criteria
Viewing Aggregated Exceptions Across All Locations
Given payroll administrator is on the Centralized Exception Dashboard, When the system aggregates data from all connected locations, Then the dashboard displays a combined list of exceptions sorted by severity, location name, and exception count.
Filtering Exceptions by Date Range and Employee
Given a selected date range and chosen employee filter, When the administrator applies filters, Then the dashboard updates to show only exceptions matching the date range and employee criteria within two seconds.
Real-Time Exception Data Refresh
Given new scheduling or time-tracking data is received, When the data source is updated, Then the dashboard automatically refreshes affected exception figures without manual intervention.
Integration with Scheduling and Time-Tracking Modules
Given both scheduling and time-tracking modules are active, When the dashboard fetches data, Then discrepancies are identified correctly and no data gaps exist between modules.
Reviewing Exception Metrics and Resolution Time
Given the dashboard displays key metrics, When an administrator views metrics panel, Then total exceptions, average resolution time, and open versus resolved counts are shown accurately.
Priority-based Exception Alerts
"As a payroll administrator, I want to receive alerts for critical scheduling discrepancies so that I can address the most impactful issues before payroll runs."
Description

Enable automatic prioritization of exceptions based on predefined criteria such as severity, number of hours discrepant, and proximity to payroll deadlines. The system should generate visual alerts and notifications for high-priority exceptions and allow administrators to customize alert rules. Integrate with email and mobile push notifications to ensure timely awareness and reduce payroll processing delays.

Acceptance Criteria
High-Priority Exception Alert Trigger
Given an exception with severity 'High' and hour discrepancy ≥ 4 occurring within 48 hours of the payroll cutoff, when the system processes exceptions, then a visual red alert is generated in the dashboard and both email and mobile push notifications are sent to the administrator.
Custom Alert Rule Creation and Activation
Given an administrator defines a new alert rule specifying severity 'Medium' and a discrepancy threshold of 2 hours, when the rule is saved and activated, then it appears in the alert settings list and triggers alerts for future matching exceptions.
Email Notification for High-Priority Exceptions
Given a high-priority exception is detected, when the alert is triggered, then the system sends an email within 5 minutes to configured recipients containing exception details and priority level, and no email delivery errors are recorded.
Mobile Push Notification Receipt
Given a high-priority exception occurs, when the notification is dispatched, then administrators receive a mobile push notification within 2 minutes containing the exception summary and a direct link to the exception resolution center.
Dashboard Prioritization and Sorting
Given multiple exceptions with varying priorities, when viewed in the exception resolution center, then high-priority exceptions are listed at the top, visually distinguished, and can be filtered by priority.
Collaborative Commenting Module
"As a payroll administrator, I want to discuss exceptions with my team directly in the system so that we can collaborate on resolutions without using external communication tools."
Description

Implement an inline commenting system within the Exception Resolution Center that allows payroll administrators and managers to discuss specific exceptions. Comments should be timestamped, linked to individual exceptions, support threaded replies, and enable @mentions to involve additional stakeholders. Ensure comments are searchable and exportable for audit and compliance purposes.

Acceptance Criteria
Adding an Inline Comment
Given a payroll administrator views an exception in the dashboard, When they click the comment icon and submit a comment, Then the comment is saved with a timestamp, linked to the specific exception, and displayed in the exception detail pane.
Threaded Reply to Comment
Given a manager sees an existing comment on an exception, When they click reply and submit their response, Then the reply appears nested under the original comment, shows the responder’s name and timestamp, and maintains the comment thread order.
Mentioning a Stakeholder
Given a user composes a comment and types '@', When they select a stakeholder from the autocomplete list and post the comment, Then the mentioned user receives a notification, and the comment displays the @mention linked to the stakeholder’s profile.
Searching Comments
Given multiple comments exist across exceptions, When an administrator uses the search bar and enters a keyword or stakeholder name, Then only comments containing the keyword or mention are returned, highlighting matches and linking each result back to its exception.
Exporting Comment Logs
Given the administrator selects the export option for an exception or date range, When the export is generated, Then a CSV file is downloaded containing all comments, timestamps, authors, and linked exception IDs, matching the selected filters.
Resolution Workflow Actions
"As a payroll administrator, I want to resolve exceptions through a set of predefined actions so that I can efficiently correct errors and ensure accurate payroll processing."
Description

Provide actionable workflow options for each exception, including approve, adjust hours, escalate to manager, or mark as resolved. Each action should trigger configurable status updates and notifications, log the decision maker and timestamp, and update the underlying time records accordingly. Ensure workflow states are configurable to match organizational policies.

Acceptance Criteria
Approve Exception Action by Payroll Administrator
Given an exception is pending approval, when the payroll administrator clicks "Approve", then the exception status updates to "Approved", a notification is sent to stakeholders, the decision maker and timestamp are logged, and the time records are updated accordingly.
Adjust Hours for an Exception
Given an exception with incorrect hours, when the payroll administrator selects "Adjust Hours" and submits corrected hours, then the exception status updates to "Adjusted", a notification is sent to stakeholders, the decision maker and timestamp are recorded, and the time records reflect the updated hours.
Escalate Exception to Manager
Given an exception requiring managerial review, when the payroll administrator selects "Escalate to Manager", then the exception status changes to "Escalated", the designated manager is notified, the decision maker and timestamp are logged, and time records remain unchanged until final resolution.
Mark Exception as Resolved
Given an exception has been resolved, when the payroll administrator marks it as "Resolved", then the exception status updates to "Resolved", stakeholders receive a notification, the decision maker and timestamp are logged, and the underlying time records are finalized.
Configure Workflow States per Organization Policy
Given organizational policies vary, when an administrator configures custom workflow states in settings, then those states appear in the resolution dropdown, actions trigger appropriate status transitions and notifications per configuration, and all decisions log the decision maker and timestamp.
Audit Trail and Reporting
"As a compliance officer, I want to access audit logs and generate exception reports so that I can verify process adherence and provide documentation for audits."
Description

Maintain a comprehensive audit log of all actions taken within the Exception Resolution Center, including data imports, comment entries, status changes, and resolutions. Provide a reporting interface to generate custom reports on exception trends, resolution times, and user activity. Reports should be exportable in CSV and PDF formats and scheduleable for automated delivery.

Acceptance Criteria
Logging Data Imports
Given a payroll administrator uploads a shift data file, When the import operation completes, Then an audit log entry is created capturing the file name, upload timestamp, and user ID.
Tracking Comment Entries
Given a user adds a comment to an exception, When the comment is saved, Then the audit trail records the comment text, associated exception ID, user ID, and timestamp.
Recording Status Changes
Given an exception’s status is updated, When the status change is confirmed, Then the audit log captures the previous status, new status, user ID, timestamp, and change reason.
Resolution Action Audit
Given an exception is marked as resolved, When the resolution is applied, Then the system logs the resolver’s user ID, resolution timestamp, and any resolution notes in the audit log.
Report Generation and Export
Given a user generates a custom report, When the report is exported to CSV or PDF, Then an audit entry logs the report parameters, export format, generation time, user ID, and file location.

Integration API Sandbox

Offers a secure, sandboxed environment for testing and configuring custom connections to third-party payroll systems. Developers can validate data mappings and workflows in real time, reducing deployment time and ensuring smooth, error-free integration.

Requirements

Sandbox Environment Provisioning
"As a developer, I want an isolated sandbox environment to safely test integrations so that I don't risk corrupting production data."
Description

The system shall provide isolated sandbox environments for each developer where they can configure and test API integrations with third-party payroll systems. This environment mimics production data schemas and supports secure testing without impacting live data.

Acceptance Criteria
Developer Requests New Sandbox Environment
Given a developer with valid credentials When the developer requests a new sandbox environment Then the system provisions an isolated sandbox instance within 60 seconds and provides the developer with unique access credentials
Developer Configures Schema Mapping
Given the developer is logged into their sandbox When the developer uploads or defines custom data schema mappings Then the system validates the mappings against the production schema and returns clear success or error messages within 30 seconds
Developer Tests API Call
Given sample production-like data is seeded in the sandbox When the developer sends authenticated GET and POST requests to the payroll integration endpoints Then the system responds with HTTP 200 and returns correctly formatted JSON matching the expected schema
Developer Validates Data Isolation
Given two sandbox environments belonging to different developers When developer A attempts to query data in developer B’s sandbox Then the system denies access and returns HTTP 403 Forbidden
Developer Tears Down Sandbox Environment
Given the developer has completed testing When the developer initiates sandbox teardown Then the system deallocates all resources, removes data, and confirms successful destruction within 120 seconds
API Endpoint Configuration UI
"As a developer, I want an intuitive UI to configure custom API endpoints so that I can set up integrations quickly without manual coding."
Description

The integration sandbox shall include a user-friendly interface for configuring custom API endpoints, including inputting endpoint URLs, authentication credentials, headers, and parameters, to facilitate quick setup and reduce configuration errors.

Acceptance Criteria
API Endpoint URL Input Validation
Given a syntactically valid URL input in the endpoint URL field, when the user saves, then the system accepts and stores the URL without errors. Given an invalid URL input, when the user exits the field, then the UI displays a clear error message and disables the save action. The endpoint URL field enforces a maximum length of 2048 characters and rejects inputs longer than this limit.
Authentication Credential Encryption
Given the user enters API key and secret in the credential fields, when the credentials are saved, then they are encrypted at rest using AES-256 encryption. When testing the connection, the system decrypts the credentials in memory only and uses them securely without exposing plaintext values. The credential fields never display plaintext values after the initial save and show masked values instead.
Header Configuration Persistence
Given the user adds custom headers with valid key-value pairs, when the configuration is saved, then the headers persist across user sessions and page reloads. When retrieving an existing endpoint configuration, the user sees all previously saved headers populated correctly in the header configuration UI.
Parameter Mapping Preview
Given the user defines request parameters and their mappings, when the user clicks the ‘Preview Request’ button, then the UI displays a sample request payload or URL with the actual parameter values substituted. When the user modifies any parameter or mapping, the preview updates instantly without requiring a full save of the configuration.
Real-Time Error Feedback
Given the user enters or edits any configuration field (URL, credentials, headers, parameters), when a validation rule is violated, then the UI displays an inline error message adjacent to the field immediately. When the input is corrected to meet validation rules, the inline error message clears and the field is marked as valid.
Data Mapping Preview and Validation
"As an integration engineer, I want to preview and validate data mappings in real-time so that I can ensure correct data flow and prevent mapping errors in production."
Description

The feature must allow real-time preview of data mappings between Shiftly’s data models and external payroll system schemas, validating field alignments and data formats to ensure mappings are correct before deployment.

Acceptance Criteria
Employee ID Field Alignment Preview
Given a mapping between Shiftly employee IDs and the payroll system’s employee identifiers, when the user previews the mapping, then the UI displays a side-by-side view showing both fields with matching data types and sample values, and no alignment errors are reported.
Date Format Validation Scenario
Given Shiftly’s date fields and the payroll system’s date format requirements, when previewing the mapping, then the system validates date formats against the target schema and highlights any mismatches, displaying error messages for incorrect formats.
Data Type Mismatch Handling
Given a mapping where Shiftly fields have a different data type than the payroll schema, when previewing the mapping, then the system identifies and flags the mismatched fields, providing suggestions or warnings for corrective action.
Bulk Data Mapping Preview Performance
Given a large dataset of 1000 mapping entries, when the user initiates the preview, then the system returns the mapping results within 5 seconds and displays aggregated success and error counts.
Missing Field Validation Scenario
Given required fields defined by the payroll schema are not mapped, when running the preview, then the system marks these fields as missing and prevents proceeding to deployment until mappings are completed.
Error Logging and Notification
"As a developer, I want detailed error logs and notifications when API calls fail so that I can quickly identify and fix issues."
Description

The sandbox must log all API requests, responses, and errors, providing detailed error messages and notifications to developers when integration tests fail, enabling quick diagnosis and resolution of issues.

Acceptance Criteria
Logging of API Requests and Responses
Given an API request is sent to the sandbox When the sandbox processes the request Then the full request and response payloads including timestamps, HTTP method, endpoint, and status code are logged
Detailed Error Message Generation
Given an API call encounters an error When the sandbox logs the error Then the log entry includes an error code, descriptive message, stack trace or error context, and guidance for resolution
Developer Notification on Integration Test Failure
Given an integration test fails When the sandbox detects the failure Then an automated notification containing the request details, error message, and link to the related log entry is sent to the configured developer channel
Error Log Retrieval Interface
Given a developer accesses the sandbox UI or API When requesting logs for a specific transaction or time range Then the interface returns the relevant log entries in chronological order with filtering by error severity and endpoint
Error Severity Classification and Alerting
Given errors are classified by severity levels When a high-severity error is logged Then an immediate alert is generated and sent to the on-call developer or team channel
Role-based Access Control
"As an admin, I want to assign access levels to developers so that only authorized users can modify integration settings."
Description

Implement role-based access control for the sandbox environment, allowing administrators to assign permissions to developers for creating, modifying, and testing integration configurations, ensuring secure and controlled access.

Acceptance Criteria
Administrator Assigns Roles
Given an administrator is logged in with 'Admin' role, when they assign the 'Developer' role to a user, then the system updates the user's permissions to include creating, modifying, and testing configurations and displays a success notification.
Developer Creates Configuration
Given a user with 'Developer' role is logged in, when they create a new integration configuration in the sandbox environment, then they can select a third-party payroll template, map fields, and save the configuration without errors.
Developer Modifies Configuration
Given a user with 'Developer' role is logged in, when they modify an existing integration configuration and save changes, then the system updates the configuration and records the change with a timestamp and user ID in the version history.
Unauthorized User Access Denied
Given a non-Admin and non-Developer user is logged in, when they attempt to access the sandbox configuration page, then the system denies access and displays a 403 Forbidden error.
Role Permission Audit Logging
Given an administrator updates any user's role or permissions, when the change is confirmed, then the system generates an audit log entry capturing the admin ID, affected user ID, roles changed, timestamp, and reason for change.

Sentiment Stream

A live feed that captures and visualizes the emotional tone of incoming swap requests and feedback in real time. Managers can monitor shifts in team mood as they happen, enabling immediate awareness of emerging issues or positive trends.

Requirements

Real-time Sentiment Analysis
"As a manager, I want the system to analyze the sentiment of incoming swap requests and feedback in real time so that I can immediately understand team mood and address issues proactively."
Description

Continuously ingest incoming swap requests and feedback texts, process them through the AI-driven sentiment engine, and assign real-time sentiment scores (positive, neutral, negative) with minimal latency. Ensure seamless integration with the Shiftly platform so managers can instantly view the emotional tone of each entry as it arrives.

Acceptance Criteria
Immediate Sentiment Score Display
Given a manager is viewing the live swap request feed, When a new swap request or feedback text arrives, Then the sentiment score is processed and displayed in the feed within 2 seconds.
Accurate Sentiment Classification
Given a test set of 100 manually labeled swap requests and feedback texts, When processed by the AI sentiment engine, Then at least 95% of entries must match the manual sentiment labels.
Sentiment Feed UI Update
Given the manager’s dashboard is open, When a sentiment score is received, Then the corresponding entry appears in the feed with correct color coding (green for positive, yellow for neutral, red for negative).
System Resilience Under High Load
Given an incoming load of 200 messages per minute, When the system processes this load, Then 99% of messages are ingested, scored, and displayed within 3 seconds without errors.
Graceful Error Fallback
Given the sentiment engine fails to process an input, When this error occurs, Then the system logs the error, assigns a neutral sentiment score in the UI, and continues processing subsequent inputs without interruption.
Sentiment Trend Visualization
"As a manager, I want to visualize sentiment trends over time so that I can identify patterns and anticipate morale issues before they escalate."
Description

Provide an interactive dashboard that displays sentiment trends over selectable time intervals using line charts, heatmaps, and time-series sliders. Enable managers to compare mood fluctuations across shifts, days, and weeks to identify patterns and anticipate morale shifts.

Acceptance Criteria
Daily Mood Overview
Given the manager selects a 24-hour time interval When the dashboard loads Then a line chart displays sentiment scores plotted hourly with tooltips showing exact values for each data point
Shift Comparison Analysis
Given the manager chooses two shifts on the same day When the comparison view is activated Then the dashboard overlays two sentiment trend lines in distinct colors with an interactive legend for toggling visibility
Weekly Heatmap Display
Given the manager selects a weekly view When the heatmap visualization is rendered Then days appear on the y-axis and hours on the x-axis colored by sentiment intensity with an accompanying color scale legend
Time-Series Slider Adjustment
Given the manager adjusts the time-series slider When the slider range is modified Then all charts update dynamically within one second to reflect data within the new time window
Multi-Interval Comparison
Given the manager selects custom time intervals (e.g., last 3 days vs previous 3 days) When the comparison is applied Then separate trend lines appear for each interval and a summary table highlights average sentiment differences
Spike Alert Notifications
"As a manager, I want to receive alerts when negative sentiment spikes so that I can quickly address emerging problems and maintain team morale."
Description

Implement a notification system that triggers alerts when negative sentiment exceeds configurable threshold values. Deliver alerts via in-app banners, push notifications, or email to ensure managers are promptly informed of emerging issues requiring immediate attention.

Acceptance Criteria
Spike Detected in Negative Sentiment
Given real-time sentiment data streams in When negative sentiment exceeds the configured threshold Then the system flags a spike event for notification
In-App Alert Display
Given a spike event is flagged When the manager is active in the app Then an in-app banner appears within 5 seconds displaying the alert message and sentiment score
Push Notification Dispatch
Given a spike event is flagged and the manager has enabled push notifications When the threshold is exceeded Then a push notification is sent to the manager’s device within 10 seconds
Email Notification Delivery
Given a spike event is flagged and email alerts are enabled When the threshold is exceeded Then an email alert is sent to the manager’s inbox within 1 minute containing details of the sentiment spike
Threshold Configuration Persistence
Given the manager updates the negative sentiment threshold value When the new threshold is saved Then the updated threshold persists across sessions and is used for all subsequent spike detections
Sentiment-based Filtering
"As a manager, I want to filter the sentiment feed by category and time so that I can focus on the most relevant feedback and make informed decisions."
Description

Allow managers to filter the live sentiment feed by sentiment category (positive, neutral, negative), date range, shift, and keyword tags. Ensure filters can be combined to drill down into specific feedback types and quickly locate critical entries.

Acceptance Criteria
Filter by Sentiment Category
Given a manager is viewing the live sentiment feed When they select a sentiment category (positive, neutral, negative) Then the feed displays only entries matching the selected sentiment category
Filter by Date Range
Given a manager has selected a start and end date When they apply the date range filter Then only feedback entries within the specified dates are displayed
Filter by Shift
Given a manager chooses a specific shift from the shift dropdown When applied Then only feedback entries associated with that shift appear in the feed
Filter by Keyword Tags
Given a manager enters a keyword or selects a tag When applied Then only feedback entries containing the keyword or tag are shown
Combined Filters Application
Given a manager applies multiple filters (sentiment category, date range, shift, keywords) When all filters are active Then the feed displays only entries that meet all selected criteria
Historical Sentiment Analytics
"As a manager, I want to view historical sentiment analytics and generate reports so that I can review team morale over time and inform strategic staffing decisions."
Description

Store and archive sentiment data to support historical analysis and reporting. Provide downloadable reports summarizing average sentiment scores, top positive and negative feedback, and comparative analytics across custom periods. Integrate with existing export tools for seamless data sharing.

Acceptance Criteria
Sentiment Data Archival Verification
Given daily sentiment data is captured, when the end of day arrives, then the system stores the data in the historical database with timestamp and unique identifier and confirms storage success.
Historical Report Download
Given a manager requests a historical sentiment report for a defined period, when they select start and end dates and click download, then a CSV file is generated within 5 seconds containing average sentiment scores, top feedback entries, and relevant metadata.
Comparative Period Analytics
Given a manager compares two custom periods, when they apply the date ranges, then the system displays side-by-side charts showing differences in average sentiment, percentage change, and highlights periods with significant shifts.
Feedback Highlights Extraction
Given sentiment data includes feedback text tagged positive or negative, when generating the summary, then the report lists the top 5 positive and top 5 negative feedback entries by sentiment score for the selected period.
Data Export Tool Integration
Given the organization uses external BI tools, when exporting via the existing export tool, then the system provides an API endpoint or scheduled export file in JSON or CSV format and completes the export without errors.
Historical Data Integrity Check
Given historical data spans multiple months, when an integrity audit is run, then the system verifies no missing records, checks correct formatting, and logs any discrepancies for admin review.

Morale Heatmap

An interactive, color-coded calendar view that maps team sentiment across days, shifts, and locations. It highlights pockets of low or high morale so managers can quickly identify when and where to focus engagement efforts.

Requirements

Data Aggregation Engine
"As a store manager, I want the system to aggregate my team's mood inputs and attendance data into unified morale scores so that I can understand overall team sentiment at a glance."
Description

Develop a backend system that collects and processes sentiment feedback from employees via mobile app inputs and shift check-ins. This engine will integrate with existing workforce data to calculate daily and shift-level morale scores using AI-driven sentiment analysis. It ensures that all team sentiment data is normalized, timestamped, and stored efficiently to support real-time visualization and historical reporting.

Acceptance Criteria
Real-Time Feedback Ingestion
Employee sentiment submissions are received by the engine within 5 seconds of submission; each entry is normalized against a standard sentiment scale; each record includes a valid timestamp and user ID; no data loss occurs during peak load conditions
Shift Check-In Sentiment Processing
Sentiment inputs recorded at shift check-in are processed and stored in under 10 seconds; sentiment scores are accurately extracted using the AI model with at least 90% accuracy in test samples; processing errors are logged and retried automatically
Daily Morale Score Computation
System aggregates all sentiment records for each calendar day; AI-driven analysis computes a daily morale score for each location with results stored in the database; computed scores match expected values for predefined test datasets
Historical Data Retrieval Performance
API queries for morale history spanning up to 6 months return complete datasets within 300 ms; returned data includes accurate timestamps, sentiment scores, and location identifiers; pagination works correctly when retrieving large datasets
Data Integration Consistency
Sentiment data imported from the workforce management system matches corresponding shift and employee records without duplication; all integrated records pass data validation rules for IDs and timestamps; integration jobs run daily without errors
Interactive Calendar Visualization
"As a regional manager, I want to see a color-coded morale calendar so that I can quickly identify days or shifts with low team engagement."
Description

Implement a color-coded calendar UI component that displays morale scores per day, shift, and location. Users can hover or tap on individual calendar cells to view detailed sentiment breakdowns, including average mood, number of responses, and trend indicators. The visualization must be responsive across web and mobile interfaces, enabling managers to filter by date ranges, locations, and teams.

Acceptance Criteria
Viewing morale heatmap on web dashboard
Given a manager is logged into the web dashboard, when they navigate to the Morale Heatmap feature, then the calendar displays each day color-coded accurately based on the morale score stored in the system.
Filtering calendar by date range
Given the morale heatmap is visible, when the manager selects a start and end date and applies the filter, then the calendar updates to show only the cells within the selected date range, maintaining correct color-coding.
Filtering calendar by location and team
Given the morale heatmap is visible, when the manager applies filters for specific locations and teams, then the calendar displays only the relevant cells and hides data for unselected locations and teams.
Accessing morale heatmap on mobile
Given a manager is using a mobile device, when they access the Morale Heatmap feature, then the calendar layout adjusts responsively to fit the screen, preserving readability and interactive elements.
Viewing detailed sentiment breakdown
Given the manager hovers (web) or taps (mobile) on a calendar cell, when the event triggers, then a detail panel displays average mood, number of responses, and trend indicator for that specific date, shift, and location.
Real-time Sentiment Updates
"As a restaurant manager, I want the morale map to update instantly when my staff provide new feedback so that I can react promptly to any morale dips."
Description

Enable the morale heatmap to refresh sentiment data in real-time as employees submit feedback or swap shifts. This feature will use WebSocket or polling mechanisms to push updates to the calendar view without full page reloads, ensuring managers always see the latest morale status. It should gracefully handle network interruptions and queue updates as needed.

Acceptance Criteria
Manager views updated morale after feedback submission
Given the manager has the Morale Heatmap open, when an employee submits sentiment feedback, then the corresponding day-shift cell color updates within 2 seconds to reflect the new sentiment score without requiring a full page reload.
Sentiment refresh during active shift swap
Given a shift swap is processed and new feedback enters the system, when the swap completes, then the heatmap automatically refreshes the involved shifts' sentiment indicators within 3 seconds.
Handling network interruption during real-time update
Given the WebSocket connection is lost, when a sentiment update occurs, then the client queues the update locally and displays a reconnection indicator without blocking user interaction.
Queued updates processed after reconnection
Given one or more sentiment updates were queued during offline mode, when the connection is restored, then all queued updates are sent to the client and the heatmap updates reflect these changes within 5 seconds.
WebSocket fallback to polling on persistent failure
Given the WebSocket fails to reconnect after three attempts, when polling is triggered, then the system polls the server every 10 seconds for new sentiment data and updates the heatmap accordingly without user action.
Morale Threshold Alerts
"As a store manager, I want to receive alerts when team morale falls below a set threshold so that I can take immediate steps to improve engagement."
Description

Create a notification system that triggers alerts when morale scores cross predefined thresholds. Managers can configure alert levels (e.g., low, critical) and choose delivery channels such as in-app banners, email, or SMS. Alerts should include context, like shift time and location, and provide recommended actions to address low morale.

Acceptance Criteria
Low Morale In-App Alert Trigger
Given a shift’s morale score falls below the configured “low” threshold, when the system recalculates the heatmap, then an in-app banner is displayed to the manager within one minute.
Critical Morale SMS Notification
Given a shift’s morale score drops below the configured “critical” threshold and SMS notifications are enabled, when the threshold is crossed, then an SMS containing shift time, location, and alert level is sent to the manager’s phone within two minutes.
Threshold Configuration and Persistence
Given a manager has set “low” and “critical” thresholds and selected delivery channels, when they save their settings, then the thresholds and channels are persisted in user settings and displayed correctly on the next login.
Detailed Alert Content Verification
Given an alert is generated for a threshold breach, when the notification is constructed, then it includes shift date/time, location, current morale score, threshold crossed, and at least one recommended action.
Duplicate Alert Suppression
Given multiple morale score updates remain below the same threshold for the same shift within 30 minutes, when subsequent triggers occur, then only the first alert is sent and further alerts are suppressed until 30 minutes have elapsed.
Export and Reporting
"As a district manager, I want to export morale trend reports so that I can share insights with senior leadership."
Description

Provide functionality to export morale heatmap data and underlying sentiment scores into CSV or PDF reports. Reports should include historical trends, summary statistics, and annotations for key events. Users can schedule automated reports to be sent periodically to stakeholders or download them on-demand.

Acceptance Criteria
On-Demand CSV Export of Morale Heatmap Data
Given the user is on the Morale Heatmap report page When the user clicks “Export CSV” Then the system generates a CSV file containing date, shift, location, sentiment score, and metadata columns, and makes it available for download within 5 seconds
On-Demand PDF Export of Morale Heatmap Data
Given the user is on the Morale Heatmap report page When the user clicks “Export PDF” Then the system generates a PDF including the color-coded calendar heatmap with legend, historical trends graph, and summary statistics, and provides the file for download within 10 seconds
Scheduled Automated Report Delivery
Given the user configures a delivery schedule specifying frequency, format (CSV or PDF), date/time, and stakeholder email list When the user saves the schedule Then the system sends the report automatically at the configured times, emails the attachment with a descriptive subject line, and logs delivery status for each recipient
Inclusion of Historical Trends and Summary Statistics in Reports
Given a report is generated (on-demand or scheduled) When the report is compiled Then it includes line charts showing weekly average sentiment for the past 12 weeks and a summary table displaying min, max, and average sentiment by location, with values matching the source data
Annotations of Key Events in Exported Reports
Given key events (e.g., store openings, promotions, holidays) are tagged in the system When a report covering those dates is generated Then the report displays annotations on trend charts with event names and dates, includes an event legend, and ensures annotations align with the configured event timestamps

Tone Analyzer

An AI-driven tool that processes text from swap requests and feedback to classify emotions—such as frustration, enthusiasm, or fatigue. It tags each interaction with sentiment categories, helping managers understand underlying feelings at a glance.

Requirements

Sentiment Analysis Engine
"As a manager, I want the system to automatically classify shift swap messages by sentiment so that I can quickly understand staff emotions and address issues proactively."
Description

Implement the AI-driven sentiment analysis module that processes text inputs from swap requests and feedback, classifying each message into predefined emotional categories such as frustration, enthusiasm, or fatigue. The module should leverage the existing AI infrastructure, ensure high accuracy, and support both batch and real-time processing. The outcome should seamlessly integrate with the backend to tag each interaction with metadata indicating sentiment, enabling other system components to filter and display based on emotional context.

Acceptance Criteria
Live Swap Request Sentiment Tagging
Given a user submits a swap request message, when the sentiment engine processes it in real-time, then it should assign one of the predefined sentiment tags within 500ms and with at least 90% classification accuracy.
Batch Processing of Weekly Feedback
Given a batch of feedback messages up to 1000 entries, when processed in batch mode, then the engine should classify each message into the correct sentiment category and complete processing within 2 minutes with at least 92% accuracy.
Sentiment Metadata Integration to Backend
Given a classified sentiment output, when stored in the backend, then each interaction record must include a sentiment metadata field accessible via the API and viewable in the manager's dashboard.
Confidence Threshold Filtering
Given sentiment results with confidence scores, when the confidence score is below 70%, then the system should flag the interaction for manual review and not auto-tag the message.
Sentiment Accuracy Monitoring Dashboard
Given ongoing system operation, when generating the weekly accuracy report, then the dashboard must display overall classification accuracy, processing latency statistics, and the count of low-confidence records, updated within 5 minutes of report generation.
Real-Time Sentiment Visualization
"As a manager, I want to see sentiment tags on messages in real time so that I can immediately gauge staff morale and respond accordingly."
Description

Design and develop a user interface component that displays sentiment tags alongside each swap request and feedback message in real time. The visualization should use color-coded icons or labels to represent different emotions, provide tooltip descriptions for clarity, and allow managers to quickly scan interactions. It should integrate with the existing mobile and web interfaces, ensuring performance and responsiveness across devices.

Acceptance Criteria
Real-Time Tag Rendering on New Swap Requests
Given a new swap request is received, when the UI component updates, then the sentiment tag appears adjacent to the request within 2 seconds.
Tooltip Display on Hover Interaction
Given a sentiment icon is displayed, when the manager hovers over the icon, then a tooltip with the sentiment name and a brief description appears within 500ms and disappears upon hover exit.
Color-Coded Icon Contrast Compliance
Sentiment icons for all categories use colors that meet a minimum contrast ratio of 4.5:1 against their background as per WCAG 2.1 AA standards.
Responsive Performance on Mobile Devices
On supported Android and iOS devices, sentiment tags load and render within 1 second of data receipt without dropping UI frame rate below 50fps during scrolling.
Consistent Integration Across Interfaces
Sentiment tags and tooltips display with identical iconography, colors, and text on both web and mobile interfaces with no visual or functional discrepancies.
Custom Sentiment Categories
"As a manager, I want to customize the sentiment categories so that the tone analyzer reflects our team’s unique communication style."
Description

Provide functionality within the admin settings to customize, add, or modify sentiment categories and their associated keywords or machine learning parameters. This feature will allow managers to tailor emotional classifications to their team’s specific terminology and culture, improving the accuracy and relevance of the sentiment tags.

Acceptance Criteria
Admin adds a new custom sentiment category
Given the admin is on the Custom Sentiment Settings page When the admin clicks “Add Category,” enters a unique category name and associated keywords Then the system saves the new category and displays it in the category list
Admin modifies an existing sentiment category’s keywords
Given the admin selects an existing category in the settings When the admin updates its keyword list and clicks “Save Changes” Then the system updates the category keywords and confirms the update with a success message
Admin deletes a custom sentiment category
Given the admin views the list of custom categories When the admin clicks “Delete” next to a category and confirms the action Then the system removes the category permanently and no longer uses it in sentiment analysis
Sentiment engine applies custom categories during analysis
Given a swap request text contains keywords matching a custom category When the sentiment analysis runs Then the system tags the request with the custom category instead of a default category
Admin previews the impact of custom sentiment categories
Given the admin clicks “Preview Impact” on the settings page When the system processes a sample batch of texts Then the system displays before-and-after sentiment tags so the admin can review accuracy
Sentiment Summary Dashboard
"As a manager, I want to view overall sentiment trends in a dashboard so that I can identify patterns in staff morale and make data-driven staffing decisions."
Description

Implement a dashboard view that aggregates sentiment data over selectable time periods, displaying trends via charts and graphs. The dashboard should highlight the proportion of each sentiment category, track changes over time, and allow filtering by team, shift, or location. Export options for CSV and PDF should be provided for reporting and stakeholder review.

Acceptance Criteria
Overall Sentiment Trend Over Time
Given a manager selects a valid time range when the dashboard loads then a line chart displays the percentage of each sentiment category for each day within the range; Data points align with the AI engine’s underlying sentiment data; Chart includes axes labels, legend, and interactive tooltips showing exact values on hover.
Filter Sentiment by Team and Location
Given the manager chooses one or more teams or locations when applying filters then the dashboard updates to show charts and graphs reflecting only the selected teams or locations; Filters support multi-select and clear-all functionality; Filtered data matches the sentiment records for the chosen parameters.
Sentiment Category Proportion Pie Chart
Given a selected time period when viewing the dashboard then a pie chart displays the proportion of each sentiment category for that period; Each slice is labeled with category name and percentage value; Chart updates dynamically when filters or time range change.
Export Dashboard Data as CSV
Given the manager clicks the CSV export button when data is displayed then a CSV file is downloaded containing all sentiment metrics, including date, sentiment category counts, and percentages; CSV columns are correctly labeled; File opens without errors in standard spreadsheet applications.
Export Dashboard as PDF Report
Given the manager clicks the PDF export button when data is displayed then a well-formatted PDF report is generated showing charts, tables, and filter criteria; Report includes title, date range, and legend; PDF is downloadable and printable.
Responsive Dashboard Display on Mobile Devices
Given the manager accesses the dashboard on a mobile device when rotating between portrait and landscape then all charts and controls resize appropriately; Interactive elements remain functional and legible; No horizontal scrolling is required to view key information.
Negative Sentiment Alerting
"As a manager, I want to receive alerts when staff messages indicate high frustration so that I can address concerns promptly."
Description

Develop an alerting mechanism that triggers notifications when messages with high negative sentiment scores (e.g., frustration or fatigue) exceed a defined threshold. Alerts should be configurable and sent via email, SMS, or in-app notifications to designated managers, enabling proactive intervention before issues escalate.

Acceptance Criteria
Threshold Breach Detection
Given a continuous stream of swap request messages, when the average negative sentiment score of messages in the last hour exceeds 0.7, then an alert must be generated and queued for delivery within 30 seconds.
Configurable Alert Channel Setup
Given a manager’s profile, when the manager configures email, SMS, and in-app notification preferences, then alerts must be routed only to the selected channels and confirmed via a test notification.
Real-Time Notification Delivery
Given an alert is triggered, when the notification is sent via the chosen channel, then the manager must receive the notification within 60 seconds and the system must log the delivery timestamp and status.
Alert Content Accuracy
Given a high negative sentiment message triggers an alert, when the alert content is generated, then it must include the sender’s name, sentiment score, key phrases flagged (e.g., frustration), and a direct link to the full message.
Manager Response Logging
Given a manager acknowledges or dismisses an alert, when the action is taken, then the system must record the manager’s response along with a timestamp and make it available in the alert history report.

Pulse Alerts

Automated notifications that trigger when team sentiment crosses defined thresholds or exhibits sudden shifts. Managers receive timely alerts via email or in-app messages, empowering them to intervene before morale issues escalate.

Requirements

Real-time Sentiment Analysis
"As a manager, I want to see live sentiment scores for my team so that I can identify morale dips as they happen."
Description

Integrate an AI-driven sentiment analysis engine that processes team feedback, chat messages, and shift swap comments in real time to detect changes in morale. This module identifies both gradual sentiment trends and sudden shifts, normalizes diverse data inputs, and provides a continuous score for each team. It seamlessly integrates with Shiftly’s backend and ensures minimal latency to enable timely alert generation.

Acceptance Criteria
Continuous Feedback Stream Ingestion
Given the system is connected to all configured team feedback sources, When a new feedback message, chat log entry, or shift swap comment arrives, Then the sentiment analysis engine processes and scores the input within 5 seconds and persists the sentiment score in the database.
Sudden Sentiment Shift Detection
Given the system maintains a rolling 24-hour baseline sentiment score, When incoming sentiment scores deviate by more than the defined threshold within a 10-minute window, Then the system flags a sudden sentiment shift event and records the timestamp and magnitude of the change.
Sentiment Score Normalization
Given sentiment scores from diverse input types (team feedback, chat messages, shift-swap comments), When raw sentiment values are computed, Then the engine normalizes all scores to a unified 0–100 scale with no more than a 5% variance across source types.
Backend Integration and Data Flow
Given the Shiftly backend API endpoints are live, When a new sentiment score or shift event is generated, Then the system sends the data payload to the designated endpoint with valid authentication credentials and receives an HTTP 200 response.
Alert Generation Latency
Given a detected sentiment shift that meets alert conditions, When the shift event is logged, Then the manager receives a notification via email or in-app message within 60 seconds of the event being recorded.
Custom Threshold Configuration
"As a manager, I want to set custom sentiment thresholds so that alerts are tailored to my team’s normal morale fluctuations."
Description

Provide a user interface allowing managers to define and adjust sentiment thresholds and sensitivity levels for alerts. Supports setting different thresholds for gradual declines versus abrupt dips, choosing specific teams or roles to monitor, and scheduling active monitoring windows. Ensures flexibility to adapt monitoring criteria to unique workplace cultures and operational needs.

Acceptance Criteria
Define Gradual vs Abrupt Thresholds
Given the manager is on the Custom Threshold Configuration screen When the manager enters valid numeric values for gradual decline and abrupt dip thresholds and clicks Save Then the system persists the thresholds, displays a success message, and the new thresholds are applied to monitoring
Select Teams or Roles for Monitoring
Given the manager has access to the team and role selection menu When the manager selects one or more teams or roles and clicks Save Then only data from the selected teams or roles are monitored and alerts are triggered accordingly
Schedule Monitoring Windows
Given the manager is configuring active monitoring schedules When the manager selects specific days of the week and time ranges and clicks Save Then alerts are only generated during the defined monitoring windows
Adjust Sensitivity and Preview Impact
Given the sensitivity slider is available on the configuration screen When the manager drags the slider to a new value Then the interface updates with a preview of expected alert volume and threshold before saving
Validate Threshold Input
Given the manager inputs threshold values outside the allowed range or with invalid formats When the manager attempts to save Then the system displays descriptive validation errors and prevents saving until corrected
Multi-channel Notification Delivery
"As a manager, I want to receive morale alerts via email and mobile so that I can act even when I’m away from the scheduling app."
Description

Implement a notification system that delivers Pulse Alerts through email, in-app banners, and push notifications. Notifications should be configurable by channel, include actionable summary and sentiment context, and link directly to the sentiment dashboard. The system must ensure high deliverability, support retry logic, and record acknowledgement timestamps.

Acceptance Criteria
Channel Configuration for Pulse Alerts
Given a manager configures notification channels When the manager selects email and in-app banners Then subsequent Pulse Alerts are delivered via the chosen channels
Content Includes Summary and Sentiment Context
Given a Pulse Alert is generated When the notification is composed Then it includes an actionable summary, sentiment context, and a link to the sentiment dashboard
High Deliverability with Retry Logic
Given a notification send attempt fails due to a transient error When the system encounters a failure Then it retries delivery up to three times with exponential backoff
Push Notification Delivery
Given a registered mobile device When an alert is triggered Then a push notification is received within 10 seconds including the summary and sentiment context
Acknowledgement Timestamp Recording
Given a manager acknowledges a Pulse Alert When the manager clicks the acknowledge action Then the system records the acknowledgement timestamp and updates the alert status
Alert Prioritization and Escalation
"As a regional manager, I want the worst morale issues to escalate automatically so that nothing critical falls through the cracks."
Description

Develop logic to prioritize alerts based on severity of sentiment change, team criticality, and time since last alert. Automatically escalate high-severity alerts after a defined period if they remain unacknowledged, notifying higher-level managers or HR. Include option for manual escalation and custom escalation chains.

Acceptance Criteria
Detect and Prioritize Severe Negative Sentiment
Given a team’s sentiment score drops by more than 20% in a 24-hour period, When the AI engine analyzes the latest feedback, Then the alert is tagged as High priority and appears at the top of the manager’s alert dashboard.
Escalation After Unacknowledged Alert Time Elapse
Given a High-priority alert is generated, When no manager action is recorded within 30 minutes, Then the system automatically escalates the alert to the next-level manager and logs the escalation time.
Manual Escalation by Manager
Given any alert visible on the dashboard, When the manager clicks ‘Escalate’ and selects an escalation path, Then the system sends notifications to the selected recipients and records the manual escalation in the audit log.
Custom Escalation Chain Execution
Given an organization has configured a three-step escalation chain, When an unacknowledged High-priority alert reaches each escalation threshold, Then notifications are sent sequentially to each role defined in the chain until acknowledgment.
Alert Handling for High-Criticality Teams
Given a sentiment drop in a team marked as critical, When an alert is generated, Then the system labels it as Critical priority and includes HR in both initial and escalation notifications.
Historical Sentiment Reporting
"As an operations lead, I want historical sentiment reports so that I can correlate team morale with scheduling practices and improve staffing strategies."
Description

Generate daily, weekly, and monthly reports showing sentiment trends over time, key drivers of mood changes, and correlations with scheduling events (e.g., overtime spikes). Reports should be exportable in PDF and CSV formats, include visualizations like line graphs and heat maps, and integrate with Shiftly’s analytics dashboard.

Acceptance Criteria
Daily Sentiment Trend Report Generation
- Given a manager selects "Daily Sentiment Report" on the analytics dashboard, when the system processes the past 24 hours of sentiment data, then it displays a line graph with hourly sentiment scores. - The report page loads within 5 seconds under normal network conditions. - The manager can export the report in PDF and CSV formats, and each export contains the correct timestamps and sentiment values.
Weekly Sentiment Correlation Analysis
- Given a manager requests the weekly sentiment report, when the system aggregates the past 7 days of data and overtime events, then it presents a scatter plot and correlation coefficient between overtime hours and sentiment scores. - The report includes a tabular data section with daily averages and correlation metrics. - The manager can successfully export this data in both PDF and CSV formats.
Monthly Key Driver Identification
- Given a manager generates a monthly sentiment report, when the system analyzes the past 30 days of sentiment inputs, then it identifies and lists the top three drivers affecting sentiment. - A heat map by day and shift highlights mood change hotspots. - The report provides a written summary of findings and is exportable in PDF and CSV.
PDF Export Compliance
- Given any sentiment report is viewed, when the manager clicks "Export to PDF," then the downloaded PDF matches the on-screen layout, including graphs, tables, and headers. - The PDF file is under 5 MB and opens without errors in standard PDF viewers. - The PDF filename follows the pattern "Shiftly_SentimentReport_<type>_<date>.pdf".
CSV Data Accuracy
- Given any sentiment report is viewed, when the manager clicks "Export to CSV," then the CSV file includes all required columns (date, time, sentiment score, event tags). - Each row in the CSV matches the corresponding data point in the system database with no missing or malformed values. - The CSV imports cleanly into spreadsheet applications without schema errors.
Manager Feedback Loop
"As a manager, I want to mark alerts as valid or false so that the system learns and improves over time."
Description

Allow managers to provide feedback on alerts (e.g., false positives, resolved issues) directly within the alert panel. Feedback adjusts the AI model’s sensitivity over time and trains it to reduce noise. Include a simple interface for tagging alerts as accurate or inaccurate and a summary of feedback-driven model updates.

Acceptance Criteria
Feedback Interface Visibility
Given a manager views the alert panel When an alert is displayed Then a feedback widget with options 'Accurate' and 'Inaccurate' and a submit button is visible
Accurate Alert Feedback Submission
Given a manager marks an alert as 'Accurate' and clicks submit When the submit button is clicked Then the system records the feedback, displays a confirmation message, and increments the accurate feedback count for that alert
Inaccurate Alert Feedback Submission
Given a manager tags an alert as 'Inaccurate' and enters an optional comment When the manager submits the feedback Then the system stores the feedback with comment, prompts for additional context if provided, and updates the inaccurate feedback metrics
Feedback-Driven Model Update Summary Display
Given feedback has been collected over a defined period When a manager accesses the model update summary Then the system displays a summary of sensitivity adjustments made to the AI model including dates, change types, and impact metrics
Historical Feedback Retrieval
Given a past alert with existing feedback When a manager selects the alert in the history view Then the system displays all feedback entries with timestamps and statuses and allows the manager to reclassify the feedback

Intervention Insights

Generates tailored, data-driven recommendations for managerial action based on detected sentiment trends. Whether suggesting one-on-one check-ins, team-building activities, or schedule adjustments, this feature guides proactive interventions to boost overall engagement.

Requirements

Real-time Sentiment Analysis
"As a restaurant manager, I want to see real-time sentiment trends from my team so that I can quickly identify and address morale issues before they escalate."
Description

Continuously collect and process staff feedback from multiple sources (e.g., in-app surveys, shift check-ins, feedback forms) to calculate real-time sentiment scores. Display aggregated results on a live sentiment feed, enabling managers to monitor team morale and detect negative trends as they emerge. This integration leverages AI-driven natural language processing to ensure accurate sentiment classification across varying feedback formats.

Acceptance Criteria
Real-time Sentiment Feed Updates
Given a manager is viewing the live sentiment feed, when new staff feedback is submitted, then the feed updates the aggregated sentiment score within 5 seconds and visually highlights the change.
Inclusion of New Feedback in Sentiment Score
Given a staff member submits feedback through any supported input channel, when the NLP engine processes the feedback, then the overall sentiment score recalculates to include the new input with at least 95% accuracy.
Accuracy of Sentiment Classification
Given a test dataset of feedback with pre-labeled sentiment, when the NLP engine classifies the feedback, then at least 90% of items match the known labels.
Visualization of Historical Sentiment Trends
Given a 7-day history of sentiment scores, when the manager views the sentiment trend chart, then the system displays correctly plotted daily averages and allows filtering by custom date ranges.
Alert for Significant Negative Sentiment Spike
Given the aggregated sentiment score drops by more than 15% compared to the previous day, when the threshold is breached, then the system sends an immediate in-app alert and email notification to the manager.
Historical Sentiment Trend Analysis
"As a retail district manager, I want to review historical sentiment trends across stores so that I can identify persistent engagement challenges and measure the effectiveness of past interventions."
Description

Aggregate sentiment data over customizable time intervals (daily, weekly, monthly) and visualize trends through interactive charts. Enable filtering by team, location, or shift type to uncover patterns and recurring issues. Provide comparative metrics to evaluate the impact of past interventions, facilitating data-driven decision-making and long-term engagement strategies.

Acceptance Criteria
Daily Sentiment Overview Visualization
Given a user selects the daily time interval, when the system renders the sentiment trend chart, then the chart displays daily average sentiment scores for each day with interactive tooltips showing date, score, and response count.
Weekly Team Sentiment Filter
Given a user filters by a specific team and selects the weekly interval, when the chart updates, then it shows weekly aggregated sentiment scores for that team and allows comparison across consecutive weeks.
Monthly Comparative Intervention Impact
Given a user selects two past interventions and the monthly interval, when the comparative chart is generated, then it overlays sentiment trends before and after each intervention, highlighting percentage change in average sentiment.
Location-Based Sentiment Drilldown
Given a user filters by location and any time interval, when the interactive chart is viewed, then it displays sentiment trends for the selected location and enables switching between locations via a dropdown menu.
Shift Type Trend Comparison
Given a user selects multiple shift types simultaneously, when the system renders the sentiment trends, then it overlays each shift type’s trend line with distinct colors and includes a legend for clear differentiation.
Personalized Intervention Recommendations
"As a shift supervisor, I want personalized recommendations for interventions so that I can proactively address emerging team issues with data-backed actions."
Description

Use AI-driven analysis of sentiment trends, shift attendance, and performance metrics to generate tailored recommendations for managerial action—such as one-on-one check-ins, team-building activities, schedule adjustments, or targeted incentives. Provide rationale for each suggestion, including supporting data points and expected outcomes, to guide proactive engagement efforts.

Acceptance Criteria
Decreasing Team Sentiment Alert
Given the weekly sentiment score has declined by more than 10% over the past two weeks, when the manager accesses the Intervention Insights feature, then the system shall display a one-on-one check-in recommendation including the percentage change in sentiment and a list of affected team members.
Elevated No-Show Rate Notification
Given the staff no-show rate exceeds 15% in the past month, when the manager views the Intervention Insights dashboard, then the system shall present a schedule adjustment recommendation that includes current no-show statistics, projected impact on coverage, and suggested shift reassignments.
Consistent Underperformance Identification
Given a staff member’s average performance rating falls below 3 for three consecutive shifts, when the manager reviews the Intervention Insights report, then the system shall generate a targeted incentive recommendation that outlines performance data, proposed incentive type, and expected improvement metrics.
Team Morale Fluctuation Advisory
Given sentiment scores fluctuate between 40% and 60% with a variance above 15% in the past week, when the manager checks Intervention Insights, then the system shall recommend a team-building activity including sentiment variance details and projected engagement uplift.
Seasonal Turnover Risk Prevention
Given the projected turnover rate for the upcoming season surpasses 20% based on historical trends, when the manager opens Intervention Insights, then the system shall propose a targeted incentive or schedule flexibility plan that includes historical turnover data, comparison to past seasons, and expected retention improvement.
Intervention Management Dashboard
"As a store manager, I want a dashboard to manage and track recommended interventions so that I can easily implement actions and monitor their effectiveness over time."
Description

Offer a centralized interface where managers can view, prioritize, and schedule recommended interventions. Display intervention details, such as suggested date, involved team members, and expected impact. Allow managers to mark actions as completed and record follow-up notes, creating an audit trail of engagement activities and outcomes.

Acceptance Criteria
Intervention List Display
Given a manager accesses the Intervention Management Dashboard with recommended interventions available, when the dashboard loads, then a list of all interventions is displayed with suggested date, involved team members, and expected impact for each.
Scheduling an Intervention
Given a manager reviews an intervention entry, when they click the 'Schedule Intervention' button and select a date, then the chosen date is saved and displayed in the intervention details.
Prioritizing Interventions
Given multiple recommended interventions on the dashboard, when the manager applies the 'Sort by Impact' filter, then interventions are reordered from highest to lowest expected impact.
Filtering Interventions by Status
Given the Intervention Management Dashboard, when the manager selects a status filter (e.g., Pending or Completed), then only interventions matching the selected status are displayed.
Marking Interventions Complete
Given an intervention entry with a 'Mark as Completed' action, when the manager clicks this action and enters follow-up notes, then the intervention status updates to Completed and the notes are saved and visible in the audit trail.
Audit Trail Accessibility
Given a completed intervention on the dashboard, when the manager views the intervention details, then all follow-up notes and action timestamps are displayed in chronological order.
Automated Alert and Notification System
"As a district manager, I want to receive automated alerts when team sentiment drops significantly so that I can act promptly to prevent disengagement."
Description

Implement configurable alerts that notify managers via mobile push, email, or in-app messages when sentiment scores fall below defined thresholds or when critical shifts show rising negative trends. Include links to relevant insights and recommended interventions, ensuring immediate awareness and facilitating swift action to mitigate emerging issues.

Acceptance Criteria
Low Sentiment Alert Trigger
Given the aggregated sentiment score for a shift falls below the manager-defined threshold, when the system processes the latest data, then a notification is sent to the manager via the selected channel within 60 seconds.
Critical Shift Rising Negative Trend Notification
Given a critical shift’s negative sentiment increases by more than 10% in a 24-hour window, when the trend threshold is breached, then the system delivers an in-app alert with a summary and link to detailed Intervention Insights.
Configurable Alert Channel Delivery
Given the manager has enabled push, email, and in-app notifications, when an alert is triggered, then the system sends the alert to all enabled channels and records delivery status in the notification dashboard.
Insight and Intervention Link Accessibility
Given a notification is received, when the manager taps the embedded link, then the app navigates to the correct Intervention Insights page showing tailored recommendations within 5 seconds.
Notification Failure Fallback Mechanism
Given a push notification fails after three retry attempts, when the system detects the failure, then an email fallback alert is sent immediately and a delivery error is logged in the system.

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Press Coverage

Imagined press coverage for this groundbreaking product concept.

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Shiftly Unveils Voice-Activated Scheduling Revolutionizing Manager Workflows

Imagined Press Article

Shiftly today launches its most intuitive update yet, introducing a full suite of voice-activated scheduling tools designed to empower restaurant and retail managers to create, adjust, and confirm shift rosters entirely by voice in seconds. The new Schedule by Voice feature leverages advanced natural language processing to interpret spoken staffing requirements, automatically generating a balanced, compliant schedule that factors in availability, certifications, labor laws, and coverage rules. Whether managers are in the kitchen, on the sales floor, or on the go, they can speak their needs out loud—“Create a weekend schedule for 10 servers and 5 cooks”—and receive a fully built roster in under 10 seconds. Beyond initial roster creation, Shiftly’s QuickEdit Voice commands enable hands-free tweaks on individual shifts with simple phrases like “Move Jessica’s shift to 6 PM Tuesday” or “Swap Raj and Maria on Friday.” If a request is ambiguous, SmartPrompt Assistant poses clarifying follow-up questions—such as “Do you want Raj’s 4 PM shift moved to Maria, or would you prefer to swap their assignments?”—ensuring accuracy before finalizing changes. Once a voice command completes, VoiceConfirm Feedback offers immediate audio confirmation through text-to-speech or Slack audio snippets—“Shift updated for Lisa, Tuesday at 3 PM”—so managers know their instructions executed correctly without glancing at a screen. Integration is seamless across platforms. For teams using Slack as their primary communication hub, the new SlackVoice Bridge allows voice scheduling commands to be issued directly within channels and direct messages. Managers can initiate roster overhauls or shift swaps in the same thread where they coordinate daily tasks, eliminating app-switching and consolidating operational workflows. Every voice interaction is automatically recorded in Shiftly’s VoiceAudit Log, creating a searchable, time-stamped record of commands, transcriptions, and user identities. This audit trail supports compliance, transparency, and accountability—critical for franchised operations and multi-location HR coordinators. Beta users have praised the hands-free capabilities and speed gains. Bistro Boss user Lauren Kim reports, “Before Shiftly Voice, I spent hours manually typing in schedules or clicking through menus on my phone. Now I can say what I need while prepping dinner service and get a perfect roster without touching my device. It’s cut my admin time by more than half.” Retail Roster Lead Arturo Gomez adds, “The integration with Slack means I don’t have to juggle Slack notifications and the scheduling app separately. A quick voice command in our operations channel keeps everyone aligned instantly.” “Voice scheduling represents the next frontier in workforce management,” said Priya Patel, Shiftly CEO. “By combining cutting-edge AI with natural language understanding, we’re removing the friction of clicking, typing, and toggling between screens. Managers can focus on what really matters—leading their teams—while Shiftly handles the complexity of compliance, coverage, and shift balance.” The voice tools also include customizable security settings to ensure only authorized users can issue commands. Multi-factor authentication and role-based permissions guard against unintended alterations, while automated notifications notify administrators whenever a major roster change occurs via voice. The system also supports multilingual commands, catering to diverse teams across North America, Europe, and Asia Pacific. Shiftly’s voice scheduling suite is available immediately to all customers at no additional charge. To learn more or schedule a demonstration, contact Shiftly Media Relations: Rebecca Lane, rebecca.lane@shiftly.ai, +1-415-555-0123. Visit www.shiftly.ai/voice for feature details and video tutorials.

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Shiftly Launches Integrated Payroll Sync to Eliminate Timesheet Headaches for SMBs

Imagined Press Article

Shiftly today announced the release of its comprehensive payroll synchronization suite, enabling small and midsize restaurants and retail outlets to connect their live shift schedules directly with payroll systems in real time. The new PaySync Bridge eliminates manual timesheet reconciliation and speeds up payroll processing by up to 40%, freeing managers and finance teams from tedious cross-checks and reducing costly errors. By mapping scheduled hours, overtime codes, and shift differentials automatically, Shiftly ensures that every penny earned is accurately captured at the end of each pay period. At the core of the suite is the AutoTimesheet Validator, which continuously cross-checks synced schedule hours against employee punch-in/out data. Discrepancies are flagged and presented in the Exception Resolution Center, a unified dashboard where payroll administrators can review, annotate, and resolve anomalies with a single click. The MultiPaycode Mapper allows granular mapping of hours to various pay codes—including holiday premiums, night-shift differentials, and meal break deductions—eliminating manual configuration errors and ensuring compliance with local labor laws. To help managers forecast labor costs before finalizing schedules, Shiftly’s PayForecast Dashboard provides a live preview of upcoming payroll liabilities. The dashboard factors in projected hours, anticipated overtime, accrued benefits, and bonus programs. Real-time Overtime Alerts notify managers when team members approach or exceed legal thresholds, prompting schedule adjustments to avoid unexpected payouts. With proactive notifications and visual cost insights, SMB operators can make informed staffing decisions to stay within budget targets. “By linking scheduling and payroll in a seamless loop, we’ve closed the gap that used to force managers to export spreadsheets, import into payroll software, and manually reconcile hours,” said Michael Tran, Shiftly Product Director. “Our integration API sandbox offers a secure environment for testing custom connections with leading payroll platforms so clients can validate workflows before going live. Once configured, changes flow automatically—shift updates today appear in payroll tomorrow.” Franchise Flow Frank, who oversees staffing and payroll across five coffee shop locations, shared his experience: “Prior to Shiftly, I spent two full days each week reconciling schedules and timesheets for payroll. Now, the process runs itself—exceptions pop up only when something truly unexpected happens. We’ve cut our payroll admin workload by 60%, and I have peace of mind knowing the numbers match exactly.” Shiftly’s integration suite supports connectivity with major payroll providers, including ADP, Gusto, QuickBooks Workforce, Paychex, and UK-based Sage. For custom or regional systems, the Integration API Sandbox provides thorough testing and real-time validation, ensuring a smooth, error-free deployment. Once connected, clients can choose bi-directional sync to allow payroll updates—such as leave balances or deductions—to feed back into the scheduling engine for future roster optimization. The rollout of the PaySync Bridge is immediate and included in all enterprise and professional plans. SMB operators interested in a guided integration demo or proof of concept can contact Shiftly’s Partnerships team: Daniel Wu, partnerships@shiftly.ai, +1-646-555-0198. More information is available at www.shiftly.ai/payroll-integration.

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Shiftly Expands AI-Driven No-Show Prevention with Advanced RiskRadar and Backup Roster Tools

Imagined Press Article

Shiftly today introduced a powerful upgrade to its AI-driven attendance management capabilities, launching an enhanced RiskRadar Notification system alongside a new BackupRoster Generator and QuickFill Broadcast feature. Together, these tools proactively identify and mitigate no-show risks up to two hours before each shift, empower instant coverage solutions, and deliver real-time insights to managers seeking uninterrupted operations in busy restaurant and retail environments. The advanced RiskRadar Notification leverages machine learning models trained on historical attendance patterns, weather data, local events, and individual employee reliability metrics. When the system detects a high probability of a no-show—based on factors like repeated tardiness, sudden availability changes, or external risk signals—managers receive immediate alerts via email, SMS, and in-app push notifications. This early warning gives managers critical lead time to activate backup coverage protocols. To streamline replacement staffing, the new BackupRoster Generator automatically compiles a prioritized shortlist of available backup staff based on real-time availability, past reliability scores, and required certifications. Managers can review the suggested roster in a single click and deploy replacements instantly. Should none of the backups accept, the QuickFill Broadcast sends a one-click open shift notification to a pre-qualified list of on-call or part-time employees. Eligible staff can claim open shifts directly from the alert, reducing fill time from hours to minutes and minimizing operational disruptions. The Predictive Heatmap provides a visual overview of no-show risk across daily and weekly schedules. Color-coded shading highlights shifts with elevated risk probabilities at a glance, enabling managers to proactively redistribute coverage or pre-schedule additional backups. Complementing this, the SmartSwap Suggestion uses AI-driven recommendations to propose optimal swap partners—matching availability, skill sets, and fairness criteria—to resolve at-risk assignments without manual oversight. “Shiftly’s new attendance toolkit is a game-changer for small business operators facing the unpredictability of last-minute staff drop-outs,” said Elena Martinez, Shiftly Head of Data Science. “By anticipating issues, automating backup selections, and enabling staff to claim open shifts with one tap, we’re turning reactive firefighting into a predictable, data-driven workflow.” Retail Roster Lead Marcus Johnson shared his experience in the pilot program: “We saw a 40% reduction in last-minute no-shows within two weeks of activating RiskRadar and Backup Roster. Instead of scrambling to find coverage, I get a warning, glance at the heatmap, press one button, and move on. It’s transformed how we manage our floor during peak hours.” Attendance Insights, Shiftly’s aggregated analytics dashboard, now includes a dedicated view for no-show trends, showing patterns by employee, shift type, and time of day. Managers can export actionable reports to address chronic attendance issues and refine scheduling strategies. Combined with existing forecasting and certification tools, these enhancements deliver end-to-end reliability and compliance. The upgraded attendance features are available immediately to all Shiftly customers at no extra charge. For a personalized demonstration or to learn how AI-driven no-show prevention can boost team performance, contact Shiftly Press Relations: Sophia Chen, press@shiftly.ai, +1-323-555-0187. Visit www.shiftly.ai/no-show-prevention for detailed feature documentation and success stories.

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