Customer Support Software

PulseDesk

Support Unleashed. Customers Thrilled. Instantly.

PulseDesk empowers non-technical SaaS support leads to resolve customer tickets 50% faster by unifying live chat, ticketing, and no-code workflow automation. Its intuitive builder eliminates manual tasks and technical hurdles, enabling agile teams to instantly create and adapt support flows—even as ticket volume surges—while boosting collaboration and customer satisfaction.

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Product Details

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

Vision & Mission

Vision: Empower every SaaS support team to deliver instant, effortless customer care through intuitive, no-code automation and seamless collaboration.
Long Term Goal: By 2028, empower 10,000 SaaS support teams worldwide to resolve tickets 50% faster and boost customer satisfaction by 30%, without requiring any technical expertise.
Impact: Enables SaaS support leads to resolve customer tickets 50% faster and reduce onboarding time by 75%, resulting in a 30% increase in customer satisfaction while eliminating manual workflows—empowering non-technical teams to scale support without IT reliance as ticket volumes surge.

Problem & Solution

Problem Statement: Non-technical SaaS support leads struggle to scale customer support as ticket volumes surge, facing slow response times and manual workflows because existing tools demand complex integrations and technical setup they cannot manage on fast-moving teams.
Solution Overview: PulseDesk unifies live chat, ticketing, and workflow automation in a single intuitive web app, letting support leads create and adapt custom support flows instantly with a no-code visual builder—eliminating technical setup delays and repetitive manual work as ticket volumes surge.

Details & Audience

Description: PulseDesk unifies live chat, ticketing, and workflow automation to help SaaS startup support teams resolve customer issues 50% faster. Designed for fast-moving, customer-focused teams, it eliminates repetitive manual tasks and boosts collaboration. Its fully customizable, no-code automation builder empowers non-technical managers to create and adapt support flows instantly—no IT required.
Target Audience: Non-technical SaaS support leads (22-40) seeking rapid ticket resolution and agile, collaborative workflows.
Inspiration: Late one night, I watched our support lead comb through 40 identical tickets, copying replies between tabs while customers waited. She sighed—no way to automate even routine answers without engineering help. Her frustration and lost time fueled PulseDesk: a no-code solution built so any fast-moving SaaS team can instantly automate support and delight customers, no IT required.

User Personas

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

Metric Maven Mia

- 32 years old, college-educated support lead at mid-stage SaaS startup - Manages 8-person support team - Earns $70K annually in Austin, TX - 5+ years experience in customer support

Background

She began as front-line rep at an e-commerce startup, where her ticket-tag analytics project saved 20% resolution time. That experience sparked her passion for data-driven workflows, shaping her current obsession with metrics.

Needs & Pain Points

Needs

1. Real-time analytics integration across chat and tickets 2. Customizable SLA alerts to prevent response delays 3. Drag-and-drop reporting to avoid manual exports

Pain Points

1. Chasing stale tickets buried across multiple dashboards 2. Manual report curation eats into strategic planning time 3. Inconsistent SLA notifications cause urgent escalations

Psychographics

- Precision-driven, thrives on measurable outcomes - Passionate about relentless process refinement - Values clarity through data visualization - Motivated by beating SLA targets

Channels

1. Slack #support-updates 2. Intercom blog deep dives 3. LinkedIn PulseDesk group 4. Twitter analytics threads 5. Email weekly newsletters

Escalation Emma

- 38-year-old support operations manager at enterprise ERP vendor - MBA graduate with 10+ years in customer support - Manages 15-person escalation response team - Earns $95K annually in Chicago suburbs

Background

Emma cut her teeth as a crisis helpline operator, mastering empathetic communication under duress. She later led escalation protocols at a fintech scale-up, forging her fast-response mindset.

Needs & Pain Points

Needs

1. Unified escalation dashboard for instant triage 2. Automated team alerts for high-priority tickets 3. Pre-built escalation workflow templates to save setup time

Pain Points

1. Fractured team communication during peak crises 2. Slow manual prioritization delays critical response 3. Lack of real-time escalation visibility across tools

Psychographics

- Adrenaline-fueled, thrives on urgent problem-solving - Empathy-driven, prioritizes customer reassurance - Champions cross-team collaboration under stress - Values clear escalation workflows

Channels

1. PagerDuty mobile alerts 2. Slack escalation channels 3. PulseDesk in-app notifications 4. LinkedIn crisis forums 5. Email SMS fallback notifications

Scaling Sam

- 29-year-old director at hypergrowth B2B SaaS - CS degree with 7 years technical support - Supports users across 3 time zones - $85K base salary plus equity

Background

Sam launched his career overseeing support at a micro-SaaS, where 4× user growth demanded daily process tweaks. That pressure taught him to build modular automations that adapt on the fly.

Needs & Pain Points

Needs

1. Modular automation blocks for rapid scaling 2. Load-balancing across agents to manage peaks 3. Version control for evolving workflow templates

Pain Points

1. Automation breakages during sudden traffic spikes 2. Tedious rerouting when volume doubles overnight 3. Difficulty maintaining consistent processes across teams

Psychographics

- Growth-obsessed, seeks scalable support solutions - Experiment-driven, tests new automations rapidly - Thrives on autonomy and agile iteration - Values future-proof process architectures

Channels

1. GitHub automation discussions 2. Slack developer-integrations channel 3. PulseDesk product forum 4. Hacker News SaaS threads 5. Twitter startup support chats

Feedback Fiona

- 34, former UX researcher turned support analyst - Masters in Psychology with 6 years VOICE insights - $75K salary, remote across EU time zones - Manages 200+ monthly NPS surveys

Background

Fiona transitioned from UX research to support, driven by passion for customer empathy. She spearheaded post-chat survey integration at her last company, embedding feedback into development sprints.

Needs & Pain Points

Needs

1. Seamless survey integration within chat conversations 2. Automated tagging of feedback sentiment 3. Detailed feedback reports for product teams

Pain Points

1. Lost feedback buried in unstructured ticket threads 2. Manual sentiment analysis drains team bandwidth 3. Slow feedback-to-development turnaround frustrates users

Psychographics

- Customer-centric, values direct user insights - Driven by continuous feedback loops - Analytical, seeks patterns in qualitative data - Advocates for user-driven roadmaps

Channels

1. Typeform feedback widget 2. PulseDesk in-app surveys 3. Slack feedback channel 4. LinkedIn UX forums 5. Email weekly feedback digest

Product Features

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

Context Capsule

Delivers a concise, three-sentence overview of a ticket’s history—highlighting key interactions and critical customer details—so agents grasp context instantly and reduce onboarding time.

Requirements

History Aggregation Module

"As a support agent, I want the system to gather all past interactions and customer details in one place so that I can get a comprehensive view of the ticket history quickly."

Description

The system must aggregate all relevant data from multiple sources including live chat transcripts, past ticket interactions, and customer profile details into a unified data set that serves as the input for context summarization. This involves integration with the ticketing database API, chat logs service, and CRM data endpoints to fetch conversation history, timestamps, and key customer attributes. The unified data set should be normalized and stored in a temporary context buffer to ensure consistent formatting and quick access for summary generation.

Acceptance Criteria

Aggregating Live Chat Transcripts

All live chat messages from the chat logs service API for the associated ticket ID are retrieved; each message record includes sender ID, timestamp, and full message content; there are no missing entries for active sessions in the last 90 days.

Fetching Past Ticket Interactions

The module retrieves all past ticket interactions for the given ticket ID from the ticketing database API; each interaction includes change type, agent notes, and timestamp; the count of interactions matches the database records.

Retrieving Customer Profile Details

Customer attributes (name, company, support tier, contact information) are fetched from the CRM data endpoints; each attribute maps correctly to the unified schema fields; values match the latest data in the CRM.

Normalizing Data Formats

All retrieved data entries are transformed to the unified schema with consistent field names and data types; date/time fields are converted to UTC ISO-8601 format; text fields are sanitized to remove HTML tags and special characters.

Storing Unified Data in Temporary Context Buffer

The normalized data set is stored in the in-memory context buffer with a unique buffer ID; a retrieval request for the buffer returns the complete data set within 100ms; the buffer is configured to expire after 10 minutes.

Automated Context Summarization

"As a support agent, I want the ticket summary algorithm to highlight the most important interactions and details in three sentences so that I can understand the context at a glance."

Description

Implement an NLP-based summarization engine that processes the aggregated data to produce a concise three-sentence overview of the ticket history. The engine should identify key events, sentiments, and critical customer details such as issue category, resolution attempts, and urgency. It must be configurable to optimize for brevity and relevance, ensuring that the output captures the most salient information without extraneous details.

Acceptance Criteria

Real-Time Summary Generation on Ticket View

Given an agent opens a ticket, when the summarization engine processes the aggregated ticket history, then a concise three-sentence overview is displayed within 2 seconds, capturing issue category, resolution attempts, and urgency.

Configurable Brevity Adjustment

Given an admin configures the summarization engine’s brevity setting to ‘high’, when a summary is generated, then the output strictly contains three sentences; when set to ‘medium’, then it contains two to four sentences.

Key Events and Sentiment Accuracy

Given a ticket containing both positive and negative customer interactions, when the engine summarizes the history, then the summary correctly identifies and labels at least two key events and reflects the predominant sentiment.

High-Load Performance

Given 100 concurrent requests for summarization, when the engine processes these requests, then the average response time remains under 2 seconds with no failures.

Exclusion of Irrelevant Details

Given a ticket with extensive comment threads including off-topic chatter, when the summary is generated, then it excludes non-critical details and focuses only on essential customer information and resolution steps.

Context Capsule UI Component

"As a support agent, I want to see the Context Capsule summary integrated directly in my ticket view so that I can quickly reference it without navigating away."

Description

Develop a dedicated UI component within the agent dashboard to display the Context Capsule summary. This component should be positioned prominently near the ticket thread, render the three sentences with clear typography, and support hover or click interactions to reveal more detail if needed. It must adhere to the product's design guidelines and be responsive to different screen sizes, ensuring consistent readability.

Acceptance Criteria

Displaying Context Capsule on Ticket Load

Given an agent opens a ticket, when the dashboard loads, then the Context Capsule UI component is displayed prominently above the ticket thread, rendering exactly three sentences summarizing the ticket history with clear typography.

Hover to Expand Detailed Context

Given an agent hovers over the Context Capsule text, when the hover state persists for 500ms, then a tooltip appears showing additional details for each sentence, including timestamps and customer metadata.

Click to Toggle Full Summary

Given an agent clicks the expand icon on the Context Capsule, when clicked, then the component expands to reveal up to six sentences with vertical scroll support, and clicking again collapses it back to three sentences.

Responsive Design on Mobile and Desktop

Given the dashboard is viewed on screens between 320px and 1200px wide, when the window is resized, then the Context Capsule adjusts its typography size, line spacing, and container width to maintain readability without horizontal scrolling.

Compliance with Design System Styles

Given the product’s design guidelines, when rendering the Context Capsule, then the component uses the approved design tokens for font family, size (14px), color (#333333), line height (1.5), and margin (16px bottom).

Real-Time Update Engine

"As a support agent, I want the Context Capsule to refresh automatically when new messages come in so that I always have the latest information."

Description

Enable real-time updates of the Context Capsule when new messages or ticket changes occur. The system should listen for events such as incoming chat messages, ticket status changes, and customer replies, triggering re-aggregation and re-summarization processes automatically. Latency between new data arrival and summary update should not exceed 2 seconds to maintain context accuracy.

Acceptance Criteria

Incoming Chat Message Update

Given a new chat message arrives for an active ticket, When the Real-Time Update Engine processes the event, Then the Context Capsule must refresh within 2 seconds to include the latest message content.

Ticket Status Change Update

Given a ticket status changes (e.g., from 'open' to 'pending'), When the event is detected by the engine, Then the Context Capsule must reflect the updated status in its summary within 2 seconds.

Customer Email Reply Integration

Given a customer replies via email linked to an existing ticket, When the system ingests the reply event, Then the Context Capsule must update the summary to include the email content within 2 seconds.

High Volume Message Spike

Given a burst of 50 message events occurs within a 10-second window for a single ticket, When the engine processes each event, Then every Context Capsule update must complete within 2 seconds of its respective event.

Recovery After Downtime

Given the Real-Time Update Engine restarts after an unplanned downtime, When it processes the backlog of missed events, Then the Context Capsule must reflect all historical changes and reach current state within 5 seconds of restart.

Admin Customization Interface

"As a support lead, I want to configure how the Context Capsule is generated so that I can tailor it to my team's workflows and priorities."

Description

Provide an admin interface that allows support leads to customize summarization parameters, such as the number of sentences, inclusion of specific data points (e.g., sentiment, attachments), and keyword filters. This interface should include tooltips explaining each parameter and preview functionality to test different configurations. Changes should be applied dynamically without requiring code deployments.

Acceptance Criteria

Adjusting Summary Length

Given an admin sets the number of sentences to 5 and clicks the preview button, then the summary preview displays exactly 5 sentences.

Including Specific Data Points

Given an admin enables sentiment and attachment inclusion toggles and saves settings, when previewing a summary, then the preview includes sentiment labels and attachment counts.

Using Keyword Filters

Given an admin enters one or more keywords into the filter field and applies changes, then the generated summary includes only sentences containing those keywords.

Viewing Inline Tooltips

Given an admin hovers over any parameter label, then a tooltip appears within 500ms explaining the parameter’s function and disappears when the cursor moves away.

Dynamic Application of Changes

Given an admin modifies any customization parameter and saves without a page reload, then subsequent summaries immediately reflect the new configuration without code deployment.

ToneCraft

Automatically tailors draft responses to match the user’s sentiment—offering empathetic, professional, or urgent tones—ensuring every message aligns with the customer’s emotional state and brand voice.

Requirements

Sentiment Analysis Engine Integration

"As a support agent, I want the system to detect the customer’s sentiment automatically so that I can craft responses that are emotionally appropriate without manual assessment."

Description

Implement a robust sentiment analysis engine that automatically evaluates incoming customer messages to determine emotional context (e.g., positive, negative, neutral, urgent). The engine should leverage machine learning models to accurately classify sentiment in real time, integrate seamlessly with existing ticketing workflows, and expose APIs for other modules to consume sentiment scores. This integration ensures that subsequent response drafting can be tailored precisely to the customer’s emotional state, reducing manual analysis and improving response relevance.

Acceptance Criteria

Real-Time Sentiment Classification of Incoming Messages

Given an incoming customer message, When the message arrives into the ticketing system, Then the sentiment engine must classify the message as positive, negative, neutral, or urgent within 1 second with at least 90% accuracy.

Sentiment Score API Accessibility

Given an authenticated module request to the sentiment API, When requesting the sentiment score for a given message ID, Then the API must return a JSON payload containing sentiment label, confidence score, and timestamp with HTTP status 200 within 500ms.

Seamless Workflow Integration

Given the sentiment engine integration, When a new ticket is created, Then the ticket routing workflow should automatically apply tagging or priority level based on the sentiment classification without manual intervention.

Error Handling for Unprocessable Messages

Given a malformed or unprocessable message, When the sentiment engine cannot classify the sentiment, Then it should return an 'unknown' sentiment with a descriptive error code, respond within 1 second, and log the incident for review.

Scalability Under High Ticket Volume

Given a load of 1000 concurrent messages, When the engine processes incoming messages, Then classification response time remains under 2 seconds for 95% of requests and no errors occur due to overload.

Model Update Deployment Without Downtime

Given a new version of the sentiment model, When deploying the update, Then the system must maintain classification availability with zero downtime and consistent response times.

Tone Profile Configuration

"As a support lead, I want to define and manage tone profiles so that the automated responses reflect our brand voice and the intended emotional impact."

Description

Create a configuration interface that allows administrators and support leads to define and manage custom tone profiles (e.g., empathetic, professional, urgent). Each profile should include parameters such as vocabulary preference, formality level, and response length guidelines. Profiles must be stored in a centralized repository, versioned for auditing, and accessible to the response generation engine for consistent application across all channels.

Acceptance Criteria

Admin Creates New Tone Profile

Given an administrator is on the Tone Profile Configuration interface When they enter valid values for profile name, vocabulary preference, formality level, and response length guidelines and click "Save" Then a new tone profile is stored in the centralized repository with version number 1 and visible in the profile list

Support Lead Edits Existing Tone Profile

Given a support lead selects an existing tone profile When they update one or more parameters (vocabulary, formality, length) and click "Save" Then the changes are persisted in the repository and the profile version is incremented by one

Version History Audit

Given an administrator opens the version history for a tone profile When they view the history Then the system displays all previous versions with timestamps, change descriptions, and the user who made each change

Profile Accessible to Response Engine

Given a customer ticket requires an automated response When the response generation engine is invoked with a specified tone profile Then the draft message uses the vocabulary, formality level, and length guidelines defined in that profile

Prevent Deletion of In-Use Profile

Given a tone profile is currently referenced by an active workflow When an administrator attempts to delete the profile Then the system blocks deletion and displays an error explaining that the profile is in use

Response Generation Module

"As a support agent, I want the system to generate draft replies based on customer sentiment and tone settings so that I can save time on writing and maintain consistency."

Description

Develop a response generation module that uses the detected sentiment and selected tone profile to produce draft replies. The module should integrate with the sentiment analysis engine and the profile repository, apply natural language generation techniques to create coherent, contextually accurate responses, and include fallback mechanisms when confidence scores are low. Generated drafts must be delivered to the agent interface for review and editing.

Acceptance Criteria

Sentiment-Aligned Draft Generation

Given a customer message with detected positive sentiment and selected “empathetic” tone profile, When the response generation module is invoked, Then the draft reply should reflect positive sentiment with empathetic phrasing and comply with the brand voice guidelines.

Fallback Mechanism for Low Confidence Scores

Given a generated draft with a confidence score below the defined threshold, When the draft is delivered to the agent interface, Then the system should flag the draft as low-confidence and provide a default template suggesting manual review.

Integration with Agent Interface

Given a newly generated draft reply, When the module completes generation, Then the draft should appear in the agent’s chat window within two seconds and allow inline editing.

Multi-Turn Context Handling

Given a conversation with multiple prior messages and an agent-selected tone profile, When the response generation module processes the latest customer input, Then the draft should accurately reference the prior context and maintain coherence across turns.

Profile Repository Retrieval

Given a request to generate a draft with a specific tone profile, When the module fetches tone parameters from the repository, Then it should retrieve the correct profile settings within one second and apply them to the generated reply.

Tone Adjustment Controls

"As a support agent, I want to adjust the tone of the suggested reply on the fly so that I can refine messaging without restarting the drafting process."

Description

Provide in-context controls within the agent’s reply editor to adjust the tone of the generated draft. Agents should be able to switch between predefined profiles or fine-tune specific attributes (e.g., formality, empathy level, urgency) with immediate regeneration of the response. Changes should be logged to preserve an audit trail and allow reversion to previous versions if needed.

Acceptance Criteria

Tone Profile Switching

Given an agent has generated a draft response in the reply editor, when the agent selects a predefined tone profile (e.g., Empathetic, Professional, Urgent), then the system shall regenerate the draft within 2 seconds using the selected profile and display the updated message with clear indication of the active profile.

Fine-Tune Formality Level

Given an agent is editing a generated draft, when the agent adjusts the formality slider to a new value and confirms, then the system shall regenerate the response reflecting the new formality level and update the draft accordingly.

Adjust Empathy Level

Given a draft response exists, when the agent increases or decreases the empathy attribute by one increment and applies the change, then the system shall produce a revised draft that corresponds to the new empathy setting and visibly annotate the change in the response history.

Modify Urgency Attribute

Given an agent views the draft in the editor, when the agent toggles the urgency setting to high or low and requests regeneration, then the system shall regenerate the message emphasizing the selected urgency tone and highlight the urgency attribute in the control panel.

Audit Trail Logging and Reversion

Given multiple tone adjustments have been made on a response, when the agent views the audit trail and selects a previous version, then the system shall restore the draft to the selected version exactly as it appeared at that point, and log the reversion action with a timestamp and user ID.

Real-time Preview and Feedback

"As a support agent, I want to preview the drafted response with sentiment indicators and provide feedback so that the system’s accuracy improves over time."

Description

Implement a real-time preview pane that displays the generated response alongside sentiment indicators and profile metadata. Agents should see visual cues (e.g., color-coded sentiment badges) and be able to provide feedback on the accuracy of tone and sentiment classification. Feedback should be collected and fed back into model training pipelines to improve future performance.

Acceptance Criteria

Agent Views Generated Response with Sentiment Indicators

Given an agent opens the preview pane When the system generates a draft response Then the response is displayed alongside sentiment badges indicating positive, neutral, or negative tone

Sentiment Badge Color Accuracy

Given a response sentiment is classified When the preview pane renders the badge Then the badge color matches the classification (green for positive, gray for neutral, red for negative)

Agent Submits Tone Feedback

Given an agent reviews the generated response When the agent selects a feedback option and submits an optional comment Then the feedback is recorded in the UI and a confirmation message is displayed

Feedback Recorded in Training Pipeline

Given agent feedback is submitted When the feedback enters the system Then it is logged and queued for model retraining within 5 minutes of submission

Preview Pane Performance under Load

Given 200 concurrent preview requests When multiple agents open the real-time pane Then the response display, sentiment badges, and feedback UI load within 1.5 seconds for each request

Reply Palette

Generates three distinct, ready-to-send response options based on sentiment and ticket context, allowing agents to choose the best fit and cut drafting time in half while maintaining personalization.

Requirements

AI Response Generation

"As a support agent, I want the system to provide three tailored response options so that I can choose the best reply faster and maintain high-quality, personalized communication."

Description

Leverage AI algorithms to automatically generate three distinct, context-aware response drafts based on the ticket’s content and identified sentiment. Each draft should vary in tone and structure, allowing agents to select the most appropriate response quickly. The feature must integrate with the existing ticketing system, ensure data privacy, and minimize manual editing to accelerate response times by at least 50%.

Acceptance Criteria

Sentiment-Based Response Variants Generated

Given a customer ticket with identified positive sentiment When AI Response Generation is triggered Then three distinct response drafts are generated with varying tones (e.g., enthusiastic, neutral, professional) that reflect the positive sentiment

Integration with Ticketing System

Given a ticket in the system When the agent requests AI-generated responses Then the generated drafts are displayed within the ticket interface without requiring manual copying or data re-entry

Data Privacy Assurance

Given a ticket containing sensitive customer information When responses are generated Then no sensitive data is logged or exposed outside the secure processing module and data retention complies with privacy policy

Tone Variation Check

Given a single ticket context When the AI model produces three drafts Then each draft must have a distinct tone (e.g., empathetic, formal, casual) and structure with at least 30% lexical variation between them

Response Generation Performance

Given a ticket submission time When AI response generation is initiated Then all three drafts are generated and displayed to the agent within 5 seconds achieving at least 50% reduction in average drafting time

Sentiment Analysis Integration

"As a support agent, I want the system to identify the customer’s sentiment so that the generated replies align with their emotional tone and improve engagement."

Description

Incorporate a sentiment analysis engine to assess the customer’s tone and emotional state within each ticket. The engine should categorize sentiment (e.g., positive, neutral, negative) and feed this insight into the response generation module to tailor message tone appropriately. It must update in real time when ticket content changes and support multiple languages for global applicability.

Acceptance Criteria

Sentiment Analysis on Ticket Submission

Given a new ticket is created with body text in English containing clear emotional indicators, When the sentiment engine processes the ticket within 2 seconds, Then it assigns a sentiment label (positive, neutral, or negative) with a confidence score of at least 85%.

Real-Time Sentiment Reevaluation on Ticket Update

Given an existing ticket is edited by the customer or agent, When the update is saved, Then the sentiment engine reprocesses the full ticket content and updates the sentiment label and confidence score in under 2 seconds.

Sentiment Detection for Multiple Languages

Given tickets written in Spanish, French, or German are received, When the sentiment engine processes them, Then it correctly classifies sentiment with at least 80% accuracy for each supported language.

Tone Selection in Response Generation

Given the sentiment label and score are available, When the reply palette generates three response options, Then each message’s tone is tailored to the classifier’s sentiment (e.g., empathetic for negative, supportive for neutral, enthusiastic for positive) and includes sentiment rationale.

Fallback Behavior on Sentiment Service Failure

Given the sentiment engine fails to respond or returns an error during analysis, When generating reply options, Then the system defaults to a neutral tone for all responses and logs the error for troubleshooting.

Contextual Data Fetch

"As a support agent, I want the reply generator to include the full ticket context so that responses are accurate and informed by the customer’s history."

Description

Retrieve and consolidate all relevant ticket data—including previous interactions, customer profile, product details, and workflow status—from the unified support platform. Ensure low-latency access to this context so that AI-generated responses reflect the full history and nuances of the conversation. Data synchronization should be seamless across live chat and ticketing modules.

Acceptance Criteria

Ticket Context Availability on Open

Given an agent opens a ticket, when the ticket interface loads, then all previous interactions, customer profile, product details, and workflow status are retrieved and rendered within 200 milliseconds.

Seamless Data Sync Between Live Chat and Ticketing

Given an active live chat session is converted to a ticket, when the conversion completes, then the entire chat history and ticket metadata are synchronized and accessible in both modules without data loss.

Real-Time Customer Profile Retrieval

Given an agent views a customer in the ticket view, when the customer profile pane is opened, then the latest customer information (contact details, account tier, usage metrics) is displayed within 150 milliseconds.

Product Information Accuracy

Given a ticket references a specific product, when the ticket context is fetched, then the correct product details (name, version, subscription plan) are displayed and match the records in the product database.

Workflow Status Reflection

Given a ticket moves through automated workflows, when the workflow state changes, then the current status is reflected in the ticket context and visible to agents in real-time.

Personalization Token Support

"As a support agent, I want personalized placeholders automatically filled so that I can send tailored replies without manual insertion of customer details."

Description

Enable insertion of dynamic personalization tokens (e.g., customer name, account ID, product name) into the AI-generated drafts. The tokens should auto-populate from the ticket metadata, and agents must have the ability to preview and adjust tokens before sending. This ensures replies remain personal and relevant without manual data entry.

Acceptance Criteria

Auto-Populate Tokens from Ticket Metadata

Given a support ticket with customer name, account ID, and product name metadata When the agent opens Reply Palette and generates drafts Then each draft must include personalization tokens replaced by the corresponding metadata values

Token Preview and Edit Interface

Given a generated draft containing personalization tokens When the agent selects the preview option Then the UI must display the actual metadata values in place of tokens and allow inline editing of each token

Manual Token Adjustment before Sending

Given auto-populated tokens in a selected draft When the agent clicks on a token placeholder Then an inline editor must allow the agent to override the token value and reflect the change in the draft text

Missing Metadata Handling

Given a ticket missing one or more metadata fields When generating AI response drafts Then the system must insert a default placeholder (e.g., “Unknown”) for each missing token and visually flag the draft for agent review

Token Persistence across Workflow Steps

Given an agent saves a draft with edited token values into a no-code workflow When the draft is retrieved in a subsequent workflow step Then the personalized token values must remain unchanged and display correctly

Agent Selection Interface

"As a support agent, I want a clear interface showing multiple reply options so that I can quickly compare and choose the best response."

Description

Design an intuitive UI component within the agent workspace to display the three AI-generated reply options side by side. Include features for quick selection, editing, and A/B comparison. The interface should provide tone and sentiment indicators for each draft, support keyboard shortcuts, and maintain consistency with PulseDesk’s design language.

Acceptance Criteria

Side-by-Side Display of Reply Options

Given AI has generated three reply options, when the agent opens a customer ticket, then the interface displays all three drafts side by side in the agent workspace.

Quick Selection via Mouse and Keyboard

Given the three displayed drafts, when the agent clicks the selection button below a draft or presses the corresponding keyboard shortcut (1, 2, or 3), then the chosen draft is immediately populated into the reply editor.

Tone and Sentiment Indicators Visible

Given the three drafts are displayed, then each draft shows its sentiment indicator (positive, neutral, negative) and tone label (formal, friendly, etc.) adjacent to the draft text.

In-line Editing of Selected Reply

Given an agent has selected a draft, when the draft is populated in the reply editor, then the agent can edit the text inline without losing the original formatting or tone indicators.

A/B Comparison Mode

Given the agent wants to compare two drafts, when the agent activates A/B compare mode and selects two drafts, then both drafts are highlighted side by side with differences visually marked.

Action Blueprint

Analyzes ticket content and customer history to recommend step-by-step follow-up tasks and resolutions, embedding actionable next steps directly into the interface to guide agents to faster, consistent outcomes.

Requirements

Ticket Content Analyzer

"As a support agent, I want the system to analyze ticket content so that I can receive recommended follow-up tasks tailored to the customer’s specific issue."

Description

Analyze incoming ticket text using natural language processing to identify key issues, extract relevant entities, and determine the appropriate context for follow-up tasks. This requirement ensures precise understanding of customer requests, enabling the recommendation engine to generate accurate, tailored next steps. It integrates directly with the ticketing system to process messages in real time and tag critical information for downstream modules.

Acceptance Criteria

Real-Time Ticket Content Parsing

Given an incoming ticket in the ticketing system, when the Ticket Content Analyzer receives the text, then it must process the text in under 500ms and identify the primary issue with at least 90% accuracy.

Entity Extraction Accuracy

Given a ticket containing customer-specific details (e.g., order number, product name), when processed by the Analyzer, then it must correctly extract all relevant entities with at least 95% precision and recall.

Contextual Follow-Up Tagging

Given analyzed ticket content, when the Analyzer completes, then it must assign context tags (e.g., 'billing_issue', 'technical_error') that align with a pre-defined taxonomy, with 98% consistency.

Multi-language Ticket Analysis

Given tickets in English, Spanish, and French, when processed, then the system identifies key issues and entities in each language with at least 90% accuracy per language.

High Volume Ticket Processing

Given a surge of 100 simultaneous tickets, when processed in real-time, then the Analyzer must maintain performance (average latency under 1 second) and accuracy above specified thresholds (90% issue detection).

Customer History Integration

"As a support agent, I want to have customer history integrated into recommendations so that I can tailor resolutions based on past interactions."

Description

Retrieve and consolidate customer interaction history, purchase records, and previous support logs to provide contextual data for the recommendation engine. This requirement enhances personalization and consistency of suggestions by factoring in past resolutions and customer preferences. It seamlessly interfaces with the CRM and ticket database to fetch relevant data before task generation.

Acceptance Criteria

Agent Opens a New Ticket

Given an agent opens an existing customer ticket, When the ticket view initializes, Then the system retrieves and displays the complete customer history—including past interactions, purchase records, and support logs—within 2 seconds.

Displaying Customer History Summary

Given the customer history data is available, When the history panel renders, Then it shows a chronological summary of the last 12 months of interactions, purchases, and support cases, with clear timestamps and identifiers for each entry.

Agent Searches Customer History Records

Given an agent enters a keyword or date range in the history search bar, When the search is executed, Then the results are accurately filtered across all history records and returned within 1 second.

CRM Unavailable During Data Fetch

Given the CRM integration endpoint is unreachable, When the system attempts to fetch history, Then it retries the request up to two times and, if still unsuccessful, displays a notification and loads available data from the ticket database.

Data Access Respects Privacy Settings

Given a customer has restricted data sharing settings, When the system retrieves history, Then only authorized fields are displayed and any restricted fields are redacted with a placeholder.

Recommendation Engine

"As a support agent, I want the system to recommend a sequence of actionable steps so that I can resolve tickets faster and more consistently."

Description

Generate a sequence of step-by-step follow-up tasks and resolution suggestions by combining insights from ticket analysis and customer history. The engine applies business rules and machine learning models to prioritize the most effective actions, providing rationale for each step. It ensures consistent outcomes and accelerates ticket resolution by offering clear, actionable guidance.

Acceptance Criteria

High-Priority Ticket Follow-up Recommendation

Given an agent opens a ticket marked as high priority, when the recommendation engine analyzes the ticket content and customer history, then it displays a sequence of at least three prioritized follow-up tasks with rationale within two seconds.

New Customer Onboarding Inquiry Resolution

Given a ticket submitted by a customer with an account age less than 7 days, when the engine processes the inquiry, then it suggests step-by-step onboarding actions tailored to the customer’s subscription level and displays clear explanations for each action.

Escalated Technical Issue Guidance

Given a ticket flagged for escalation due to technical complexity, when the engine evaluates technical logs and prior agent notes, then it recommends a minimum of two advanced troubleshooting steps and a fallback escalation path with accompanying justification.

Multi-Channel Ticket Context Recommendations

Given a ticket that includes both live chat transcripts and email correspondence, when the engine aggregates channel data and customer history, then it generates cohesive follow-up tasks that reference key points from both channels and ensures no duplication of actions.

Recurring Issue Pattern Suggestions

Given a ticket matching a known recurring issue in the system, when the engine identifies pattern similarities, then it recommends the proven resolution workflow with timestamps of past successful resolutions and highlights any deviations needed for the current context.

Interactive Task Embedding

"As a support agent, I want tasks embedded in the interface so that I can execute recommended steps without leaving the ticket view."

Description

Embed recommended tasks directly into the agent interface as interactive elements—such as buttons, checklists, and inline annotations—for one-click execution, progress tracking, and note-taking. This requirement streamlines workflow by allowing agents to act on suggestions without context switching and ensures that each step’s completion is recorded in the ticket timeline.

Acceptance Criteria

One-Click Task Action Button Integration

Given an agent views a recommended task in the ticket interface, when the agent clicks the associated action button, then the task is executed and the ticket timeline logs the action with timestamp.

Progress Tracking Checklist Display

Given a multi-step recommended task is embedded as a checklist, when the agent marks a step complete, then the checklist visually updates and the ticket timeline records each completed step.

Inline Annotation Note Recording

Given an agent adds an inline annotation to a recommended task, when the agent submits the note, then the note is saved under the task with author and timestamp and appears in the ticket timeline.

Real-Time Task Status Synchronization

Given multiple agents viewing the same ticket, when one agent completes an embedded task, then the task status updates in real time for all agents and synchronizes with the ticket timeline.

Accessible Task Execution via Keyboard Shortcuts

Given an agent uses a keyboard shortcut for a recommended task, when the shortcut matches the task action, then the task executes and the ticket timeline logs the action.

Workflow Customization Interface

"As a support lead, I want to customize recommendation workflows so that they align with our team's processes and business rules."

Description

Provide a no-code configuration UI where support leads can customize recommendation templates, adjust task sequences, define escalation rules, and manage business logic. This requirement allows teams to adapt action blueprints to their specific processes, maintain compliance, and respond to evolving support strategies without developer intervention.

Acceptance Criteria

Customize Recommendation Template

Given a support lead opens the Workflow Customization Interface and selects a recommendation template, When they modify the template fields and click Save, Then the updated template is persisted in the database and reflected in the Action Blueprint preview.

Adjust Task Sequence

Given a support lead views the task sequence list, When they drag and drop tasks to reorder and click Publish, Then the new sequence is saved, and subsequent Action Blueprints display tasks in the updated order.

Define Escalation Rule

Given a support lead navigates to escalation rules, When they create a rule with specified conditions and save it, Then the new rule appears in the rule list and triggers correctly when conditions are met in a test ticket scenario.

Manage Business Logic Conditions

Given a support lead configures conditional logic based on ticket priority or customer sentiment, When they apply the logic, Then the conditions evaluate correctly during a ticket test, executing the appropriate follow-up tasks.

Preview and Publish Workflow

Given a support lead finishes customizing the workflow, When they click Preview and then Publish, Then the system displays an accurate walkthrough of the workflow steps and makes the new configuration available to live agents.

Alert Beacon

Provides real-time notifications for tickets with escalating or negative sentiment, automatically prioritizing high-risk cases so agents can intervene proactively and prevent churn.

Requirements

Real-Time Sentiment Analysis

"As a support agent, I want the system to analyze ticket messages for negative sentiment in real-time so that I can be notified immediately when customers are dissatisfied and intervene proactively."

Description

Implement a sentiment analysis engine that processes incoming ticket text in real-time, identifying negative or escalating sentiment as messages arrive. This feature will leverage natural language processing (NLP) to analyze customer language, detecting cues of frustration or dissatisfaction. By integrating directly into the ticket ingestion pipeline, the system will flag tickets with negative sentiment immediately upon receipt, enabling swift action. The expected outcome is a significant reduction in overlooked unhappy customers, leading to faster resolutions and higher satisfaction.

Acceptance Criteria

New Ticket Negative Sentiment Flagging

Given a new ticket with customer message containing negative sentiment (score ≤ 0.3), When the ticket ingestion pipeline processes the message, Then the system flags the ticket with a 'Negative Sentiment' tag and sets its priority to 'High' within 2 seconds.

Escalating Sentiment Alert During Live Chat

Given an ongoing live chat session, When the sentiment score drops by more than 20% compared to the initial message, Then the system sends a real-time alert to the support dashboard and notifies the assigned agent within 1 second.

Batch Sentiment Analysis Accuracy Validation

Given a test dataset of 100 labeled tickets, When processed by the sentiment engine, Then the engine correctly classifies negative versus positive sentiment with at least 90% accuracy.

High-Volume Ticket Ingestion Under Load

Given 200 tickets arriving simultaneously, When processed by the system under peak load, Then 99% of tickets are analyzed, flagged appropriately, and returned within 2 seconds each.

Multi-Language Sentiment Analysis Support

Given ticket messages in English, Spanish, and French containing negative sentiment, When processed by the engine, Then each language is correctly identified as negative sentiment with at least 85% accuracy.

High-Risk Case Prioritization

"As a support manager, I want high-risk tickets to be auto-prioritized so that agents can focus on critical cases first and prevent potential churn."

Description

Automatically rank and prioritize tickets based on sentiment scores and escalation risk, ensuring that high-risk cases surface at the top of agents’ queues. The prioritization logic will combine sentiment intensity, ticket age, and customer value metrics to compute a risk score. Integration with the existing ticket dashboard will visually highlight prioritized cases, guiding agents to focus on cases most likely to churn. The result is more efficient resource allocation and reduced customer churn.

Acceptance Criteria

Real-Time Risk Score Computation

Given a new support ticket is received with sentiment intensity, ticket age, and customer value metrics, When the ticket is ingested into the system, Then a risk score is computed within 2 seconds using the defined prioritization logic and stored in the ticket record.

High-Risk Ticket Highlighting on Dashboard

Given the ticket dashboard is open and a ticket has a risk score above the high-risk threshold, When the dashboard refreshes, Then the high-risk ticket is visually highlighted with a red banner and labeled "High-Risk" at the top of the ticket card.

Agent Queue Sorting by Risk Score

Given an agent logs into the dashboard, When the ticket list is displayed, Then tickets are sorted in descending order by risk score so that the highest-risk tickets appear first in the queue.

Risk Score Calculation with Customer Value and Ticket Age

Given a ticket record contains sentiment intensity, ticket creation timestamp, and customer lifetime value, When the prioritization engine processes the ticket, Then the risk score is calculated using the formula (SentimentWeight * SentimentScore) + (AgeWeight * TicketAgeHours) + (ValueWeight * CustomerValue) and results in a normalized value between 0 and 100.

Notification Trigger for Newly Prioritized High-Risk Tickets

Given a ticket’s computed risk score crosses the high-risk threshold after re-evaluation, When the score is updated, Then an automated real-time notification is sent to the assigned agent’s inbox and displayed as a pop-up alert in the dashboard.

Customizable Alert Thresholds

"As a support lead, I want to set custom sentiment alert thresholds so that I can tune the system’s sensitivity to match our team’s capacity and avoid unnecessary notifications."

Description

Provide configuration capabilities that allow support leads to define custom sentiment and escalation thresholds that trigger alerts. Users can adjust sensitivity levels based on support volume, customer segments, or product lines. The settings interface will enable threshold tuning via sliders or numeric inputs, with real-time previews of expected alert behavior. This customization ensures alerts are relevant and reduces noise from non-critical tickets.

Acceptance Criteria

Admin Defines Sentiment Alert Threshold

Given the support lead opens the alert settings panel When they adjust the sentiment slider to 75% Then the system displays a confirmation that new sentiment alerts will trigger for tickets scoring below 75%

Admin Configures Escalation Level Threshold

Given the support lead selects an escalation numeric input When they enter a value of 3 escalations Then the system applies this threshold and highlights tickets with three or more escalations for real-time alerting

Real-time Preview Updates on Threshold Change

Given the support lead changes either slider or numeric input When the value is modified Then the preview list updates instantly to show sample tickets that would trigger alerts under the new settings

Validation Prevents Invalid Threshold Values

Given the support lead enters a negative number or non-numeric value in the threshold field When they attempt to save Then the system displays an inline validation error and disables the save button

Threshold Settings Persist Across Sessions

Given the support lead saves custom threshold settings When they log out and back in Then their previous settings are loaded and applied without requiring reconfiguration

Multi-Channel Notification Delivery

"As a support agent, I want alerts delivered to Slack and my email so that I don’t miss critical notifications regardless of where I am working."

Description

Enable alert delivery through multiple channels—including in-app notifications, email, and Slack—to ensure agents receive high-priority alerts wherever they work. The feature will integrate with the existing notification service, adding connectors for email SMTP and Slack Webhooks. Users can select preferred channels per agent or team. This approach minimizes missed alerts and accelerates response times by meeting agents in their preferred communication tools.

Acceptance Criteria

Agent Configures Notification Channels

Given an admin user accesses the multi-channel notification settings and selects email and Slack for a team; when the user saves the configuration; then the selected channels are persisted in the user’s profile and displayed correctly in the settings UI.

High-Priority Alert Sent via Email

Given a ticket’s sentiment score falls below threshold and status escalates; when the alert engine triggers notifications; then an email with ticket details and escalation reason is sent to the configured agent’s email address within 30 seconds.

High-Priority Alert Sent via Slack

Given a Slack webhook URL is configured for a support team; when a ticket is flagged as high-risk; then a POST request with the alert payload is successfully delivered to the specified Slack channel and receives a 200 OK response.

In-App Notification Display

Given an agent is logged into PulseDesk; when a ticket’s sentiment escalates to high-priority; then an in-app banner notification appears on the agent’s dashboard within 5 seconds with a clickable link to the ticket.

Agent Preference Overrides Defaults

Given an agent has chosen only email notifications in personal settings; when a ticket-triggered alert occurs; then the system sends the notification via email only and does not send in-app or Slack notifications to that agent.

Sentiment Trend Dashboard

"As a support manager, I want to see sentiment trends over time so that I can identify patterns and adjust team resources to address recurring issues."

Description

Create a dashboard view that visualizes sentiment trends over time, showing the volume and intensity of negative, neutral, and positive tickets. The dashboard will include charts for daily and weekly sentiment distribution, heatmaps for peak negative sentiment hours, and filters by product or customer segment. By surfacing patterns and anomalies, this feature helps leadership identify systemic issues and allocate resources proactively.

Acceptance Criteria

Viewing Daily Sentiment Trends

Given the user navigates to the Sentiment Trend Dashboard, when the daily sentiment line chart loads, then it displays separate data points for negative, neutral, and positive ticket counts for each of the past 30 days with data accuracy within ±2%.

Comparing Weekly Sentiment Distribution

Given the user switches to the weekly view, when the weekly sentiment bar chart renders, then it shows the percentage distribution of negative, neutral, and positive tickets for the current week and the previous week side by side.

Analyzing Peak Negative Sentiment Hours

Given the dashboard’s heatmap panel is displayed, when the heatmap loads, then it highlights the top 5 hours with the highest volume of negative sentiment tickets in the selected date range, and hovering over a cell reveals the exact timestamp and count.

Filtering Sentiment by Product

Given one or more products are selected in the product filter dropdown, when the filter is applied, then all charts and heatmaps update to reflect only tickets associated with the selected products, and the filter state persists on page reload.

Filtering Sentiment by Customer Segment

Given the user selects a customer segment from the segment filter, when the filter is applied, then all visualizations update to show sentiment data exclusively for tickets within that segment and clear the filter when the segment is deselected.

Identifying Sentiment Pattern Anomalies

Given anomaly detection is enabled, when daily negative sentiment increases by more than 20% compared to the rolling 7-day average, then the dashboard flags the corresponding data point with a visual indicator and logs an alert in the activity feed.

Smart Template Finder

Leverages AI-driven filters and historical usage data to surface the most relevant industry-tailored workflow blueprints. Users spend less time searching and more time implementing, ensuring they always start with the optimal template for their unique support scenarios.

Requirements

Adaptive AI Filter Algorithms

"As a support lead, I want AI-driven filters that adapt to my ticket context so that I can quickly find the most relevant workflow templates without manual searching."

Description

Implement AI-driven filters that dynamically analyze user input, ticket metadata, and conversation context to surface the most relevant workflow templates. This requirement ensures that the system interprets user needs accurately, delivering precise template recommendations and reducing manual search time. It integrates with the AI engine and existing ticketing data, continuously learning from new queries to refine its filtering logic, resulting in more efficient template discovery and improved user satisfaction.

Acceptance Criteria

Real-time Keyword Extraction

Given a user enters a free-text search query, when the AI filter processes the input, then the top-recommended template must have a relevance score ≥ 0.8 and correspond to the most frequently used template for that query in historical data.

Metadata-based Filtering

Given a ticket with specific metadata attributes (e.g., priority: high, language: Spanish), when the AI filter runs, then it must only recommend templates tagged for Spanish localization and high-priority workflows.

Contextual Chat Transcript Analysis

Given an ongoing live chat where the last five messages reference billing issues, when the AI analyzes the conversation context, then it must surface at least one template addressing billing resolutions with ≥ 75% keyword match.

Continuous Learning from User Selections

Given a user selects the same AI-recommended template for 10 similar ticket queries, when a new similar query arrives, then that template must appear as the top recommendation in subsequent searches.

Performance under High Query Volume

When 100 concurrent users submit template-filtering requests, then the AI filter must return recommendations within 500ms on average and maintain an overall relevance accuracy ≥ 80%.

Historical Usage Analytics Integration

"As a support manager, I want historical usage data to influence template recommendations so that I can start with workflows that have a proven track record of success."

Description

Aggregate and analyze historical template usage data, including frequency of use, success rates, and user ratings, to inform and prioritize recommendations. This requirement leverages past usage patterns to surface proven templates and promotes those that have demonstrated high performance in similar scenarios. It connects to the analytics database and updates recommendations in real time, ensuring that users benefit from collective organizational intelligence.

Acceptance Criteria

Real-time Recommendation Update

Given new usage data is stored in the analytics database When the recommendation engine runs Then updated recommendations reflecting the latest usage patterns appear in the Smart Template Finder within 2 seconds of data arrival

High-Frequency Template Usage Aggregation

Given 10,000 template usage records are added within a 5-minute window When the aggregation process executes Then it completes processing all records without error and within 5 minutes, ensuring no data loss

User Rating-Weighted Template Prioritization

Given templates have user rating values between 1 and 5 When computing recommendation scores Then each template's score must incorporate its average user rating with a minimum weight of 20% in the overall ranking algorithm

Data Sync Error Handling

Given a transient database connectivity error during data sync When the system attempts to sync analytics data Then it retries up to 3 times with exponential backoff, logs each failure, and resumes normal operation without crashing

Analytics Dashboard Visualization

Given aggregated usage statistics are available When a support lead views the historical usage analytics dashboard Then frequency of use, success rates, and user ratings are displayed correctly with time-stamped data accuracy within a 1-minute delay

Dynamic Template Preview

"As a support agent, I want a dynamic preview of workflow templates so that I can evaluate their structure and fit before applying them to my ticket flow."

Description

Provide a live preview of selected templates, showcasing key steps, automation triggers, and expected outcomes before implementation. This requirement enables users to assess a template’s suitability at a glance, reducing trial-and-error and ensuring alignment with their support processes. The preview integrates with the template editor and AI filters, offering contextual highlights and usage insights for informed decision-making.

Acceptance Criteria

Template Steps Visibility

Given a user selects a template from the Smart Template Finder, When the Dynamic Template Preview is displayed, Then the preview lists all key steps of the template in the correct order with step titles.

Automation Triggers Display

Given a user views the template preview, Then each automation trigger associated with the template is shown with a brief description and trigger condition; And the user can hover or click to view detailed logic.

Expected Outcomes Overview

Given a template preview is open, Then the preview includes a summary section of expected outcomes, metrics impacted, and typical resolution time improvements based on historical data.

Contextual Usage Insights

Given a user is previewing a template, Then the preview shows AI-filtered insights such as industry usage frequency, average user rating, and number of implementations in similar use cases.

Live Preview Integration with Editor

Given a user modifies template parameters in the editor, When changes are made, Then the Dynamic Template Preview updates in real-time to reflect new key steps, triggers, and outcomes without requiring manual refresh.

User-defined Custom Filters

"As a support lead, I want to define custom filter criteria so that I can tailor template suggestions to specific scenarios and team preferences."

Description

Allow users to create, save, and apply their own filter criteria—such as industry, ticket priority, channel, and custom tags—to tailor template recommendations to their unique support needs. This requirement empowers non-technical users to refine recommendation results without coding, enhancing personalization and flexibility. The custom filters UI integrates seamlessly with the Smart Template Finder interface for easy configuration and reuse.

Acceptance Criteria

Defining a New Custom Filter

Given a support lead is on the Smart Template Finder, when they specify filter criteria for industry, ticket priority, and channel and click 'Save Filter', then the new filter should appear in the 'My Filters' list and be selectable for future recommendation searches.

Applying an Existing Custom Filter

Given a saved custom filter in 'My Filters', when a support lead selects it and runs a template search, then only templates matching the filter criteria should be displayed in the results.

Editing a Saved Custom Filter

Given an existing custom filter, when a support lead updates its criteria (e.g., changes ticket priority) and clicks 'Save', then the filter should update in the 'My Filters' list with the new criteria and reflect in subsequent template searches.

Deleting a Custom Filter

Given a custom filter in 'My Filters', when a support lead clicks 'Delete' and confirms, then the filter should be removed from 'My Filters' and no longer available for selection.

Handling Invalid Filter Configurations

Given a support lead inputs conflicting or invalid criteria (e.g., selects unsupported custom tags), when they attempt to save the filter, then an inline validation message should display specifying the issue and prevent saving until corrected.

Persisting Custom Filters Across Sessions

Given a saved custom filter, when the support lead logs out and logs back in, then the filter should still appear in 'My Filters' and be available for selection.

Multi-criteria Relevance Scoring

"As a support supervisor, I want to see a relevance score for each template so that I can choose the best workflow based on multiple factors."

Description

Develop a scoring engine that evaluates templates across multiple dimensions—relevance, complexity, resource requirement, and success history—and assigns a composite relevance score. This requirement provides transparent rationale behind each recommendation, enabling users to compare templates objectively. The scoring engine pulls data from AI filters, usage analytics, and user-defined filters, recalculating scores in real time as criteria change.

Acceptance Criteria

Composite Score Calculation

Given templates each with predefined metrics for relevance, complexity, resource requirement, and success history, When the scoring engine runs, Then it computes a composite score equal to the weighted sum of each metric according to the configured weighting schema.

Real-time Score Update on Filter Adjustment

Given a user modifies any AI-driven or user-defined filter, When the filter change is submitted, Then the system recalculates and updates all template relevance scores within 500 milliseconds.

Transparency of Scoring Rationale Display

Given a recommended template is presented to the user, When the user views the recommendation details, Then the system displays a breakdown of the composite score showing individual metric scores and their respective weights.

Handling Incomplete Analytics Data

Given one or more scoring dimensions lack data for a template, When the engine calculates scores, Then it assigns a neutral default value for missing metrics and flags the template as having incomplete data without halting score computation.

Performance Under High Load

Given a dataset of 10,000 templates and multiple simultaneous filtering changes, When the engine processes score recalculations, Then it completes all updates within 2 seconds while maintaining system stability.

Feedback-driven Model Refinement

"As a support lead, I want to provide feedback on recommended templates so that the system learns and improves future recommendations."

Description

Implement a feedback mechanism that captures user ratings and improvement suggestions post-implementation, feeding this data back into the AI model and analytics engine. This requirement ensures continuous learning and enhancement of recommendation quality over time. The feedback loop integrates with the template execution tracker and analytics dashboard, automating data collection and model retraining processes.

Acceptance Criteria

Post-Implementation Feedback Capture

Given a user completes a template implementation, When the feedback prompt appears, Then the user can rate the template from 1 to 5 and submit improvement suggestions, And the system stores the feedback with template ID, user ID, and timestamp.

Automatic Feedback Data Collection

Given multiple feedback entries are submitted, When the feedback ingestion process runs, Then all new feedback records are automatically ingested into the analytics engine within 5 minutes of submission.

AI Model Retraining Trigger

Given the feedback dataset reaches 100 new entries or the average rating for a template changes by ±10%, When the threshold is met, Then the system triggers the model retraining job and logs the initiation event with details.

Analytics Dashboard Feedback Visualization

Given feedback data has been ingested, When an admin views the analytics dashboard, Then they see updated charts showing average ratings, suggestion trends, and retraining history for each template.

User Feedback Confirmation Notification

Given a user submits feedback, When the submission is successful, Then the user receives an in-app confirmation message and email summarizing the feedback details within 1 minute.

Rapid Preview Mode

Allows teams to simulate an imported workflow blueprint end-to-end in a sandbox environment before deployment. By visualizing each step and outcome in real time, users can validate processes instantly and deploy with confidence.

Requirements

Sandbox Environment Initialization

"As a support lead, I want to initialize a sandbox instance of my workflow blueprint automatically so that I can test changes safely without impacting live operations."

Description

Automatically clone a workflow blueprint into an isolated sandbox environment with mirrored configurations and data schemas to enable safe end-to-end simulation without impacting production systems.

Acceptance Criteria

Cloning Workflow Blueprint into Sandbox

Given a user selects a workflow blueprint and initiates sandbox creation, when the process completes, then the sandbox must contain an exact replica of the blueprint with identical steps, metadata, and version number within 60 seconds.

Data Schema Synchronization Verification

Given the sandbox environment is initialized, then all data schemas from production—including custom fields and relationships—must be present and pass a schema validation check with zero errors.

Configuration Mirroring Assessment

Given sandbox creation is complete, then all workflow configuration settings (permissions, triggers, and integrations) must match the source blueprint configuration and be reported as 'in sync' in the audit log.

Isolation from Production Environment

Given sandbox initialization, then any operations performed within the sandbox (ticket creation or modification) must not alter or reflect in production systems, verified by a zero-change confirmation in production logs for the associated workflow.

Performance Threshold during Initialization

Given up to 10 sandbox creation requests are queued simultaneously, when all requests are processed, then each sandbox must be initialized without failures and within 120 seconds in at least 99% of attempts.

Real-Time Step-by-Step Visualization

"As a support lead, I want to see each step and its outcomes in real time so that I can quickly identify logic errors or bottlenecks in my workflows."

Description

Provide an interactive view that displays each workflow step’s execution status, input/output data, and decision branches in real time, allowing users to monitor and inspect the flow dynamically during simulation.

Acceptance Criteria

Live Execution Status Display

Given a user initiates a workflow simulation in sandbox When each step begins execution Then the step’s status indicator updates to "Running" within 1 second And when execution completes Then the status updates to "Success" or "Failed" accordingly

Input and Output Data Inspection

Given a simulated step is selected in the visualization panel When the user clicks to view details Then the input and output payloads are displayed in a structured JSON viewer And data fields match expected values from the workflow definition

Decision Branch Visualization

Given a workflow step includes a conditional branch When the simulation reaches the decision point Then both potential branches are highlighted And the path taken is clearly distinguished from alternate branches

Error Highlighting on Failure

Given a step fails during simulation When the failure occurs Then the step is highlighted in red And an error message detailing the cause is displayed

Performance Real-Time Update

Given a complex workflow with multiple concurrent steps When the simulation runs Then the visualization updates all step statuses in real time without more than 500ms latency And UI remains responsive throughout execution

Dynamic Input Injection

"As a support lead, I want to change test inputs on the fly during simulation so that I can validate how the workflow handles diverse data conditions without needing multiple run setups."

Description

Allow users to modify or inject test data such as ticket attributes and user variables at runtime during simulation to evaluate workflow behavior under different scenarios without restarting the preview.

Acceptance Criteria

Modify Ticket Subject During Preview

Given a workflow is loaded in Rapid Preview Mode When the user updates the ticket subject field with valid text at runtime Then the preview simulation immediately reflects the new subject in all subsequent steps without requiring a restart

Inject Custom User Variable at Runtime

Given a user variable placeholder exists in the workflow When the user injects a new variable value during preview Then all workflow nodes referencing that variable use the updated value for processing and display

Real-Time Update Reflection in Workflow Execution

Given the simulation is paused at step N When the user modifies any injected input data Then the simulation resumes from step N+1 using the updated data and shows the change effects immediately

Handle Invalid Input Gracefully

Given the user injects data that violates defined attribute constraints When the invalid data is submitted at runtime Then the system displays a clear validation error message and prevents simulation continuation until corrected

Persist Injected Data for Subsequent Steps

Given the user changes multiple input values during a single preview session When navigating forward through workflow steps Then the last injected values are retained and applied consistently across all remaining steps without data loss

Validation and Error Feedback

"As a support lead, I want the system to highlight configuration errors and suggest fixes during preview so that I can correct issues early and avoid deployment failures."

Description

Implement validation checks for common misconfigurations and display actionable error messages or warnings within the preview interface to guide users in resolving issues before deployment.

Acceptance Criteria

Missing Required Field Validation

Given a workflow with a form step missing a required field, when the user initiates Rapid Preview Mode, then the system highlights the missing field, displays an inline error stating 'Field X is required,' and prevents further simulation until resolved.

Invalid Conditional Branch Configuration

Given a workflow with a conditional branch referencing a non-existent variable, when the user initiates Rapid Preview Mode, then an error message 'Undefined variable in condition' is shown, and the faulty condition step is highlighted in preview.

Unsupported Action Parameter Detection

Given a workflow action configured with an unsupported parameter value, when the user runs the preview, then a warning 'Unsupported parameter: Y' appears next to the action, and the preview continues for other valid steps.

Circular Workflow Loop Warning

Given a workflow blueprint containing a loop that references an earlier step creating potential infinite recursion, when previewing, then the system detects the loop, warns 'Circular loop detected,' identifies the loop steps, and halts simulation.

Preview Execution Failure Notification

Given a scenario where preview execution encounters an unexpected exception, when executing Rapid Preview Mode, then a modal appears with 'Preview execution failed: Z error occurred,' includes a 'Report Issue' button, and logs the error for diagnostics.

Preview Result Export

"As a support lead, I want to export a summary of my preview session so that I can share results with stakeholders and maintain a record of workflow validations."

Description

Enable users to export a detailed report of simulation results, including execution logs, data transformations, and validation outcomes, in PDF or JSON format for documentation and review.

Acceptance Criteria

Export PDF report from completed simulation

Given a completed workflow simulation in sandbox mode, when the user selects "Export as PDF", then the system generates and downloads a PDF file containing execution logs, data transformation details, and validation outcomes; file name includes workflow name and timestamp

Export JSON report with valid schema

Given a completed workflow simulation, when the user selects "Export as JSON", then the system generates and downloads a JSON file that adheres to the defined export schema and includes fields for simulationId, steps, logs, transformations, and validationResults

Handle large export sizes without timeout

Given a simulation with extensive logs and data (report size >50MB), when the user initiates an export (PDF or JSON), then the export completes within 30 seconds without errors or timeouts

Access control for export feature

Given a user without export permissions, when viewing the simulation results page, then the "Export" option is hidden or disabled and attempting direct API calls returns a 403 Forbidden error

Error handling on export failure

Given a server-side error during export processing, when the user initiates an export, then the system displays a descriptive error message with an option to retry or report the issue

Template Customizer

Provides an intuitive, no-code editor for tweaking imported templates—adjusting fields, branching logic, and automations with drag-and-drop ease. Custom variants can be saved as new templates, empowering teams to adapt blueprints precisely to their support needs.

Requirements

Drag-and-Drop Editor

"As a support lead, I want to reposition and organize template elements with drag-and-drop ease so that I can tailor workflows quickly without writing code."

Description

An intuitive visual canvas enabling users to add, remove, and rearrange template fields, branching points, and automation steps via drag-and-drop interactions, seamlessly integrating with the existing template structure to accelerate customization and reduce errors.

Acceptance Criteria

Adding a New Field to the Visual Canvas

Given the user has opened an existing template in the drag-and-drop editor When the user drags a “Text Input” field from the component panel onto an empty area of the canvas Then the field is added at the drop location, is selectable, and appears in the template’s underlying JSON without errors

Removing an Existing Field from the Template

Given the canvas contains at least one field When the user drags a field to the delete zone or presses the delete key with the field selected Then the field is removed from the canvas and the template’s structure updates to exclude the field without affecting other elements

Reordering Fields via Drag-and-Drop

Given multiple fields appear on the canvas in a sequence When the user drags Field A and drops it before Field B Then Field A is repositioned correctly in the visual sequence and the template’s execution order updates accordingly

Connecting Branching Logic Nodes

Given two branching nodes exist on the canvas When the user drags a connector from the output port of Node X to the input port of Node Y Then a visual connection appears, the branching logic is saved in the template’s configuration, and the connection is validated in preview mode

Integrating Automation Step into Template Flow

Given the automation action list is visible When the user drags a “Send Email” automation step onto a target field Then the automation icon attaches to the field, the template’s JSON includes the automation trigger, and the step runs successfully in test execution

Field Configuration Panel

"As a support lead, I want to adjust field properties like labels and validations so that the templates capture accurate and relevant information for each support workflow."

Description

A dynamic side panel offering property settings for each template field, including labels, default values, validations, and conditional visibility rules, ensuring precise control over user input and data collection within customized templates.

Acceptance Criteria

Accessing the Field Configuration Panel

Given the user is editing a template and selects a field When the user clicks the configuration icon for that field Then the side panel appears displaying property settings including label, default value, validation options, and conditional visibility rules

Updating Field Label and Default Value

Given the field configuration panel is open When the user edits the label and default value fields and clicks 'Save' Then the field's label and default value update in the template preview and persist after refreshing

Applying Validation Rules to a Field

Given the field configuration panel is open When the user sets validation rules (e.g., required, max length) and saves Then validation settings are enforced on form submission and invalid input triggers appropriate error messages

Configuring Conditional Visibility for a Field

Given the field configuration panel is open When the user defines visibility conditions (e.g., show if another field equals a value) and saves Then the field's visibility toggles in the template based on defined conditions during preview and live use

Saving Customized Field Configuration

Given the user has made changes in the field configuration panel When the user saves the template variant Then the customized field settings are included in the new template variant and are available for future use

Branching Logic Builder

"As a support lead, I want to set up conditional branches based on ticket attributes so that the workflow follows appropriate paths for different scenarios."

Description

A visual rule engine allowing users to define conditional pathways within templates by specifying ‘if-then’ criteria, enabling complex, context-sensitive support flows that adapt to user responses and ticket data.

Acceptance Criteria

Adding a Simple If-Then Branch

Given the user is editing a template in the Branching Logic Builder When the user adds a new branch with a single ‘If ticket priority is High’ condition and defines the ‘Then’ action as ‘Route to Escalation’, Then the branch is displayed in the visual flow, the condition is correctly parsed, and the action is set as specified.

Configuring Multi-Condition Branch

Given the user has opened an existing branch in the Branching Logic Builder When the user adds two conditions using ‘AND’ logic (‘If ticket type is Bug’ AND ‘Customer satisfaction < 3’) and sets the ‘Then’ action to ‘Trigger Follow-Up Survey’, Then both conditions are applied correctly, the ‘AND’ operator is represented visually, and the follow-up survey automation is linked.

Previewing Branch Execution in Template

Given the user has defined at least one branch in the Branching Logic Builder When the user clicks ‘Preview Flow’ with sample ticket data matching a branch condition, Then the template simulation highlights the branch path taken and displays the resulting actions for review.

Saving Custom Branch Variant

Given the user has configured one or more branches in the Branching Logic Builder When the user clicks ‘Save as New Template’, Then the system creates a new template variant including the defined branches, lists it under the user’s templates, and allows it to be selected for future use.

Handling Invalid Logic Input

Given the user attempts to add a branch without specifying a condition or action When the user clicks ‘Apply’ or ‘Save’, Then the system prevents saving, highlights the missing fields, and displays an inline error message describing the issue.

Automation Step Integration

"As a support lead, I want to insert automated actions into my template so that routine tasks execute without manual intervention, reducing resolution times."

Description

A module that lets users embed pre-built automation actions (e.g., sending notifications, updating ticket status, or triggering external APIs) directly into the template flow, streamlining repetitive tasks and ensuring consistent process execution.

Acceptance Criteria

Drag-and-Drop Automation Actions

Given the user opens the template flow builder, when they drag a pre-built automation action into a flow step, then the action appears at the intended position with its title, icon, and placeholder displayed.

Configure Action Parameters

Given an automation action is added to the flow, when the user clicks the action card to edit, then they can view and modify all configurable parameters and see real-time validation errors if inputs are missing or invalid.

Save and Persist Automation Integration

Given the template includes embedded automation actions, when the user saves and reopens the template, then all automation steps remain intact with their configured parameters and ordering unchanged.

Execute Automation Actions in Flow

Given a support workflow runs with an embedded automation step, when the flow reaches that step, then the specified action executes successfully (notification sent, ticket updated, or external API called) and logs a success entry.

Handle Automation Execution Failures

Given an automation action fails during flow execution, when the error occurs, then the system logs the error details, retries up to two times, and surfaces an alert to the user with actionable steps.

Template Variant Management

"As a support lead, I want to save my customized template as a new variant so that I can reuse and share tailored workflows with my team."

Description

Functionality to save customized templates as new variants, tag them for easy retrieval, organize them into folders, and maintain version history, empowering teams to iterate on blueprints while preserving original designs.

Acceptance Criteria

Saving a New Template Variant

Given a user has customized a template When the user clicks 'Save as Variant' and enters a unique variant name Then the system saves the new variant, displays it in the template list, and preserves the original template without changes

Tagging a Template Variant for Easy Retrieval

Given a user views a saved template variant When the user adds one or more tags and confirms Then the tags are associated with the variant and it appears in search/filter results for those tags

Organizing Variants into Folders

Given a user has one or more folders created When the user drags or selects a variant into a folder Then the variant is assigned to that folder and displays under that folder in the UI

Accessing Version History of a Variant

Given a template variant has multiple saved versions When the user opens the version history panel for that variant Then the panel lists all versions with timestamps, author names, and change notes

Reverting to a Previous Version

Given a user is viewing the version history of a variant When the user selects an earlier version and clicks 'Revert' Then the variant’s current configuration updates to match the selected version and a new version entry is created

Real-Time Preview & Testing

"As a support lead, I want to preview and test my customized template in real time so that I can verify its behavior and catch errors before using it with customers."

Description

A built-in sandbox environment that renders the customized template live, allowing users to simulate user interactions, validate branching logic, and test automations end-to-end before deploying to production.

Acceptance Criteria

Instant Template Rendering in Sandbox

Given a user modifies any element of the template in the no-code editor, when the change is saved, then the sandbox preview updates within 2 seconds reflecting the exact layout, fields, and styling

Branching Logic Simulation

Given a template with configured branching logic, when a simulated user selects any decision path in the sandbox preview, then the subsequent fields and flows displayed must match the configured logic for every possible branch

Field Validation Testing

Given required fields and custom validation rules in a template, when a simulated submission is attempted without meeting those rules in the sandbox, then the preview must display the appropriate validation messages and prevent submission

Sandbox Automation Execution

Given automations (e.g., notifications, task creation) configured in the template, when a simulated end-to-end interaction is completed in the sandbox, then each automation must execute in sequence, and logs of execution must be visible in the sandbox console

Template Export Readiness Check

Given a fully previewed and tested template in the sandbox, when the user clicks export or deploy, then the system must validate no errors exist, package all template assets, and generate a deployable file confirming readiness

Team Sync & Share

Enables seamless sharing and collaborative editing of templates across departments and support tiers. Permission controls ensure the right stakeholders can view, edit, or publish blueprints, fostering alignment and consistency throughout the organization.

Requirements

Template Access Control

"As a support manager, I want to assign specific access levels to team members for each template so that only authorized personnel can view, edit, or publish blueprints, ensuring security and consistency."

Description

Enable granular permission settings for templates, allowing administrators to grant view, edit, or publish rights to specific users or groups. Integrates with existing role-based access control to ensure only authorized stakeholders can modify or distribute support blueprints, reducing risk of unauthorized changes and maintaining template integrity across departments.

Acceptance Criteria

Grant View Permission to User

Given an administrator and a template exist When the administrator grants view permission to User A on the template Then User A can view but not edit or publish the template

Assign Edit Permission to Group

Given an administrator and Group Sales exist When the administrator assigns edit permission to Group Sales on the blueprint Then all members of Group Sales can edit but not publish the blueprint

Restrict Publish Permission to Authorized Roles

Given a support agent without publish rights When the agent attempts to publish a template Then the system prevents the action and displays an authorization error

Enforce External RBAC Role Mappings

Given integration with external RBAC When a role from the external system is mapped in PulseDesk Then the mapped role inherits the corresponding template permissions defined externally

Log Permission Changes to Audit Trail

Given a permission change occurs When any administrator updates template permissions Then an audit log entry with admin ID, target user or group, permission type, template ID, and timestamp is recorded

Real-time Collaborative Editing

"As a support team member, I want to edit templates concurrently with colleagues so that we can collaboratively build and refine blueprints without overwriting each other's changes."

Description

Implement real-time, multi-user editing capabilities on template blueprints, with live cursor tracking, change highlights, and instant synchronization across sessions. This feature allows support agents from different tiers and departments to collaborate simultaneously, improving alignment and reducing duplication of efforts.

Acceptance Criteria

Concurrent Editing by Multiple Support Agents

Given two or more authenticated support agents open the same template blueprint When any agent updates content or formatting in a template section Then all other agents see those updates reflected in real time (within 1 second) without requiring a page reload

Live Cursor Tracking Across Sessions

Given an agent’s cursor position and text selection When the agent moves or types in the template Then other agents’ views display the cursor position and selection range in real time, annotated with the agent’s name or identifier

Change Highlight Visibility

Given a modification (insertion, deletion, formatting) by an agent When the change is made Then the system highlights the change in a unique color for that agent and displays a tooltip with the agent’s name, and logs the change in the revision history

Offline Edit Synchronization upon Reconnect

Given an agent loses network connectivity and continues editing the template offline When the agent’s connection is restored Then the system merges offline edits into the live blueprint, resolves non-conflicting changes automatically, and notifies all active agents of the synchronized edits

Edit Conflict Resolution Workflow

Given two agents make conflicting edits to the same text segment When the system detects a conflict Then the system presents a side-by-side diff with both versions and prompts the initiating agent to choose which version to apply or to merge changes manually

Version History & Rollback

"As a team lead, I want to view past versions of a template and revert to an earlier state so that I can recover from unintended edits or mistakes."

Description

Provide a comprehensive version history for each template with timestamps and author annotations, allowing users to review past versions and restore to any previous state. This safeguard ensures traceability of changes, facilitates auditing, and enables quick rollback in case of errors.

Acceptance Criteria

Viewing Template Version History

Given a user is on a template detail page When they click the 'Version History' tab Then the system lists all template versions with timestamps, author names, and version numbers sorted from newest to oldest

Previewing Previous Template Versions

Given a user views the version history When they select a specific version and click 'Preview' Then the system displays the template content exactly as it existed in that version without modifying the current template

Restoring Template to Previous Version

Given a user selects a past version in the history When they click 'Restore' and confirm the action Then the system creates a new version identical to the selected version, sets it as the current version, and logs the restore action

Documenting Version Change Audit Logs

Given a user views any version entry When they expand the 'Change Details' section Then the system shows a clear diff of added and removed content, a change summary, and a link to the author’s profile

Enforcing Rollback Permissions

Given a user without rollback permissions views the version history When they attempt to restore a version Then the 'Restore' button is disabled and a tooltip explains that they lack necessary permissions

Template Publishing Workflow

"As a department head, I want to review and approve template changes before they are published so that only vetted blueprints are used across the organization."

Description

Design a structured approval and publishing workflow enabling draft, review, and publish stages for templates. Notifications and review queues guide stakeholders through approvals before a blueprint goes live, ensuring quality control and cross-team alignment.

Acceptance Criteria

Draft Template Submission

Given a support lead has created a template draft, When they submit it for review, Then the system moves it to the review queue and notifies assigned reviewers.

Reviewer Approval Process

Given a template is in review state and a reviewer is assigned, When the reviewer approves the template, Then the template status updates to "Approved" and notification is sent to the original author.

Reviewer Rejection and Feedback

Given a reviewer rejects a template in review, When the reviewer provides feedback, Then the template returns to the draft state with annotated comments and author is notified.

Publishing Approved Template

Given a template has the "Approved" status, When a publisher clicks "Publish", Then the template status updates to "Published", is available in the shared library, and stakeholders receive a publication notification.

Permission-Controlled Editing

Given a user with "Editor" permissions accesses the template library, When they open a draft or approved template, Then they can edit or request review based on their permission level, and changes are tracked with version history.

Department-Specific Template Libraries

"As a support agent, I want to filter templates by my department so that I can access relevant blueprints quickly and maintain consistency in my team's processes."

Description

Build separate template libraries for different departments and support tiers, with customizable filtering and tagging. Users can quickly locate relevant blueprints, maintain departmental autonomy, and share best practices across teams.

Acceptance Criteria

Accessing Department-Specific Library

Given a user assigned to the Sales department, When they navigate to the template libraries page, Then only Sales department templates appear in the list

Filtering Templates by Tags

Given a user is viewing the Marketing library, When they apply the "Urgent" tag filter, Then only templates tagged "Urgent" from the Marketing library are displayed

Sharing Templates Across Departments

Given a template in the Support tier library, When an admin selects "Share with Engineering", Then the template appears in the Engineering department’s library with read-only access

Permission Control Enforcement

Given a user with editor permissions, When they attempt to edit a department template, Then they can modify and save changes; Given a user without edit permissions, When they attempt the same action, Then they receive a "Permission Denied" error

Template Search Performance

Given a library containing 500+ templates, When a user searches by keyword, Then search results are returned within 2 seconds and include relevant departmental templates only

Template Analytics

Delivers metrics on template utilization, average resolution time improvements, and CSAT impact per blueprint. Teams gain actionable insights into which workflows drive the best outcomes, guiding continuous optimization and resource allocation.

Requirements

Interactive Template Dashboard

"As a support lead, I want an interactive dashboard of template performance metrics so that I can monitor overall workflow effectiveness at a glance and make data-driven decisions."

Description

An interactive dashboard that consolidates key metrics for all templates, including utilization rates, average resolution time improvements, and CSAT impact per blueprint. Users can apply filters by date range, team, or template category and compare performance across dimensions. The dashboard integrates seamlessly into PulseDesk’s analytics module, enabling support leads to monitor template effectiveness in real time and quickly identify high- and low-performing workflows.

Acceptance Criteria

View Overall Template Metrics

Given the support lead navigates to the Interactive Template Dashboard, when the dashboard loads, then utilization rates, average resolution time improvements, and CSAT impact for all templates are displayed in clearly labeled charts and tables.

Filter Templates by Date Range

Given the support lead has selected a custom date range filter, when the filter is applied, then the dashboard refreshes to show only template metrics within the selected dates and updates all visualizations accordingly.

Filter Templates by Team

Given the support lead chooses one or more teams from the team filter dropdown, when the filter is applied, then only metrics for templates used by the selected teams are displayed and all statistics recalculate accurately.

Filter Templates by Category

Given the support lead selects a template category filter, when the category is applied, then the dashboard displays metrics for templates in that category and excludes metrics for other categories.

Compare Template Performance

Given the support lead selects two or more templates to compare, when the comparison view is activated, then side-by-side metrics for utilization, resolution time improvement, and CSAT impact are displayed and any differences are clearly highlighted.

Drill-Down Template Analysis

"As an analytics user, I want to drill down into template-specific metrics so that I can understand which workflows are driving the best outcomes and where adjustments are needed."

Description

A feature that enables users to click into individual templates from the dashboard and view detailed analytics, such as time-series trends, usage by agent or team, and correlation between template usage and CSAT feedback. Includes visualizations like line charts and heatmaps to highlight patterns. This component integrates directly with the analytics data store to provide granular insights for continuous optimization of support blueprints.

Acceptance Criteria

Access Template Analytics

Given a user is on the Template Analytics dashboard, When they click on a specific template card, Then the system navigates to the Drill-Down page for that template within 2 seconds.

View Time-Series Trends

Given the Drill-Down page is open, When the user selects a date range filter, Then a line chart displays the template’s usage metrics over that range with daily data points and no missing intervals.

Segmentation by Agent or Team

Given the Drill-Down page is open, When the user applies an agent or team filter, Then the system updates the analytics to show only usage data for the selected agent(s) or team(s) and recalculates summary statistics accordingly.

Correlation with CSAT Feedback

Given the Drill-Down page is open, When the user enables CSAT correlation analysis, Then the system computes and displays a correlation coefficient between template usage frequency and CSAT scores with an explanatory tooltip.

Visual Heatmap Interaction

Given the Drill-Down page is open, When the user hovers over any cell in the heatmap, Then a tooltip appears showing the exact usage count and corresponding time period without visual distortion or delay.

Automated Report Scheduling

"As a support manager, I want to schedule automated delivery of template performance reports so that I can keep my team and stakeholders informed without manual effort."

Description

A scheduling system that allows users to configure and automate periodic generation and distribution of template analytics reports. Reports can be delivered via email or exported to CSV/PDF at daily, weekly, or monthly intervals. Users can select specific metrics and templates to include. This capability enhances stakeholder visibility and reduces manual reporting overhead.

Acceptance Criteria

Daily Automated Email Report Generation

Given a support manager has scheduled a daily email report, When the scheduled time arrives, Then the system generates the report including selected metrics and templates, and sends it via email to the configured recipients.

Weekly CSV Export Scheduling

Given the user selects weekly interval and opts for CSV format, When the next weekly schedule triggers, Then the system exports the report in CSV format and attaches it to an email or makes it available for download.

Monthly PDF Report Distribution

Given the user schedules a monthly PDF report for selected templates, When the monthly schedule triggers, Then the system generates the PDF including correct metrics for those templates and sends it to stakeholders.

Custom Metric Selection in Scheduled Reports

Given the user has selected specific metrics for the report, When the scheduled report is generated, Then the report includes only those metrics and excludes others.

Editing an Existing Report Schedule

Given the user navigates to an existing schedule and modifies the delivery time or recipients, When saved, Then the updated schedule triggers the report at the new time or to the new recipients.

Disabling a Scheduled Report

Given the user disables a scheduled report, When the next scheduled time arrives, Then no report is generated or sent.

Threshold-Based Alerts

"As a support lead, I want to receive alerts when template performance metrics fall outside acceptable ranges so that I can take immediate corrective action."

Description

A notification mechanism that triggers real-time alerts when template metrics cross predefined thresholds (e.g., resolution time exceeds target, CSAT drops below a set value, or utilization falls under a minimum percentage). Alerts can be configured for individual templates or overall system performance and sent via in-app notifications or email. This feature proactively notifies teams of potential issues.

Acceptance Criteria

Resolution Time Exceeds Template Threshold

Given a template has a resolution time threshold of X hours, When the average resolution time for that template exceeds X hours in the past 24 hours, Then the system generates an alert including template name, threshold value, current average resolution time, and timestamp; And the alert is visible in the in-app notification center and sent via email within 2 minutes.

CSAT Drops Below Template Threshold

Given a template has a CSAT threshold of Y percent, When the average CSAT over the last 20 resolved tickets for that template falls below Y percent, Then the system generates an alert including template name, threshold value, current average CSAT, and timestamp; And the alert is visible in the in-app notification center and sent via email within 2 minutes.

Template Utilization Falls Below Minimum Percentage

Given a template has a utilization threshold of Z percent, When the utilization (tickets assigned to this template divided by total tickets) over the last 7 days falls below Z percent, Then the system generates an alert including template name, threshold value, current utilization percentage, and timestamp; And the alert is visible in the in-app notification center and sent via email within 2 minutes.

Threshold Configuration for Individual Templates

Given a user configures a threshold for resolution time, CSAT, or utilization for a specific template, When the configuration is saved, Then the system persists the threshold settings and reflects them in the threshold management UI with correct values; And the thresholds are enforced for subsequent ticket data evaluation.

Aggregated Alerts for Multiple Templates

Given multiple templates cross their configured thresholds within the same hour, When these thresholds are breached, Then the system aggregates the individual alerts into a single summary notification listing all affected templates and metrics; And this summary is visible in-app and sent via email within 5 minutes.

Custom Metric Builder

"As an admin user, I want to define custom metrics for template performance so that I can measure and optimize the specific KPIs that matter most to my organization."

Description

A configuration interface that enables administrators to define and track custom metrics for template analytics, such as first-response time, escalation rate, or resolution quality score. Users can create formula-based metrics by combining existing data points and set calculation parameters. The new metrics integrate into dashboards, reports, and alerts for a tailored analytics experience.

Acceptance Criteria

Admin Defines a New Custom Metric

Given the administrator is on the Custom Metric Builder page When they select multiple existing data points and define a valid formula And they provide a unique name and save the metric Then the system adds the new custom metric to the metrics list with the correct formula And the metric is available for inclusion in dashboards, reports, and alerts.

Admin Updates Existing Custom Metric Parameters

Given an existing custom metric with a defined formula When the administrator modifies the formula or calculation parameters and saves changes Then the system updates the metric's formula in the metrics list And the updated metric is immediately reflected in all dashboards, reports, and alert configurations.

Admin Views Custom Metrics List

Given multiple custom metrics exist When the administrator navigates to the Custom Metrics List view Then the system displays each metric's name, formula summary, creation date, last modified date, and status in a tabular format And metrics can be sorted and searched by name and date.

Admin Deletes a Custom Metric

Given an existing custom metric is listed When the administrator selects the delete action for a metric and confirms the deletion Then the system removes the metric from the Custom Metrics List And the metric is no longer available in dashboards, reports, or alerts configurations.

Custom Metric Integrates into Dashboard and Alerts

Given a custom metric has an associated alert threshold When metric values meet or exceed the configured threshold during processing Then the system generates an alert notification in the Alerts panel And the dashboard displays the real-time metric value and alert status.

Version Vault

Maintains a complete history of template updates and customizations, allowing users to compare versions, restore previous states, or branch off new blueprints. This ensures governance, auditability, and the ability to experiment without risk.

Requirements

Version Recording & Storage

"As a support lead, I want every template update to be automatically recorded with metadata so that I can track who made changes and when for governance and accountability."

Description

Automatically record and securely store each change made to support templates and workflows, capturing metadata such as author, timestamp, and change description. Ensure a comprehensive history of updates for traceability and governance. Integrate with PulseDesk’s database to enable seamless retrieval and management of historical versions without impacting live operations.

Acceptance Criteria

Recording Template Change Metadata

Given a user with edit permissions modifies a support template and submits their changes, when the change is saved, then the system creates a new version record in the version vault containing the updated template, author ID, timestamp, and change description, and the record is persisted in the database.

Retrieving a Historical Template Version

Given a user with view permissions is on the version history page, when they select a version from the list, then the system retrieves the version data and displays it accurately in a read-only view, including template content and metadata.

Restoring a Template to a Previous Version

Given a user selects an earlier version and confirms a restore action, when the restore is executed, then the system replaces the current template state with the selected version, updates metadata with restoration details (restorer ID and restore timestamp), and logs the restoration as a new version entry.

Branching a New Blueprint from an Earlier Version

Given a user views an earlier version in the history, when they choose to branch a new blueprint from that version, then the system creates a new template branch with the selected version as its base, assigns a unique identifier to the branch, and records the branching action and metadata.

Non-Disruptive Version Storage under High Load

Given the system is handling peak live ticketing operations, when a version storage operation occurs, then the process completes within 2 seconds without error and does not increase live operation latency by more than 1%.

Version Comparison Viewer

"As a support lead, I want to compare two versions of a template side by side so that I can quickly identify changes and decide if I need to revert or branch from a specific version."

Description

Provide an interactive interface to select and visually compare two versions of a template side by side, highlighting added, removed, and modified elements. Support both UI and logic differences for granular insight. Integrate within PulseDesk to enable rapid review of updates and informed decision-making on rollbacks or branching.

Acceptance Criteria

Comparing Template Versions in Builder Interface

Given a user has selected two template versions from Version Vault When the user opens the Version Comparison Viewer Then both versions are displayed side by side with synchronized scrolling and version metadata visible for each pane

Highlighting UI Changes between Versions

Given two template versions contain UI element updates When the comparison viewer renders the diff Then added elements are highlighted in green, removed elements in red, and modified elements in yellow, with tooltips describing each change

Highlighting Logic Changes between Versions

Given two template versions include workflow logic modifications When the comparison viewer switches to logic diff mode Then all added, removed, and altered conditions and actions are clearly marked, with a side-by-side code or rule comparison and change annotations

Initiating Rollback to Previous Version

Given a user identifies the need to revert to a previous template version When the user clicks the Rollback button on the older version Then a confirmation dialog appears, and upon confirmation the selected version replaces the current template while saving the former current version as a new version entry

Creating a New Branch from Older Template Version

Given a user wants to experiment without affecting the main template When the user clicks Branch Off on an older version Then the system creates a new branch with a copy of the selected version, assigns a unique branch name, and redirects the user to the builder on the new branch

Version Restore & Rollback

"As a support lead, I want to restore a previous template version in one click so that I can recover from mistakes and maintain consistent support flows."

Description

Allow users to restore any previous version of a template with a single click, reverting all changes to that state while creating a new version entry to preserve the rollback action. Ensure fast recovery from errors or undesired updates without manual reconstruction, maintaining continuity in support workflows.

Acceptance Criteria

Single-Click Restore to Previous Version

Given user is on the template version history page When the user clicks the restore button next to a specific version Then the template content reverts exactly to that version within 3 seconds

Rollback Action Creates New Version Entry

Given user confirmed a restore action When the system completes the rollback Then a new version entry labeled 'Restored to [version name] by [user]' is appended at the top of the version history with a timestamp

Concurrent Users Restoring Templates

Given two users initiate restore actions on different versions simultaneously When both actions complete Then each template is restored correctly without data conflicts or overwriting the other's changes

Restoration Preserves Workflow Integrity

Given the template includes linked workflows and automations When a version is restored Then all associated workflows and automations revert to their states in the selected version without requiring manual reconfiguration

Restoration from Older Branch

Given a user restores a version that branched off an earlier blueprint When the restore action finishes Then the system creates a new branch from that version and clearly marks it in the version history

Blueprint Branching

"As a support lead, I want to create a new blueprint branch from an existing template version so that I can test changes without impacting the production template."

Description

Enable branching from a selected template version to create a new isolated blueprint for experimentation or parallel development. Inherit full context of the base version, allow independent modifications without affecting the original, and manage branch metadata to support safe testing and iterative improvement of support workflows.

Acceptance Criteria

User Initiates Branch from Selected Template Version

Given a user is viewing a specific template version, when they click 'Branch', then a new blueprint is created inheriting all elements from the base version and presented in the blueprint list.

Independent Modification in Branch Without Affecting Original

Given a branch blueprint exists, when the user modifies nodes or configurations in the branch, then the base template remains unchanged and the version history only reflects changes in the branch.

Branch Metadata Management and Audit Trail

Given a branch blueprint has been created, when accessed, then the UI displays branch metadata including parent version ID, creation timestamp, and author, and all actions are logged for audit purposes.

Branch Restoration to Parent Version

Given a branch blueprint diverges significantly, when the user selects 'Restore from Parent', then the branch's content resets to match the base version without altering the base template or other branches.

Branch Merge Readiness Validation

Given a branch blueprint is ready for integration, when the user clicks 'Validate for Merge', then the system checks for conflicts with the parent template and displays a summary of changes and potential conflicts.

Audit Trail & Change Log

"As a compliance officer, I want to view an audit log of all versioning actions so that I can ensure governance and traceability of template changes."

Description

Generate a comprehensive audit trail logging all versioning activities—creations, comparisons, restores, and branches—with user and timestamp details. Present logs in a searchable, filterable interface within Version Vault to ensure compliance, transparency, and accountability for all template lifecycle events.

Acceptance Criteria

Template Creation Logging

Given a user creates a new template, when the creation is saved, then an audit trail entry is logged with the user’s ID, template ID, action type “create”, and timestamp.

Template Comparison Logging

Given a user compares two versions of a template, when the comparison view is generated, then an audit trail entry is logged with the user’s ID, versions compared, action type “compare”, and timestamp.

Template Restore Logging

Given a user restores a previous template version, when the restore action is confirmed, then an audit trail entry is logged with the user’s ID, original version, restored version, action type “restore”, and timestamp.

Template Branch Creation Logging

Given a user branches off a template version, when the branch is saved, then an audit trail entry is logged with the user’s ID, base version, new branch ID, action type “branch”, and timestamp.

Audit Log Search and Filter

Given a user accesses the audit log interface, when they apply search keywords and date filters, then the system displays matching entries sorted by timestamp within two seconds.

Sentiment Heatmap

Displays a real-time, color-coded matrix of customer sentiment across all live-chat channels, allowing support teams to instantly identify areas of praise or concern and allocate resources where they’re needed most.

Requirements

Real-time Sentiment Ingestion

"As a support manager, I want customer sentiment data ingested in real-time from all chat channels so that I can monitor and respond to negative feedback immediately."

Description

Enable continual, real-time collection and processing of customer sentiment data from all live-chat channels (e.g., web chat, mobile app, social media). This pipeline will parse incoming messages using the sentiment analysis engine, standardize the results, and feed them into the heatmap system with minimal latency. By integrating seamlessly with existing chat platforms and ensuring high throughput, support teams gain instant visibility into evolving customer moods, allowing for proactive engagement and timely intervention.

Acceptance Criteria

High-Volume Web Chat Ingestion

Given a web chat stream receiving 1000 user messages per minute, when messages arrive, then the sentiment ingestion pipeline parses and processes all messages with end-to-end latency not exceeding 2 seconds and no message loss.

Multi-Channel Integration

Given concurrent message streams from web chat, mobile app, and social media channels, when messages arrive simultaneously, then the system ingests and correctly tags and routes sentiment results for each channel with 99.9% accuracy.

Sentiment Data Standardization

Given sentiment analysis outputs in various formats, when the pipeline processes the results, then it normalizes sentiment scores to a standardized scale (e.g., -1 to +1) and attaches consistent metadata fields.

System Scalability Under Peak Load

Given a simulated peak load scenario of 5000 messages per minute, when the system is under load, then the ingestion pipeline scales horizontally and maintains throughput without performance degradation.

Error Handling for Malformed Messages

Given incoming messages with missing or invalid fields, when such messages are encountered, then the system logs the error, quarantines the message, and continues processing remaining messages without interruption.

Real-Time Heatmap Update Latency

Given processed sentiment data, when results are fed into the heatmap system, then the visual heatmap reflects updates within 3 seconds of message ingestion.

Dynamic Color-coded Heatmap Display

"As a support agent, I want a color-coded heatmap of sentiment across channels so that I can quickly identify and prioritize areas needing attention."

Description

Provide an interactive matrix-based visualization that assigns colors (e.g., green for positive, yellow for neutral, red for negative) to sentiment scores for each live-chat channel. The heatmap will auto-refresh at configurable intervals and support intuitive hover-over tooltips to show numeric values. This visual representation accelerates pattern recognition, enabling teams to quickly pinpoint areas of customer satisfaction or dissatisfaction without scanning individual tickets.

Acceptance Criteria

Real-time Heatmap Update During Peak Traffic

The heatmap auto-refreshes at the user-configured interval without page reload; sentiment scores update within 5 seconds of new data; all channel cells reflect updated colors correctly.

Auto-Refresh Interval Configuration

Users can select refresh intervals of 30s, 1m, and 5m; selected interval applies immediately upon saving; invalid interval inputs are rejected with an error message.

Tooltip Display on Hover

Hovering over a heatmap cell displays a tooltip showing the channel name and exact numeric sentiment score; the tooltip appears within 200ms of hover and disappears when the cursor leaves the cell.

Correct Color Assignment Based on Sentiment

Cells with sentiment scores ≥ 0.6 display green; scores between 0.4 and < 0.6 display yellow; scores < 0.4 display red; color thresholds can be updated in settings and apply instantly.

Dynamic Channel Addition and Removal

When a new live-chat channel is added, it appears in the heatmap at the next refresh; when a channel is removed, its cell disappears; the heatmap layout adjusts dynamically without overlap.

Sentiment Aggregation Engine

"As an analyst, I want sentiment data aggregated by time and channel so that I can track shifts in customer mood and identify root causes of satisfaction changes."

Description

Implement a back-end module that groups sentiment scores by channel, time interval, and predefined categories (e.g., product line), then calculates aggregate metrics such as average sentiment, trend direction, and sentiment volatility. This engine will support customizable aggregation windows (e.g., 5-minute, hourly, daily) to suit different analysis needs and ensure that the heatmap reflects accurate and context-rich sentiment insights for data-driven decision making.

Acceptance Criteria

Real-time Sentiment Aggregation by Channel

Given live chat data streams across channels, when the aggregation engine processes data in real-time, then it should group sentiment scores by channel and update the heatmap within 5 seconds with accurate average sentiment values.

Custom Aggregation Window Processing

Given a user selects a specific aggregation window (e.g., 5-minute, hourly, daily), when the engine executes aggregation, then it calculates average sentiment, trend direction, and volatility for that window within 2 seconds of request.

Category-based Sentiment Grouping

Given sentiment scores tagged with predefined categories (e.g., product line), when the engine aggregates data, then it correctly groups scores by category and computes separate aggregate metrics for each category.

Trend Direction Calculation

Given historical sentiment data over a defined period, when trend analysis runs, then the engine identifies the overall sentiment trend direction (increasing, decreasing, stable) using a regression algorithm with at least 95% accuracy.

Sentiment Volatility Detection

Given incoming sentiment data for a daily window, when volatility analysis is performed, then the engine computes the standard deviation of sentiment scores and flags any category exceeding the configurable volatility threshold.

Interactive Drill-down Detail View

"As a support lead, I want to drill down from the heatmap into session transcripts so that I can understand and address the specific issues causing negative sentiment."

Description

Enable users to click on any cell of the heatmap to drill down into detailed information, including the list of chat sessions, transcripts, sentiment timestamps, and associated metadata (agent, customer profile). The detailed view will provide filters and search capabilities, allowing teams to investigate specific incidents, understand context, and tailor responses. This feature ensures that high-level sentiment insights can seamlessly translate into actionable support tasks.

Acceptance Criteria

Open Detail View from Heatmap Cell

Given a user clicks on a heatmap cell corresponding to a specific sentiment range, When the click is registered, Then a detail view modal opens displaying the list of related chat sessions.

Filter Chat Sessions by Agent and Date

Given the detail view is open, When the user applies filters for agent name and date range, Then the list of chat sessions updates to show only sessions matching those criteria.

Search Within Transcripts

Given the detail view is open, When the user enters a keyword into the search bar, Then all transcripts containing the keyword are highlighted and listed in the results.

Display Metadata for Each Session

Given the detail view is open, Then each chat session entry displays the agent name, customer profile details, sentiment timestamp, and sentiment score.

Performance of Detail View Loading

Given up to 100 chat sessions are linked to the selected heatmap cell, When the user clicks the cell, Then the detail view fully loads within 2 seconds.

Threshold-based Sentiment Alerts

"As a support lead, I want to receive alerts when negative sentiment rises above a set threshold so that I can allocate resources and address issues before they escalate."

Description

Offer a configurable alerting system that monitors sentiment heatmap metrics against user-defined thresholds (e.g., negative sentiment exceeding 30% in any channel). When thresholds are breached, the system sends real-time notifications via email, SMS, or in-app alerts, and logs events in the workflow automation engine. This capability ensures support teams are immediately notified of sentiment spikes, enabling rapid response and preventing potential escalation of customer dissatisfaction.

Acceptance Criteria

Threshold Breach Detection

Given the sentiment heatmap metrics refresh every minute When negative sentiment in any channel exceeds the configured threshold Then the system flags the breach and triggers the alert workflow within 5 seconds

Real-Time In-App Alert Notification

Given a threshold breach is detected When the user is logged into the PulseDesk application Then an in-app notification appears in the alerts panel within 10 seconds

Email Alert Dispatch

Given a threshold breach is detected and email notifications are enabled When the breach event occurs Then an email is sent to the configured support leads within 15 seconds containing channel name, sentiment percentage, and timestamp

SMS Alert Dispatch

Given a threshold breach is detected and SMS notifications are enabled When the breach event occurs Then an SMS is delivered to the configured phone numbers within 20 seconds with channel identifier and breach details

Workflow Engine Logging

Given a threshold breach is detected When the alert workflow is executed Then an event is logged in the workflow automation engine with breach details, timestamp, and notification status

TrendSpotter

Analyzes incoming chat data to surface emerging topics and recurring keywords, empowering teams to proactively address common issues and update knowledge bases before small problems escalate.

Requirements

Real-time Topic Detection

"As a support lead, I want to see emerging conversation topics in real time so that I can address potential issues before they escalate."

Description

Continuously analyzes incoming chat messages to identify emerging discussion topics and recurring phrases as they occur, enabling support teams to detect issues early, allocate resources proactively, and reduce ticket resolution times.

Acceptance Criteria

Emerging Product Issue Detection

Given streaming chat messages, When the system detects a phrase appearing more than 10 times within a 5-minute window AND the growth rate is greater than 20% over the previous window, Then the dashboard displays an emerging topic alert with the phrase, count, and growth percentage.

Refund Request Surge Identification

Given incoming support chats, When user messages contain the words ‘refund’, ‘cancel subscription’, or ‘money back’ more than 15 times in 10 minutes, Then an alert is generated in the TrendSpotter panel indicating a refund request surge with timestamp and frequency.

New Keyword Discovery for Knowledge Base Update

Given the knowledge base does not include a detected phrase, When that phrase appears in chat messages at least 8 times within 15 minutes AND is not matched to existing KB entries, Then the system flags the phrase as a candidate for knowledge base update and adds it to the ‘New Topics’ list.

Negative Sentiment Trend Alert

Given sentiment analysis of chat messages, When the proportion of negative sentiment phrases exceeds 30% of all messages in a 10-minute interval, Then TrendSpotter displays a negative sentiment trend alert with sample messages and percentage.

High Traffic Peak Topic Highlight

Given any 5-minute period with chat volume above 500 messages, When the system identifies the top 3 recurring keywords during this peak, Then the system highlights these keywords in the TrendSpotter dashboard with counts and percentage of total volume.

Keyword Frequency Dashboard

"As a support agent, I want to view a dashboard of trending keywords so that I can quickly understand and address the most common customer inquiries."

Description

Displays a visual dashboard of the most frequently mentioned keywords and their trends over selectable time intervals, helping teams prioritize common issues and monitor shifts in customer concerns.

Acceptance Criteria

Time Interval Selection Scenario

Given the user accesses the Keyword Frequency Dashboard, when they select a predefined or custom time interval, then the dashboard updates to display keyword frequencies for the selected interval within 2 seconds.

Top Keywords Visualization Scenario

Given keyword frequency data is available, when the dashboard renders, then it displays the top 10 keywords in a bar chart sorted by descending frequency with tooltips showing exact counts on hover.

Keyword Trend Comparison Scenario

Given multiple keywords are selected, when the user enters comparison mode, then the line chart overlays trends for each selected keyword with distinct colors and a visible legend.

Dashboard Data Refresh Scenario

Given new chat data is received, when the auto-refresh interval (5 minutes) elapses, then the dashboard reloads data and displays a notification with the latest refresh timestamp.

Dashboard Export Scenario

Given the dashboard view is active, when the user clicks ‘Export CSV’ or ‘Export PNG’, then the system generates and initiates download of the corresponding file within 5 seconds.

Threshold-based Alert Notifications

"As a support manager, I want to receive alerts when a topic frequency surpasses a set threshold so that I can respond promptly to surges in related tickets."

Description

Enables configuration of custom thresholds for keyword and topic occurrences, triggering email or in-app alerts when thresholds are exceeded to prompt immediate investigation and action.

Acceptance Criteria

Admin Sets Keyword Occurrence Threshold

Given an admin on the Threshold Settings page When they enter a keyword, select a numerical threshold between 1 and 10,000, choose an evaluation time window, and click Save Then the system validates inputs, persists the new threshold rule, and displays a confirmation message

Email Alert Sent When Keyword Threshold Breach

Given a configured keyword threshold of N occurrences in a 1-hour window When incoming chat data triggers N+1 occurrences within that window Then the system sends an email alert to all subscribed users within 60 seconds including keyword, count, time window, and a link to TrendSpotter details

In-App Notification Display on Threshold Breach

Given a configured topic threshold rule When the threshold is exceeded Then an in-app alert appears in the user’s TrendSpotter dashboard within 30 seconds showing topic name, occurrence count, time window, and a link to view related chat logs

Multiple Topic Threshold Breaches Handled Independently

Given multiple threshold rules for different topics When each topic’s occurrences exceed its respective threshold Then the system generates separate alerts for each topic and delivers them via configured channels without delay or merging

Threshold Rule Modification and Update

Given an existing threshold rule When an admin edits the threshold value or time window and clicks Update Then the system validates changes, overwrites the previous rule, applies the updated rule immediately, and logs the modification with a timestamp

Customizable Topic Categories

"As a support lead, I want to create and manage my own topic categories so that insights reflect our internal processes and terminology."

Description

Allows users to define, group, and label detected topics into custom categories, improving clarity in reporting and ensuring that insights align with the organization’s terminology and workflows.

Acceptance Criteria

Creating a new custom topic category

Given a user is on the Custom Topic Categories page When the user enters a unique category name and optional description and clicks 'Create' Then the category appears in the category list within 2 seconds

Assigning detected topics to a custom category

Given detected topics are listed on the Topics page When the user selects multiple topics and chooses a custom category from the 'Category' dropdown and clicks 'Assign' Then the selected topics display the chosen category label and are filterable by that category

Editing an existing custom category

Given a custom category exists When the user navigates to the category's edit modal, updates the name or description, and saves changes Then the updated name and description are reflected in the category list and related topic labels

Deleting a custom topic category

Given a custom category with no topics assigned exists When the user clicks the 'Delete' button for that category and confirms the action Then the category is removed from the list and no longer available for assignment

Filtering analytics reports by custom category

Given custom categories contain assigned topics When the user generates a Trends report and filters by a specific custom category Then the report displays only topics within that category and updates visualizations accordingly

Knowledge Base Auto-Sync

"As a knowledge manager, I want surfaced topics to auto-sync with our knowledge base so that documentation stays current and reduces repetitive explanations."

Description

Automatically creates or updates knowledge base articles based on high-frequency topics, linking surfaced insights directly to relevant documentation to streamline issue resolution and maintain up-to-date resources.

Acceptance Criteria

High-Frequency Topic Detection Trigger

Given a topic’s mention count exceeds the defined frequency threshold in a 24-hour window When the auto-sync process runs Then a draft knowledge base article is created with the topic title, summary, and suggested tags

Existing Article Update on Topic Volume Surge

Given an existing knowledge base article is tagged with the topic keyword When the topic’s mention volume increases by more than 20% over the past week Then the article is updated with a new section detailing the emerging issues and resolutions

Successful Article Linking to Insight

Given a new or updated article is created by the auto-sync process When the process completes Then the article includes a valid link back to the original trendspotter insight report

Failed Sync Notification and Retry

Given the auto-sync process encounters an API or network error When the error occurs Then the system retries the sync up to three times and logs the failure If all retries fail Then an alert is sent to the support admin dashboard

No Action on Low-Frequency Topics

Given topics with mention counts below the frequency threshold When the auto-sync process runs Then no new articles are created and no existing articles are modified

Channel Pulse

Provides individualized sentiment graphs for each chat channel, enabling support leads to compare performance, spot underperforming channels, and tailor engagement strategies to suit different customer preferences.

Requirements

Channel Pulse Data Aggregation

"As a support lead, I want all channel chat data ingested and updated in real time so that I can have accurate, up-to-date sentiment insights across channels."

Description

Implement a robust data ingestion pipeline that collects chat transcripts, sentiment scores, and metadata across all channels. This pipeline should normalize data formats, ensure real-time updates, and integrate seamlessly with the analytics engine. It must handle data at scale, ensure data accuracy, and support incremental loading for improved performance.

Acceptance Criteria

Data Ingestion Continuity During Peak Traffic

Given the pipeline handles a surge of 10,000 messages per minute, When peak traffic occurs, Then no data loss should occur and end-to-end ingestion latency must remain under 5 seconds.

Data Normalization Consistency Across Channels

Given incoming chat data from multiple channels, When data is processed, Then all fields must conform to the unified schema with consistent field names, data types, and formats.

Real-Time Updates Reflected in Analytics

Given new chat transcripts and sentiment scores are ingested, When the analytics engine processes them, Then updates must appear on the sentiment dashboard within 2 minutes.

Incremental Data Loading Efficiency

Given previously ingested data and new data, When an incremental load runs, Then only new or modified records are ingested and the process completes within 3 minutes.

Data Accuracy Validation Against Source Records

Given ingested records stored in the analytics database, When cross-validated with source records, Then discrepancies must be below 0.1% for all metadata fields.

Customizable Sentiment Dashboard

"As a support lead, I want to customize sentiment dashboards by channel and timeframe so that I can focus on the metrics that matter most to my team’s performance."

Description

Develop an interface that allows support leads to customize sentiment graphs by channel, timeframe, and sentiment thresholds. Users should be able to apply filters, choose chart types (line, bar, heatmap), and save dashboard presets. This feature enhances flexibility, enabling tailored analysis and quicker identification of trends.

Acceptance Criteria

Applying Channel Filters

Given the support lead is on the sentiment dashboard When they select a specific chat channel filter Then only sentiment data for that channel is displayed correctly

Filtering by Timeframe

Given the support lead is on the sentiment dashboard When they set the timeframe to the last 7 days and apply the filter Then the sentiment graphs update to reflect data only within that selected period

Switching Chart Types

Given the support lead is viewing a sentiment graph When they choose a new chart type (line, bar, heatmap) from the dropdown Then the dashboard renders the selected chart type with the same filtered data without errors

Setting Sentiment Thresholds

Given the support lead is on the sentiment dashboard When they define positive and negative sentiment thresholds and apply them Then data points outside the thresholds are highlighted clearly according to the defined levels

Saving and Loading Dashboard Presets

Given the support lead has configured filters, chart types, and thresholds When they save the current dashboard as a preset and later select it Then the dashboard restores all saved settings exactly as they were configured

Channel Performance Comparison

"As a support lead, I want to compare channel performance metrics side by side so that I can quickly identify underperforming channels and allocate resources effectively."

Description

Provide a tool for side-by-side comparison of sentiment metrics across channels. This feature should highlight variance in sentiment scores, response times, and ticket resolution rates. Visual indicators (e.g., color coding for underperformance) should draw attention to channels that require intervention, facilitating data-driven strategy adjustments.

Acceptance Criteria

Side-by-Side Sentiment Score Comparison

Given the user selects two or more channels and a date range, when the comparison tool is loaded, then the sentiment scores for each channel are displayed side-by-side in a bar chart or table with values matching the system-calculated averages within ±0.5%.

Response Time Comparison Across Channels

Given the user selects channels and a date range, when viewing the response time comparison, then average and median first-response times for each channel are displayed, and each value reflects the correct calculation within ±1 minute of raw ticket timestamps.

Ticket Resolution Rate Comparison

Given the user views the resolution rate comparison for selected channels, when the data is rendered, then the percentage of tickets resolved within SLA for each channel is displayed and matches backend data within ±1%.

Underperforming Channel Highlighting

Given the comparison view is displayed, when any channel's sentiment score falls below 0.4, response time exceeds 2 hours, or resolution rate drops below 80%, then the channel row is highlighted in red and a tooltip explains the specific underperformance metric.

Cross-Metric Channel Ranking

Given the user selects a metric (sentiment score, response time, or resolution rate), when sorting is applied, then channels are reordered correctly in descending order for sentiment and resolution rate, and in ascending order for response time.

Real-time Sentiment Alerts

"As a support lead, I want to receive real-time alerts when sentiment declines so that I can intervene immediately and prevent potential escalations."

Description

Implement an alert system that notifies support leads when a channel’s sentiment score drops below configured thresholds or exhibits sudden negative trends. Alerts should be configurable via email, Slack, or in-app notifications. This proactive feature enables timely intervention to address customer dissatisfaction before escalation.

Acceptance Criteria

Email Notification on Threshold Breach

Given a channel’s sentiment score falls below the configured threshold; when the system detects the drop in real time; then an email containing channel identifier, current sentiment score, threshold value, timestamp, and link to detailed analytics is sent to all configured recipients within 60 seconds.

Slack Notification for Sudden Negative Trend

Given a channel’s sentiment score declines by more than the defined percentage within a 10-minute window; when the negative trend is confirmed; then a formatted Slack message with channel name, trend percentage, time window, and actionable recommendations is posted to the designated Slack channel immediately.

In-App Alert Visibility

Given an alert is triggered for any channel; when a support lead logs into PulseDesk; then a visible in-app notification badge and alert message displaying channel, alert type, and brief description appear in the notifications panel until marked as read.

Configurable Threshold Settings

Given a support lead navigates to alert settings; when the user updates threshold values or trend percentages per channel; then the system saves the new configuration and applies it to all subsequent real-time sentiment evaluations without requiring a page reload.

Duplicate Alert Suppression

Given an alert has already been issued for a specific channel within the last 30 minutes; when additional threshold breaches or negative trends occur in the same channel; then no duplicate email, Slack, or in-app alerts are sent until the suppression window elapses.

Channel-specific Engagement Recommendations

"As a support lead, I want personalized engagement recommendations based on channel sentiment trends so that I can optimize interactions and improve customer satisfaction."

Description

Build an AI-driven engine that analyzes sentiment data and historical resolutions to suggest tailored engagement strategies for each channel. Recommendations might include tone adjustments, scripting suggestions, or workflow automations. Integration with the no-code workflow builder should allow one-click implementation of recommended actions.

Acceptance Criteria

Generating Recommendations for Low-Sentiment Channels

Given a chat channel with average sentiment score below the defined threshold When the AI engine runs Then at least three tailored tone adjustment suggestions must be displayed

Suggesting Workflow Automations for High-Volume Channels

Given a chat channel with ticket volume exceeding 100 per day When historical resolution patterns are analyzed Then the engine must propose at least two relevant no-code workflow automations

One-Click Implementation of Recommended Actions

Given a displayed recommendation When the user clicks the “Apply Recommendation” button Then the corresponding action must be automatically configured and activated in the no-code workflow builder

Personalized Scripting Suggestions with Dynamic Placeholders

Given a recommended scripting suggestion When previewing the script Then all dynamic placeholders (e.g., customer name, ticket ID) must be correctly populated with real ticket data

Comparative Display of Channel-Specific Recommendations

Given multiple channels selected by the user When viewing recommendations Then the UI must display a side-by-side comparison of sentiment metrics and corresponding engagement strategies for each channel

Spike Alerts

Sends customizable notifications when sentiment or chat volume surges beyond set thresholds, ensuring teams can jump on critical spikes immediately and prevent negative experiences from spiraling.

Requirements

Threshold Configuration

"As a support lead, I want to configure precise spike thresholds so that my team only receives alerts relevant to our operational capacity and customer impact."

Description

Enable support leads to define custom thresholds for chat volumes and sentiment scores that trigger spike alerts. Administrators can specify numeric limits or percentage changes over a given time window, set different thresholds per channel or team, and adjust sensitivity settings. The system validates input ranges, provides real-time feedback on threshold impact, and stores configurations persistently for audit and rollback. Upon threshold breach, the alert engine flags the event for processing by downstream notification modules.

Acceptance Criteria

Chat Volume Numeric Threshold Configuration

Given an administrator navigates to the threshold settings page When they enter a numeric chat volume value between 1 and 10000 and click Save Then the system validates the range, persists the threshold, and displays a confirmation message

Sentiment Percentage Change Threshold Configuration

Given an administrator selects sentiment threshold mode When they enter a percentage change value between 1% and 100% over a specified time window and click Save Then the system validates the input, stores the configuration, and shows a success notification

Channel-Specific Threshold Assignment

Given an administrator views channel settings When they assign unique thresholds for chat volume and sentiment per support channel and save Then each channel’s thresholds are saved independently and reflected in the configuration list

Real-Time Threshold Impact Feedback

Given an administrator adjusts threshold values When they modify the input sliders Then the system displays estimated alert frequency feedback dynamically without saving

Threshold Configuration Persistence and Audit

Given an administrator saves threshold settings When the system persists data Then configuration changes are recorded with timestamp, user ID, and previous values for audit and rollback purposes

Spike Alert Triggering on Threshold Breach

Given live chat volume or sentiment data exceeds configured thresholds When the breach occurs Then the alert engine flags the event for downstream notification modules within 30 seconds

Real-time Spike Detection

"As a support lead, I want the system to detect volume or sentiment spikes in real time so that I can respond immediately to emerging issues."

Description

Implement a streaming analytics component that continuously ingests chat volume and sentiment data from live conversations and tickets. The engine applies sliding time windows and statistical algorithms to detect spikes exceeding configured thresholds. It must handle high throughput, maintain low detection latency (under 5 seconds), and generate structured spike events containing metadata (timestamp, channel, team, metric values) for downstream processing.

Acceptance Criteria

High Throughput Data Ingestion

Given a simulated stream of 1,000 chat messages per second across multiple channels When the analytics engine ingests the data Then 95% of messages are processed without loss and passed to the detection pipeline within 5 seconds

Sentiment Spike Detection

Given a 5-minute sliding window where average sentiment decreases by more than 20% below the configured threshold When sentiment metrics are evaluated Then a spike event is generated within 5 seconds

Metadata Enrichment of Spike Events

Given a detected spike When the system generates the event Then the event payload includes an ISO8601 timestamp, channel identifier, team identifier, and the raw metric values that triggered the spike

Threshold Configuration Adaptation

Given an administrator updates the spike threshold settings in the configuration UI When the change is saved Then the new thresholds are applied to all subsequent detection windows within 60 seconds

Low Latency Processing

Given continuous ingestion of chat volume and sentiment data When spike detection is triggered Then 99th percentile of detection-to-event latency remains under 5 seconds

Transient Failure Recovery

Given a temporary ingestion failure or backpressure When the data stream resumes Then the analytics engine automatically recovers, processes any buffered data, and ensures no events are lost

Multi-channel Notification Delivery

"As a support manager, I want spike alerts delivered via my preferred communication channels so that I can coordinate my team’s response without switching tools."

Description

Provide a notification service that dispatches spike alerts across multiple channels such as email, Slack, Microsoft Teams, and in-app notifications. Users can map alert types to preferred channels, define escalation paths, and configure rate limits or snooze periods. The service ensures reliable delivery with retries, logs all dispatch attempts, and supports templated payloads including dynamic fields (metric values, timestamps, links to dashboards).

Acceptance Criteria

Email Notification Dispatch

Given a spike alert is triggered and the user has configured email as a delivery channel When the notification service sends the alert Then the email is delivered to the recipient’s inbox within 30 seconds and a delivery log entry is created

Slack Notification Dispatch

Given a spike alert is triggered and the user has mapped the alert type to a Slack channel When the notification service dispatches the alert Then the message appears in the configured Slack channel within 15 seconds with correct formatting and dynamic fields

Microsoft Teams Notification Dispatch

Given a spike alert is triggered and the user has mapped the alert type to a Microsoft Teams channel When the notification service dispatches the alert Then the message appears in the designated Teams channel within 15 seconds and includes links to the dashboard

In-App Notification Delivery

Given a spike alert is triggered and in-app notifications are enabled for the user When the service dispatches the alert Then the notification appears in the user’s in-app notification center within 10 seconds with a clickable link to the relevant dashboard view

Retry and Logging on Delivery Failure

Given a dispatch attempt fails due to a transient error When the notification service retries up to three times Then each attempt is logged with timestamp, status, and error code and a final failure log is generated if all retries fail

Rate Limit and Snooze Enforcement

Given the user has configured rate limits or snooze periods for alerts When the number of alert dispatches exceeds the configured threshold Then further notifications are suppressed during the snooze period and a summary log entry is created indicating suppression count

Alert Template Library

"As a support lead, I want to use and customize alert templates so that I can quickly configure consistent and actionable notifications."

Description

Offer a library of customizable alert templates for different spike scenarios (e.g., sudden volume surge, negative sentiment trend). Templates include predefined text, variable placeholders, severity levels, and suggested remediation steps. Users can clone, edit, and save templates, assign defaults per team or channel, and preview rendered messages. The library integrates with the notification service to populate and send alerts.

Acceptance Criteria

Cloning and Editing an Alert Template

Given a user selects an existing alert template and clicks 'Clone', then a new template is created with a default name appended with 'Copy'; When the user modifies template text, placeholders, severity level, or remediation steps and clicks 'Save', then the changes persist and are displayed correctly in the template list and preview.

Creating and Saving a New Alert Template

Given a user opens the 'New Template' form and fills in required fields (template name, text, placeholders, severity level), the 'Save' button becomes enabled; When the user clicks 'Save', then the new template appears in the library with correct details and is selectable for assignment and preview.

Previewing a Rendered Alert Message

Given a user clicks 'Preview' on a template, then the system replaces all variable placeholders with sample data; When the preview loads, it displays the fully rendered message with severity icon and remediation steps without any placeholder tags or errors.

Assigning Default Alert Templates to Teams or Channels

Given a user navigates to team/channel settings, then the default template dropdown lists all saved templates; When the user selects a template and clicks 'Save', then that template is set as default and is automatically applied to future alerts for that team/channel unless overridden.

Sending Alert via Notification Service

Given a spike alert condition is met and its associated template is set, then the system populates the template with live event data and triggers the notification service; When the notification is sent, then the service receives the correct payload including rendered message, severity, and remediation link, and the alert is delivered to the designated channel.

Spike Activity Dashboard

"As a support analyst, I want a dashboard showing past and ongoing spikes so that I can analyze trends and refine our alert configurations."

Description

Design an interactive dashboard within PulseDesk that visualizes historical and current spike events across channels and teams. The dashboard displays time-series charts of volume and sentiment metrics, highlights threshold breaches, and allows filtering by date range, channel, or team. It supports drill-down into individual spike events for detailed context, shows notification statuses, and provides export capabilities for reporting.

Acceptance Criteria

Viewing Historical Spike Trends

Given the support lead accesses the Spike Activity Dashboard, when they select a historical date range, then the time-series charts display volume and sentiment metrics for that range with correct values plotted.

Filtering Spikes by Channel and Team

Given the support lead opens the filter panel, when they select a specific channel and team, then the dashboard updates to show only spike events for the selected channel and team.

Drill-Down into Individual Spike Event Details

Given a spike event in the chart, when the support lead clicks on the data point, then a detailed view opens showing event timestamp, volume data, sentiment scores, and related chat transcripts.

Highlighting Threshold Breaches in Charts

Given threshold limits are configured, when an event exceeds volume or sentiment thresholds, then the chart visually highlights the breach with a red marker and tooltip indicating threshold value.

Exporting Spike Data for Reporting

Given the support lead clicks the export button, when they choose an export format (CSV or PDF), then the system generates and downloads a file containing the filtered spike event data including timestamps, metrics, and notification status.

Displaying Notification Statuses for Spike Events

Given the dashboard displays spike events, when notification statuses are available, then each event shows an icon indicating whether the alert was sent, pending, or failed, with tooltip details on hover.

Cluster Insights

Automatically groups similar conversations into thematic clusters, helping agents uncover root causes, prioritize bulk resolutions, and craft targeted responses for the most common support requests.

Requirements

Data Ingestion Pipeline

"As a support manager, I want the system to automatically ingest all customer conversations from live chat and ticketing channels so that cluster insights have complete data for accurate analysis."

Description

Implement a robust ingestion pipeline to collect and normalize conversation data from live chat and ticketing systems. The pipeline must support real-time streaming and batch processing, handle various data formats and sources, ensure data integrity, and populate the unified data store for cluster analysis. This ensures comprehensive and up-to-date conversation data feeding into the Cluster Insights feature.

Acceptance Criteria

Peak Volume Real-Time Streaming

Given the ingestion pipeline is receiving live chat and ticket data at 1,000 events per second, When the system processes the incoming stream continuously, Then no data is dropped and the unified store is updated with each event within 2 seconds of receipt.

Historical Batch Processing Execution

Given a backlog of 1 million historical conversation records in CSV and JSON formats, When the batch job runs during non-business hours, Then all records are ingested, transformed, and loaded into the unified store within 3 hours with zero ingestion errors.

Multi-Format Data Normalization

Given incoming data in JSON, XML, and CSV formats from different ticketing systems, When the ingestion pipeline processes the data, Then all records are normalized to the unified schema and no malformed records remain in staging.

Data Integrity and Validation

Given ingested conversation records, When the system applies schema validation and duplicate detection, Then invalid records are logged with error details and excluded from the unified store while valid records are ingested.

Unified Data Store Population Confirmation

Given the ingestion pipeline has processed a mix of real-time and batch data, When querying the unified store, Then the total conversation count matches the source systems' count within a 0.1% margin and required metadata fields are correctly populated.

Topic Modeling Engine

"As a support agent, I want similar conversations grouped by theme automatically so that I can quickly identify trending issues."

Description

Develop an automated topic modeling engine leveraging NLP and machine learning to analyze conversation text, extract key phrases, and group similar ticket and chat interactions into thematic clusters. The engine should allow configurable clustering thresholds, support continuous learning to improve accuracy over time, and integrate with the data store to feed results into the UI. This component enables agents to uncover root causes and identify common support trends.

Acceptance Criteria

Initial Clustering Accuracy Validation

Given a labeled dataset of 1000 tickets with known topics, when the engine processes the dataset using default clustering parameters, then at least 85% of tickets must be correctly grouped into clusters matching their labels.

Configurable Threshold Effect

Given the clustering threshold is adjusted to 0.7, when the engine runs on a sample dataset, then the average intra-cluster similarity score must be at least 0.7 and the number of clusters dynamically reflects the threshold change.

Continuous Learning Model Improvement

Given corrected cluster assignments are fed back into the system, when the engine retrains, then the clustering accuracy on a held-out validation set must improve by at least 5%.

Integration with Data Store

Given clustering results are generated, when they are stored in the data store, then each ticket record must include a cluster ID field and top three key phrases fields without data loss or corruption.

Performance Under Scale

Given a batch of 10,000 tickets, when the engine runs clustering, then processing must complete within 5 minutes with no failures or timeouts and resource usage within acceptable limits.

Root Cause Analysis Dashboard

"As a support lead, I want a centralized dashboard displaying cluster summaries and metrics so that I can prioritize and assign bulk resolutions effectively."

Description

Design and build a dashboard within PulseDesk that displays cluster summaries, including title, size, trend metrics, and representative conversation snippets. The dashboard should enable filtering by date range, channel, and priority, allow agents to drill down into individual clusters, and provide visualizations like charts and word clouds. This interface empowers support leads to quickly understand cluster patterns and prioritize resources.

Acceptance Criteria

Viewing Cluster Summaries

Given the support lead opens the Root Cause Analysis Dashboard When the dashboard loads Then a list of clusters is displayed with each cluster’s title, size, trend metric visualization, and a representative conversation snippet

Filtering Clusters by Date Range

Given the dashboard is displayed When the support lead selects a start and end date Then only clusters containing conversations within the selected date range are shown and the cluster counts and trend metrics update accordingly

Filtering Clusters by Channel and Priority

Given the dashboard is displayed When the support lead applies channel and/or priority filters Then only clusters matching the selected channel(s) and priority level(s) are displayed

Drilling Down into Cluster Details

Given a cluster entry is visible on the dashboard When the support lead clicks on the cluster entry Then a detailed view opens showing all conversations in the cluster, associated metadata, and provides an export option for the conversation list

Visualizing Cluster Data with Charts and Word Clouds

Given the support lead views a specific cluster on the dashboard When the visualizations tab is selected Then interactive charts show conversation trends over time and a word cloud displays the most frequent terms from the cluster’s conversations

Bulk Resolution Suggestions

"As a support agent, I want the system to suggest templated responses for each conversation cluster so that I can craft targeted replies faster."

Description

Implement a suggestion engine that proposes templated responses or workflows for identified clusters based on historical resolutions and best practices. The engine should allow review and editing of suggested replies, support no-code workflow automation triggers, and track usage metrics for feedback. This requirement streamlines response creation, reduces resolution time, and maintains consistency.

Acceptance Criteria

Template Suggestion Generation for Identified Cluster

Given a support ticket cluster is identified by the system When the suggestion engine is triggered Then it displays at least three templated responses derived from historical resolutions and best practices with each suggestion including subject, body, and workflow tags

Agent Reviews and Edits Suggested Reply

Given one or more templated responses are suggested When an agent selects a suggestion to review Then the system opens the response in an editor allowing the agent to modify content, variables, and placeholders and save the edited response

Workflow Trigger Configuration via No-Code Builder

Given an agent has accepted a suggested workflow trigger When the agent opens the no-code builder Then the system pre-populates the trigger configuration and allows the agent to adjust conditions, actions, and automations before saving

Usage Metrics Tracking for Suggested Responses

Given templated responses are suggested and used When an agent applies a suggestion to resolve a ticket Then the system logs the usage event with timestamp, agent ID, cluster ID, and suggestion ID for analytics purposes

Feedback Loop Integration for Suggestion Refinement

Given an agent has used or modified a suggested response When the ticket is closed Then the system prompts the agent for feedback on the suggestion quality and stores the feedback score linked to the suggestion for future model training

Real-Time Cluster Updates

"As a support agent, I want clusters to update in real time as new tickets are created so that I always have the latest insight into trending issues."

Description

Ensure the clustering system updates dynamically as new conversations are ingested, delivering real-time cluster modifications to the dashboard and suggestion engine. This includes incremental re-clustering, event-driven updates, and notification mechanisms for agents when significant cluster shifts occur. Real-time updates keep agents informed of emerging issues immediately.

Acceptance Criteria

Live Dashboard Cluster Refresh

Given the agent has the cluster dashboard open, when a new conversation is ingested, then the dashboard updates only the affected clusters and refreshes within 5 seconds to display the latest cluster composition.

Incremental Re-clustering on New Conversation

Given the clustering system receives a new conversation event, when processing it, then incremental re-clustering occurs in under 3 seconds and the resulting clusters maintain at least 90% similarity to a full re-cluster operation.

Suggestion Engine Update

Given the suggestion engine is active, when clusters change due to new data, then the engine fetches and displays updated suggestions reflecting the current clusters with no stale or outdated entries.

Agent Notification on Significant Cluster Shift

Given a cluster’s size or label composition changes by more than 20%, when this threshold is crossed, then subscribed agents receive an in-app notification within 2 minutes detailing the cluster shift.

Event-Driven Cluster Update Trigger

Given a conversation ingestion event occurs in the system, when the event is published to the clustering service, then the service triggers a cluster update workflow automatically and logs the event with a timestamp.

SkillSync

AI-powered assignment that analyzes ticket content, customer context, and agent expertise to match issues with the best-fit support agent—reducing transfers and maximizing first-contact resolution.

Requirements

Ticket Content Analyzer

"As a support lead, I want the system to analyze incoming ticket content automatically so that I can assign tickets to the most qualified agents without manual review."

Description

Automatically parse and analyze the textual content of incoming tickets using natural language processing to identify issue categories, keywords, and sentiment. This functionality ensures that key ticket attributes are extracted and standardized for downstream processing, enabling precise matching with agent expertise and reducing manual triage overhead.

Acceptance Criteria

Category Identification in Ticket Intake

Given an incoming customer support ticket containing descriptive text When the Ticket Content Analyzer processes the ticket Then it assigns one of the predefined issue categories with at least 90% matching accuracy

Keyword Extraction for Ticket Routing

Given an incoming ticket text When the analyzer extracts keywords Then it identifies and returns the top 5 keywords with a relevance score of at least 0.8 per keyword

Sentiment Analysis for Escalation Alerts

Given a support ticket When the analyzer evaluates sentiment Then it labels the ticket as positive, neutral, or negative with confidence above 85% and flags negative sentiment tickets for supervisor review

Standardization of Ticket Attributes

Given an analyzed ticket with raw metadata When the analyzer standardizes attributes Then it populates standardized fields (category, keywords, sentiment) in the ticket record according to schema without data loss

Performance Threshold under High Volume

Given a volume of 1000 tickets per minute When the analyzer processes tickets Then average processing latency per ticket remains below 200 ms with no errors

Customer Context Integrator

"As a support agent, I want the system to consider a customer’s context so that I have the necessary background and can tailor my response appropriately."

Description

Aggregate and evaluate customer-specific data such as account tier, purchase history, previous support interactions, and personalized preferences. By integrating this context into the matching algorithm, the system can prioritize assignments based on customer value and history, improving first-contact resolution and customer satisfaction.

Acceptance Criteria

Priority Assignment Based on Account Tier

Given a ticket originates from a customer with a Platinum account tier, when the Customer Context Integrator evaluates the ticket, then the system assigns it to an agent in the Platinum support queue within 5 seconds.

Assignment Influenced by Purchase History

Given a ticket is submitted by a customer whose purchase history shows high-value transactions, when the integrator analyzes purchase records, then the ticket is prioritized and routed to an agent with relevant product expertise.

Routing Using Previous Support Interactions

Given a customer has an ongoing support case history, when a new ticket is received, then the integrator matches it to the last handling agent if available and flags if escalation is needed.

Incorporation of Personalized Preferences

Given a customer’s language and communication preferences are stored, when a ticket is created, then the integrator routes the ticket to an agent fluent in the preferred language and using the preferred channel.

Balancing Workload with Contextual Priority

Given multiple high-value tickets in the queue, when the integrator aggregates customer context, then it distributes assignments evenly among qualified agents while still prioritizing by customer value.

Agent Expertise Profiling

"As a support manager, I want up-to-date profiles of each agent’s expertise so that ticket assignments reflect current strengths and availability."

Description

Continuously build and maintain a dynamic repository of agent skills, certifications, historical performance metrics, and domain expertise. This profile is updated in real time from multiple data sources and serves as the foundation for matching tickets with agents whose capabilities align with the ticket requirements.

Acceptance Criteria

Certification Update Trigger

Given an agent completes a new certification and the training platform sends a webhook; When the system processes the webhook; Then the agent's profile includes the new certification within 5 minutes and it is visible in the agent dashboard.

Performance Metric Aggregation

Given historical performance data is available; When the system runs its hourly aggregation job; Then the agent's profile’s ticket resolution times and customer satisfaction scores are updated accurately without data discrepancies.

Domain Expertise Tag Assignment

Given an agent has handled over 100 tickets in a specific domain with a CSAT ≥4 within the past 30 days; When the monthly domain analysis runs; Then the agent’s profile is automatically tagged with that domain expertise.

Availability and Workload Balancing

Given the agent’s availability schedule and current ticket workload; When a new ticket is queued; Then the system filters out agents exceeding their workload threshold or marked unavailable and only matches tickets to eligible agents.

Profile Consistency Across Sources

Given multiple data sources for agent skills (HR records, L&D platforms, internal logs); When the nightly synchronization process runs; Then all skill entries match across sources and any conflicts are flagged for manual review.

Match Recommendation Engine

"As a support lead, I want ranked agent recommendations so that I can quickly assign tickets to agents with the highest match confidence."

Description

Leverage machine learning algorithms to compute a match confidence score for each agent-ticket pair by evaluating ticket attributes against agent profiles. The engine ranks potential agents and provides explainable recommendations to support leads, facilitating transparent and informed assignment decisions.

Acceptance Criteria

Agent Confidence Score Calculation

Given a ticket with defined attributes and active agent profiles, when the match engine processes the ticket, then it returns a confidence score as a decimal between 0.00 and 1.00 for each agent without any null or invalid values.

Recommendation Ranking Display

Given multiple agent-ticket confidence scores, when recommendations are presented to the support lead, then agents are listed in descending order by confidence score and the top five recommendations are displayed.

Explainability of Recommendations

Given a recommended agent and their confidence score, when the support lead requests details, then the system provides the top three contributing factors with their individual weight percentages and a brief description for each factor.

Real-time Recommendation Update

Given a ticket attribute update (e.g., priority or customer segment change), when the update is saved, then the recommendation engine recalculates scores and refreshes the displayed agent ranking within two seconds.

Edge Case Handling for Missing Data

Given incomplete or missing ticket or agent profile data, when the engine attempts to calculate scores, then default fallback values are applied, the process completes without errors, and a warning log entry is generated for the missing fields.

Assignment Workflow Orchestrator

"As a support agent, I want tickets to be routed automatically and escalated as needed so that I can focus on resolution rather than manual triage."

Description

Automate the end-to-end ticket assignment workflow, including triggering notifications to selected agents, enforcing SLA-based reassignments, and logging all assignment events. This orchestration ensures tickets are routed efficiently, escalated when necessary, and tracked for performance analytics.

Acceptance Criteria

Initial Ticket Assignment Notification

Given a new ticket matching agent expertise is created, when the system processes the ticket, then the ticket is assigned to the best-fit agent and a notification is sent within 2 minutes containing ticket ID, priority, and customer context.

SLA-Based Reassignment Trigger

Given a ticket remains unacknowledged past its SLA threshold, when the SLA breach occurs, then the system automatically reassigns the ticket to the next available qualified agent or escalates to a team lead and records the reassignment event.

Agent Acknowledgement Logging

Given an agent receives a ticket assignment notification, when the agent acknowledges the ticket, then the system logs the acknowledgement timestamp and agent ID in the assignment history within 1 minute of acknowledgement.

Escalation Notification Handling

Given a ticket escalation rule is met (e.g., priority change or no response), when escalation is triggered, then the system sends an escalation notification to the designated team lead and flags the ticket status as escalated.

Assignment Event Audit Trail

Given any assignment action (initial assignment, reassignment, escalation), when the action occurs, then the system logs the event with timestamp, actor type (AI or human), and reason, and makes the audit trail retrievable via API.

LoadBalancer

Continuously monitors real-time agent workloads and dynamically distributes incoming tickets to ensure an even, fair queue—preventing burnout and maintaining rapid response times.

Requirements

Real-Time Workload Monitoring

"As a support manager, I want real-time visibility into each agent’s workload so that I can trust the LoadBalancer to distribute tickets evenly and prevent burnout."

Description

Implement a monitoring module that captures and updates each agent’s active ticket count, chat sessions, and workflow automation tasks in real time. This module integrates with PulseDesk’s ticketing, live chat, and workflow systems to provide continuous visibility into agent capacity. By maintaining up-to-the-second data on agent workloads, the system ensures accurate inputs for ticket distribution, prevents overload, and supports data-driven staffing decisions.

Acceptance Criteria

Initial Agent Dashboard Load

Given the monitoring module is connected to ticketing, chat, and workflow systems, when an agent opens their dashboard, then their active ticket count, chat session count, and workflow task count are displayed within 5 seconds and match the backend data.

Concurrent Ticket Updates

Given two new tickets are assigned to an agent simultaneously, when the assignments occur, then the agent’s active ticket count increments by 2 in the dashboard within 2 seconds.

Workflow Task Completion Sync

Given an agent completes a workflow automation task, when the task status updates in the workflow system, then the completed task is removed from the agent’s active count and the dashboard reflects the change within 2 seconds.

Live Chat Session Tracking

Given a customer initiates a live chat session with an agent, when the chat session is established, then the agent’s active chat session count increases by one in real time on the dashboard.

High Volume Load Test

Given the system processes up to 1000 workload updates per minute across all agents, when subjected to this load, then no data packets are dropped, and all agents’ workload counts remain accurate within a 1% margin of error.

Dynamic Ticket Distribution Algorithm

"As a support team lead, I want tickets to be allocated automatically based on live agent workload so that no single agent becomes overwhelmed."

Description

Design and implement an adaptive allocation algorithm that uses real-time workload metrics to assign incoming tickets. The algorithm evaluates agent availability, current ticket queues, and predefined load thresholds to route new tickets to the most suitable agent. Integration with the core ticketing engine ensures seamless handoff and maintains rapid response times, improving overall team efficiency and customer satisfaction.

Acceptance Criteria

High Volume Ticket Influx

Given a spike of 50+ incoming tickets in 5 minutes When the dynamic algorithm processes each ticket Then no agent’s queue length differs from the mean queue length by more than 10%

Idle Agent Ticket Assignment

Given an agent has been idle for over 2 minutes When a new ticket arrives Then the ticket is assigned to the idle agent immediately

Predefined Load Threshold Breach

Given an agent’s active ticket count exceeds the predefined threshold When the algorithm evaluates agent loads Then no new tickets are routed to that agent until their load falls below the threshold

Skill-Based Ticket Routing

Given a ticket with a specific skill tag (e.g., “Billing”) When the allocation algorithm runs Then only agents with the matching skill are considered and the one with the lowest relative load is assigned the ticket

Agent Offline or Unavailable

Given an agent goes offline or marks themselves unavailable When the distribution algorithm runs Then all tickets in the agent’s queue are redistributed and no new tickets are assigned to that agent

Skill-Based Routing

"As a support lead, I want tickets routed to agents with the right skills so that customers receive knowledgeable, accurate assistance on first contact."

Description

Extend the LoadBalancer to factor in agent skill profiles and ticket metadata (e.g., issue type, priority level). The system matches ticket requirements—such as technical expertise, language proficiency, or product knowledge—with agent qualifications, ensuring that each ticket is handled by the best-fit agent. This feature enhances resolution quality and reduces escalations by leveraging specialized skills.

Acceptance Criteria

Technical Expertise Matching

Given a ticket tagged as ‘backend API issue’ When the LoadBalancer processes the ticket Then it is assigned to an agent whose skill profile includes ‘Backend API’ expertise

Language Proficiency Routing

Given a ticket submitted in French When the LoadBalancer enqueues the ticket Then it assigns it to an agent with verified French language proficiency

Priority Level Alignment

Given a ticket marked Priority 1 When the LoadBalancer evaluates agent workload Then it assigns the ticket to an agent with fewer than three open high-priority tickets

Skill Gap Escalation

Given a ticket requiring a specialized skill not present in any active agent profile When no match is found Then the ticket is flagged for manual review and escalated to the support lead

Real-time Skill Profile Update

Given an agent updates their skill profile in the system When the LoadBalancer makes routing decisions thereafter Then it uses the agent’s new skills for matching incoming tickets

Peak-Time Surge Handling

"As a support operations manager, I want the system to detect and respond to ticket surges automatically so that service levels remain consistent during high-volume events."

Description

Implement thresholds and rules for detecting workload surges during peak periods. When agent capacity approaches defined limits, the LoadBalancer automatically adjusts distribution parameters—such as lowering per-agent ticket caps or enabling overflow queues—to maintain service levels. This capability prevents response delays and preserves team productivity under sudden volume spikes.

Acceptance Criteria

Threshold Detection During Peak Load

Given real-time agent workload data When any agent’s ticket count exceeds 90% of their capacity for more than 2 minutes Then the LoadBalancer automatically lowers the per-agent ticket cap by 25% and diverts new tickets to an overflow queue

Overflow Queue Activation

Given primary queues have reached defined capacity thresholds When a new ticket arrives Then the LoadBalancer routes the ticket to a designated overflow queue and notifies the support lead

Dynamic Distribution Parameter Adjustment

Given a detected surge in incoming ticket volume When overall agent capacity utilization exceeds 85% Then the LoadBalancer updates distribution parameters (e.g., ticket cap and assignment interval) without manual intervention

Surge Response Latency Compliance

Given peak-time conditions when surge thresholds are met When the LoadBalancer processes incoming tickets Then distribution adjustments occur within 30 seconds of threshold breach

Post-Surge Normalization

Given a return to normal ticket volume levels When average agent utilization falls below 70% for 5 consecutive minutes Then the LoadBalancer restores original distribution parameters and disables overflow routing

Workload Alerting and Reporting

"As a support lead, I want alerts and reports on agent workload imbalances so that I can intervene or adjust rules before performance degrades."

Description

Build an alerting and reporting component that notifies support leads when workload imbalances occur or key thresholds are breached. The component generates real-time alerts via email, in-app notifications, or Slack, and produces periodic reports on distribution metrics, agent utilization, and SLA compliance. These insights enable proactive management and continuous optimization of the LoadBalancer feature.

Acceptance Criteria

Immediate Alert on Threshold Breach

Given the LoadBalancer is monitoring agent workloads in real time and an agent’s ticket count imbalance exceeds 20%, When the system detects the threshold breach, Then it shall generate an alert and deliver notifications via email, in-app, and Slack within 60 seconds.

Consolidated In-App Notification Delivery

Given a workload imbalance breach occurs, When the alert is generated, Then an in-app notification shall appear in the support lead’s dashboard within 60 seconds containing the agent with highest load, the imbalance percentage, breach timestamp, and a link to detailed metrics.

Email Notification Delivery to Support Leads

Given a workload threshold breach, When the alert is triggered, Then the system shall send an email to the configured support leads distribution list within 2 minutes, with subject “Workload Imbalance Alert”, body detailing agents’ workload metrics, imbalance percentage, timestamp, and a link to the reporting dashboard.

Slack Notification Channel Integration

Given the support team has configured a Slack channel, When a workload imbalance threshold is breached, Then the system shall post a formatted message to the channel within 2 minutes including breach details, affected agents, imbalance percentage, timestamp, and a direct link to the dashboard.

Weekly Workload Distribution Report

Given the system is scheduled to generate periodic reports, When it is Monday at 08:00 AM UTC, Then the system shall compile a PDF report containing distribution metrics, agent utilization rates, and SLA compliance statistics for the previous week, store it in the reporting repository, and email it to all support leads.

PriorityPulse

Calculates ticket urgency by evaluating SLA deadlines, customer tier, sentiment analysis, and historical trends—elevating high-priority cases in the queue and routing them to rapid-response experts.

Requirements

SLA Deadline Tracker

"As a support lead, I want to monitor tickets with nearing SLA deadlines so that I can prioritize urgent tickets before SLA breaches occur."

Description

Implement a real‐time SLA tracking component that calculates the remaining time for each ticket against its SLA deadline, displays countdown alerts, and flags tickets nearing breach thresholds. This functionality integrates seamlessly into the PriorityPulse urgency engine, ensuring SLA compliance and reducing breach incidents by proactively surfacing at‐risk tickets.

Acceptance Criteria

Countdown Display for Active Tickets

Given an active ticket with an SLA deadline set, when the ticket view is loaded, then a live countdown timer displays the remaining hours, minutes, and seconds until the SLA breach.

Breach Warning Alert for Impending SLA Breach

Given a ticket with less than 30 minutes remaining to SLA breach, when the threshold is reached, then a visual warning (e.g., red highlight and popup) appears in the ticket queue to alert support agents.

Flagging Tickets at Risk in PriorityPulse Queue

Given tickets with remaining time below the configured breach threshold, when the PriorityPulse urgency engine processes the queue, then those tickets are flagged with high-priority status and sorted to the top of the queue.

Automatic SLA Escalation Notification

Given a ticket breaches its SLA, when the breach occurs, then an automatic notification is sent to the designated escalation group within five minutes and the ticket status updates to “Escalated SLA Breach.”

Real-Time SLA Timer Performance Under Load

Given 100 simultaneous active tickets with SLA deadlines, when the SLA tracker updates, then all countdown timers refresh in real-time with no more than a two-second delay under peak load conditions.

Customer Tier Weighting

"As a support lead, I want higher-tier customers to have higher urgency scores so that VIP customers receive timely responses."

Description

Incorporate customer subscription tiers into the urgency calculation by assigning weighted scores based on predefined tier levels (e.g., Gold, Silver, Bronze). This feature enhances PriorityPulse by ensuring high‐value customers receive elevated priority, improving satisfaction and retention.

Acceptance Criteria

Gold Tier Ticket Prioritization

Given two tickets with identical SLA deadlines, sentiment analysis scores, and historical trends—one from a Gold-tier customer and one from a Silver-tier customer—when PriorityPulse calculates urgency, the Gold-tier ticket’s urgency score must be higher, and it must appear before the Silver-tier ticket in the sorted queue.

Silver Tier Ticket Prioritization

Given two tickets with identical SLA deadlines, sentiment scores, and historical trends—one from a Silver-tier customer and one from a Bronze-tier customer—when urgency is calculated, the Silver-tier ticket’s urgency score must be higher, and it must be prioritized over the Bronze-tier ticket.

Tier Weight Configuration Enforcement

Given the configured subscription tier weights (Gold=3, Silver=2, Bronze=1), when a ticket’s urgency is calculated, the system must retrieve and apply the correct weight from the configuration to the urgency calculation formula.

Weighted Urgency Score Accuracy

Given a ticket with a base urgency score of 40, SLA adjustment of +10, sentiment adjustment of +5, and Bronze-tier weight of 1, when the final urgency score is computed, it must equal (40 + 10 + 5) * 1 = 55.

Missing Tier Configuration Handling

Given a ticket from a customer whose subscription tier is not defined in the weighting configuration, when urgency is calculated, the system must default to the Bronze-tier weight and record a warning in the audit log.

Sentiment Score Analyzer

"As a support agent, I want the system to analyze customer sentiment so that negative interactions are escalated quickly."

Description

Develop a natural language processing module that analyzes incoming ticket text and chat transcripts to determine customer sentiment (positive, neutral, negative) in real time. Integrate sentiment scores into the urgency algorithm to surface emotionally charged or critical tickets for faster resolution.

Acceptance Criteria

New Ticket Sentiment Evaluation

Given a newly submitted ticket containing clear positive or negative language, when processed by the Sentiment Score Analyzer, then the system classifies the sentiment as 'Positive', 'Neutral', or 'Negative' with at least 90% accuracy against human-labeled benchmarks.

Live Chat Transcript Sentiment Analysis

Given an active live chat session with multiple customer messages, when the Sentiment Score Analyzer runs in real time, then it updates the sentiment score every 15 seconds and generates an alert if the sentiment turns from neutral or positive to negative.

Multi-Language Sentiment Support

Given tickets written in English, Spanish, or French, when analyzed, then the system correctly identifies sentiment polarity with at least 85% accuracy for each supported language.

Sentiment Score Integration with Priority Algorithm

Given a sentiment score and SLA deadline details, when computing ticket priority, then the urgency algorithm elevates tickets with negative sentiment one priority level above neutral tickets with identical SLA deadlines.

Ambiguous or Mixed Sentiment Handling

Given a ticket containing both positive and negative statements, when processed, then the system flags the ticket as 'Mixed Sentiment' for manual review and does not automatically alter its urgency score.

Historical Trend Correlation

"As a support manager, I want the urgency engine to consider past resolution patterns so that recurring issues are prioritized based on impact."

Description

Build a historical analytics engine that reviews past ticket resolution times, frequency of issue types, and seasonal trends to adjust urgency scores. By correlating current tickets with historical data, PriorityPulse can identify recurring high‐impact issues and prioritize them proactively.

Acceptance Criteria

Correlating Ticket with Past Issue Resolution Times

Given a new ticket is created, when the system retrieves historical resolution times for the same issue type, then the urgency score is adjusted by at least 10% in proportion to deviation from the average resolution time recorded in the past six months.

Identifying Seasonal Surge Patterns

Given the current date falls within a historically high-volume period, when the system analyzes past monthly ticket volumes for the same period, then the urgency score for matching issue types increases by at least the average percentage surge observed over the past two years.

Detecting Recurring High-Impact Issues

Given the system detects that the current ticket’s issue type has recurred in at least five tickets over the last 30 days, when analyzing issue frequency, then the ticket is flagged as high-impact and automatically routed to the rapid-response queue.

Validating Urgency Score Adjustment

Given a ticket is processed through the historical analytics engine, when urgency is recalculated, then the new urgency score differs from the original score and the system logs the historical factors and calculation details for audit purposes.

Prioritizing Tickets Based on Historical Frequency

Given multiple tickets of the same issue type in the queue, when sorting by urgency score, then tickets with issue types that have appeared more than 10 times in the last month are placed within the top 20% of the queue.

Expert Routing Mechanism

"As a support agent, I want the system to automatically assign urgent tickets to the right expert so that they are resolved faster."

Description

Create an automated routing system that matches high‐priority tickets to specialized support experts based on skill tags, past performance, and real‐time availability. This ensures rapid assignment and resolution by the most qualified personnel.

Acceptance Criteria

Routing High-Priority Ticket to Available Expert

Given a ticket flagged as high-priority with SLA breach imminent and skill tag 'networking', When the routing engine processes the ticket, Then the system assigns it to an 'Available' expert with the 'networking' tag within 5 seconds.

Fallback to Next Available Expert When Primary is Busy

Given a high-priority ticket and the primary expert is currently handling the maximum number of tickets, When routing occurs, Then the ticket is automatically assigned to the next available expert matching the required skill tag, ensuring no expert exceeds 5 concurrent tickets.

Skill Tag Matching Accuracy

Given a ticket requiring 'database' and 'security' skills, When selecting an expert, Then the system assigns the ticket only to experts possessing both 'database' and 'security' tags, and if none exist, flags the ticket for manual review within 1 minute.

Real-Time Expert Availability Update

Given an expert changes status to 'Unavailable' during routing, When the status update occurs, Then any unassigned or pending tickets are removed from their queue and re-routed to other available qualified experts within 2 seconds.

Historical Performance-Based Expert Selection

Given multiple available experts with matching skill tags, When choosing among them, Then the system assigns the ticket to the expert with the highest resolution rate over the past 30 days and the fewest tickets in queue.

EscalateGuard

Automatically flags tickets trending toward SLA breaches or negative sentiment and reroutes them to senior or specialized agents for immediate attention—safeguarding customer satisfaction.

Requirements

Real-Time Ticket Monitoring

"As a support lead, I want real-time monitoring of ticket statuses so that I can identify tickets at risk of SLA breaches before they occur."

Description

Continuously scans incoming and ongoing tickets in real time to detect SLA thresholds approaching breach conditions or changes in customer sentiment. It integrates with the core ticketing module, leveraging event-driven architecture to stream ticket updates and deliver near-instant analysis. This relentless monitoring ensures potential issues are flagged immediately, enabling proactive intervention and minimizing risk of SLA violations.

Acceptance Criteria

Ticket Approaching SLA Breach

Given a ticket has an SLA threshold set, When the remaining time to breach is less than or equal to 5 minutes, Then the system flags the ticket and sends an alert to the assigned agent.

Negative Sentiment Detected

Given an ongoing ticket conversation, When a new customer message receives a sentiment score below -0.5, Then the system flags the ticket and routes it to a senior agent.

Ongoing Ticket Update SLA Check

Given a ticket being updated, When the update causes the SLA breach threshold to be crossed, Then the system instantly triggers the escalation workflow.

Multiple SLA Threshold Crossing

Given multiple tickets monitored concurrently, When two or more tickets cross their SLA warning thresholds within 1 minute, Then the system batches alerts and notifies the support lead.

Rapid Sentiment Shift

Given a ticket conversation that was neutral or positive, When two consecutive messages show sentiment shifting from positive to negative, Then the system immediately escalates the ticket to specialized support.

Sentiment Analysis Integration

"As a support agent, I want tickets to be automatically scored for sentiment so that negative interactions are escalated before the issue worsens."

Description

Implements sentiment detection by analyzing ticket conversation content using natural language processing APIs. It assesses tone, word choice, and response patterns to gauge customer satisfaction levels. Integrated seamlessly with the ticketing system, it tags tickets with sentiment scores and updates them dynamically, allowing the escalation engine to account for negative sentiment alongside SLA criteria.

Acceptance Criteria

Ticket Sentiment Tagging

Given a new ticket message is received When the system invokes the sentiment analysis API Then a sentiment score between -1 and 1 is assigned and a sentiment tag (Positive, Neutral, Negative) is applied within 2 seconds and visible in the ticket UI

Dynamic Sentiment Score Updates

Given an ongoing ticket conversation When an agent or customer sends a follow-up message Then the sentiment score is recalculated and updated in the ticket metadata within 3 seconds reflecting the latest message

Negative Sentiment Escalation Trigger

Given a ticket’s sentiment score falls to or below -0.5 When the sentiment tag becomes Negative Then the ticket is flagged for EscalateGuard, moved to the high-priority queue, and a notification is sent to the senior support team within 1 minute

Sentiment Analysis API Failure Handling

Given the sentiment API call fails or times out When the system encounters an error Then it retries up to 2 times within 30 seconds, logs the error with the ticket ID, and defaults the sentiment tag to Unknown without blocking ticket processing

Sentiment Threshold Configuration

Given an administrator updates sentiment threshold settings When thresholds are saved Then new tickets use the updated thresholds for classification and existing tickets refresh their sentiment tags accordingly

SLA Breach Prediction Engine

"As a support manager, I want the system to predict SLA breaches based on ticket history so that I can allocate resources to prevent violations."

Description

Builds a predictive analytics component that uses historical ticket resolution times and current workflow metrics to forecast tickets likely to miss SLA targets. It runs periodic batch jobs and real-time calculations, providing risk scores for each ticket. This predictive insight empowers the escalation logic to target high-risk tickets proactively.

Acceptance Criteria

Ticket Risk Score Generation

Given a newly created ticket with historical resolution times and current workflow metrics, when the SLA Breach Prediction Engine runs, then the system assigns a risk score between 0 (low risk) and 100 (high risk) to the ticket.

Periodic Batch Prediction

At 02:00 UTC daily, when the batch prediction job executes, then the engine processes all open tickets and updates their risk scores within five minutes of job completion.

Real-Time Risk Update

Given a ticket's workflow metrics change (e.g., priority or time in queue), when the update is received, then the engine recalculates and updates the ticket’s risk score within one minute.

Risk Threshold Alert Trigger

Given a ticket’s risk score exceeds the configurable escalation threshold (e.g., 80), when the score is updated, then the system raises an alert and flags the ticket for senior agent escalation.

Data Quality Validation

Before calculating a risk score, when the prediction engine retrieves ticket data, then it validates that all required fields (creation time, priority, status) are present; any ticket with missing data is logged and excluded from scoring.

Automated Escalation Workflow

"As a support lead, I want tickets meeting escalation criteria to be automatically routed to specialized agents so that critical issues receive immediate attention."

Description

Defines and automates escalation rules that trigger when tickets meet specific criteria such as high-risk SLA status or negative sentiment. It routes flagged tickets to designated senior or specialized agents, notifies stakeholders, and logs escalation history. Configurable within the no-code workflow builder, it ensures seamless integration with existing support flows.

Acceptance Criteria

SLA Breach Imminent Ticket Escalation

Given a ticket’s SLA timer displays 30 minutes or less remaining; When the timer reaches 15 minutes without resolution; Then the system automatically flags the ticket for escalation and assigns it to a senior agent within the designated team; And an escalation notification is sent to the senior agent and original ticket owner within one minute

Negative Sentiment Ticket Escalation

Given a ticket’s sentiment analysis score falls below –0.5; When the latest customer message triggers negative sentiment detection; Then the system flags the ticket and reroutes it to a specialized agent; And updates the ticket status to 'Escalated' in the ticketing dashboard

Custom Escalation Rule Configuration in Workflow Builder

Given the user opens the no-code workflow builder; When the user defines a new escalation rule with specified SLA threshold or sentiment threshold and selects the target agent group; Then the rule is saved and listed in the workflow configuration; And the system applies the rule to incoming tickets matching the criteria

Stakeholder Notification on Escalation

Given a ticket has been escalated; When the escalation event occurs; Then the system sends email and Slack notifications to all configured stakeholders within two minutes; And includes ticket ID, escalation reason, and assigned agent details

Escalation History Logging

Given an escalated ticket; When the escalation is triggered; Then the system creates an escalation history entry recording timestamp, trigger condition, previous assignee, and new assignee; And displays this history in the ticket’s audit log

Escalation Dashboard & Alerting

"As a support lead, I want a dashboard that shows the status of escalated tickets and alerts so that I can monitor team performance and customer issues at a glance."

Description

Provides a centralized dashboard displaying real-time metrics on escalated tickets, pending SLA breaches, and sentiment trends. It includes customizable alerts via email, SMS, or in-app notifications for support leads and agents. The dashboard offers drill-down capabilities and historical reporting to track escalation performance and customer satisfaction over time.

Acceptance Criteria

Real-Time Escalation Dashboard Overview

Given escalated tickets exist, When the support lead accesses the Escalation Dashboard, Then the dashboard displays all escalated tickets, pending SLA breaches, and sentiment trends updated within 60 seconds.

Custom Alert Configuration

Given a support lead configures alert thresholds for SLA breaches and negative sentiment, When the thresholds are met, Then the system sends alerts via the selected channels (email, SMS, or in-app) within 2 minutes.

In-App Notification Delivery

Given a ticket triggers an escalation condition (pending SLA breach or negative sentiment), When EscalateGuard flags the ticket, Then an in-app notification appears for the assigned agent within 30 seconds.

Historical Escalation Reporting

Given a support lead selects a date range for escalation reporting, When the report is generated, Then the system provides accurate historical data on escalated tickets, SLA breach rates, and sentiment trends, and allows export as CSV or PDF.

Sentiment Trend Drill-Down

Given the Escalation Dashboard displays sentiment trend charts, When the support lead clicks on a specific trend segment, Then the system presents a detailed list of associated tickets with sentiment scores and timestamps.

RouteRefine

Continuously learns from resolution times, agent performance, and customer feedback to refine routing algorithms—improving assignment accuracy and adapting to evolving support patterns.

Requirements

Historical Data Ingestion

"As a support operations manager, I want to ingest historical ticket data so that the routing algorithm can learn from past performance patterns."

Description

System must regularly import and normalize past support ticket data, including timestamps, agent IDs, resolution durations, customer satisfaction scores, and routing paths, delivering a consistent dataset for continuous model training and analysis without impacting platform performance.

Acceptance Criteria

Scheduled Historical Data Import Initiation

Given the data ingestion schedule is configured, when the scheduled time arrives, then the system automatically starts importing the specified historical ticket data without manual intervention.

Data Normalization Verification

Given raw support ticket data has been imported, when normalization routines execute, then all timestamps, agent IDs, resolution durations, customer satisfaction scores, and routing paths match the defined schema formats and accepted value ranges.

Performance Impact Monitoring

Given historical data ingestion is running concurrently with normal platform operations, when import is in progress, then CPU and memory usage remain below 80% and average response time for user interactions stays under 200ms.

Incremental Data Sync Handling

Given new or updated ticket records exist since the last import, when the incremental import job runs, then only those new or updated records are ingested and no duplicate records appear in the normalized dataset.

Error Handling and Retries

Given a transient failure occurs during data import, when the import process fails, then the system retries the operation up to three times with exponential backoff and logs each attempt, sending an alert to the admin if all retries fail.

Performance Tracking Metrics

"As a data analyst, I want key performance metrics available via API so that I can monitor agent efficiency and feed insights into the routing model."

Description

System should calculate and store key metrics such as average resolution time per agent, ticket complexity levels, first response times, and customer satisfaction ratings, exposing them via secure APIs for algorithmic refinement and dashboard visualization.

Acceptance Criteria

Real-Time Average Resolution Time Calculation

Given a ticket has been resolved, when 10 tickets have resolution timestamps within a 24-hour window, then the system calculates the average resolution time with an accuracy of ±1 minute.

Ticket Complexity Level Classification

Given historical ticket data inputs, when a ticket is logged, then the system classifies its complexity level into 'Low', 'Medium', or 'High' based on predefined parameters with 95% accuracy.

First Response Time Exposure via API

Given a valid API request with proper authentication, when fetching first response time for a ticket, then the API returns the metric in milliseconds within 200ms and with HTTP status 200.

Customer Satisfaction Rating Storage

Given a customer submits a satisfaction rating after ticket closure, when the rating is recorded, then the system persists the rating within 500ms and makes it retrievable via API.

Secure API Access Verification

Given an API request without a valid access token, when requesting performance metrics, then the system responds with HTTP 401 Unauthorized and no data exposure.

Adaptive Routing Algorithm

"As a support team lead, I want the routing algorithm to adapt assignments in real time based on performance data so that tickets are handled by the most qualified agents."

Description

Implement a machine learning–based routing algorithm that dynamically adjusts assignment weights based on real-time and historical performance data, customer priority, and agent skill profiles to improve match accuracy, balance workload, and reduce resolution times.

Acceptance Criteria

Initial Assignment Weight Calculation

Given a new support ticket with defined customer priority and required skill tags When the Adaptive Routing Algorithm processes the ticket Then it calculates assignment weights for all eligible agents based on historical resolution times, performance scores, and skill match accuracy within 5% of the benchmark

Real-Time Performance Adjustment

Given an agent’s resolution time deviates by more than 20% from their average When the algorithm runs periodic recalibration Then it updates the agent’s weight contribution in less than 2 minutes and logs the adjustment for audit

High-Priority Ticket Routing

Given a ticket marked as high-priority by the customer When the algorithm evaluates routing Then it ensures assignment to an agent whose combined priority-handling score is in the top 10% within the system

Workload Balancing Verification

Given multiple concurrent tickets queued for assignment When the algorithm assigns tickets Then no agent receives more than 20% above their average open-ticket load and workload distribution variance remains under 10%

Fallback Agent Selection

Given all primary matching agents are unavailable When the routing algorithm must assign the ticket Then it selects an alternate agent with the next highest combined suitability score and records the fallback reason

Feedback Loop Integration

"As a support agent, I want my feedback on ticket resolutions captured automatically so that future assignments reflect real-world preferences and satisfaction."

Description

Develop a feedback mechanism to capture post-resolution input from customers and agents, automatically tagging sentiments and issue outcomes, then feeding these annotations into the model training pipeline to refine routing decisions and adapt to evolving support patterns.

Acceptance Criteria

Customer Feedback Submission Prompt

Given a ticket status is “Resolved” When the customer views the ticket Then a feedback prompt (1–5 rating plus comment field) is displayed and feedback is stored successfully in the database

Agent Feedback Input Interface

Given an agent closes a ticket When prompted in the agent dashboard Then the agent can select sentiment tags (positive/neutral/negative) and add outcome notes and save them without errors

Sentiment Tagging Accuracy

Given collected feedback entries When the sentiment analysis runs Then at least 90% of sentiment tags match human review and discrepancies are logged for review

Automatic Annotation Export to Model Training

Given new feedback annotations exist When the nightly pipeline executes Then annotations are exported to the model training data store in the required JSON schema and no export errors occur

Feedback-Based Routing Adjustment

Given updated model training data When the routing model retrains Then routing accuracy improves by at least 5% on validation data and the new model is deployed without rollback

Configuration Dashboard

"As a SaaS support lead, I want a configuration dashboard so that I can fine-tune routing parameters without writing code."

Description

Provide an intuitive no-code dashboard within PulseDesk where support leads can review routing analytics, adjust algorithm weighting factors (e.g., resolution speed vs. expertise), configure retraining thresholds, and preview projected routing changes before deployment.

Acceptance Criteria

Dashboard Access and Load Performance

Given a logged-in support lead When they navigate to the Configuration Dashboard Then the dashboard loads within 2 seconds and displays all sections: routing analytics, algorithm weighting sliders, retraining threshold settings, and preview panel

Adjusting Algorithm Weighting

Given the weighting sliders for resolution speed and expertise When the support lead adjusts the sliders and clicks Save Then the new weighting factors persist in the system and reflect in the preview panel within 1 second

Configuring Retraining Thresholds

Given the retraining threshold settings panel When the support lead enters valid threshold values and confirms Then the system schedules retraining according to the new thresholds and displays a confirmation message

Previewing Routing Changes

Given updated weighting and threshold settings When the support lead clicks Preview Then the system displays projected routing changes with comparative metrics (e.g., predicted resolution time, agent load) for the next 7 days

Handling Invalid Input

Given invalid input values (e.g., out-of-range weighting, non-numeric threshold) When the support lead attempts to save Then inline validation errors display next to each invalid field and prevent saving until corrected

ForecastFlow

Leverages predictive analytics to forecast upcoming CSAT trends based on historical interaction data, helping support leads anticipate satisfaction shifts and proactively allocate resources before issues arise.

Requirements

Data Ingestion Pipeline

"As a support lead, I want historical interaction data automatically collected and normalized so that the forecasting engine has reliable inputs for accurate CSAT predictions."

Description

Implement a robust pipeline to aggregate and normalize historical customer interaction data from live chat, ticketing, and workflow logs. This pipeline will ensure data consistency, support scalable processing, and serve as the foundation for accurate CSAT trend forecasting within PulseDesk’s ecosystem.

Acceptance Criteria

Historical Data Extraction

- Given the pipeline is triggered, when historical data extraction starts for live chat, ticketing, and workflow logs, then the system must retrieve all records from the specified date range without errors. - Given network interruptions occur, when the pipeline retries extraction, then it should resume without duplicating records and complete within configured retry limits.

Data Normalization Consistency

- Given raw interaction records with varying timestamp formats, when normalization runs, then all timestamps must be converted to ISO 8601. - Given different user ID formats, when normalization runs, then user identifiers must align to the unified alphanumeric schema.

Scalable Processing Performance

- Given one million records, when processed, then the pipeline must complete aggregation and normalization within 30 minutes. - Given the data volume doubles, when processed, then the overall processing time increases by no more than 50% of the baseline.

Error Handling and Recovery

- Given a record fails validation, when encountered, then the pipeline logs the error, skips the record, and continues processing. - Given an upstream API is unavailable, when extraction is attempted, then the pipeline retries up to three times with exponential backoff and reports status after failure.

Data Integrity Verification

- Given normalization is complete, when integrity checks run, then no duplicate records exist based on unique transaction IDs. - Given critical fields are missing, when verified, then the pipeline flags records and moves them to a quarantine dataset.

Predictive Analytics Engine

"As a support manager, I want a predictive engine to forecast upcoming CSAT trends so that I can anticipate satisfaction shifts and proactively adjust resources."

Description

Develop and integrate a machine learning engine that applies time-series forecasting techniques on normalized interaction data to predict future CSAT scores. The engine should retrain models periodically, handle data drift, and expose APIs for real-time and batch forecast requests.

Acceptance Criteria

Real-time Forecast Request Processing

Given valid normalized interaction data is received via the real-time API endpoint, When a forecast request is submitted, Then the engine returns a CSAT prediction within 500ms with a confidence score.

Batch Forecast Generation

Given a batch data file of historical interactions uploaded to the batch API, When processing completes, Then a CSV file containing CSAT forecasts for each time interval is generated and made available for download.

Model Retraining Schedule Execution

Given 24 hours have elapsed since the last training, When the scheduled retraining job runs, Then the model is retrained on the latest normalized data and the new model is deployed without errors.

Data Drift Detection and Handling

Given new incoming interaction data distribution diverges by more than 10% from training data, When drift threshold is exceeded, Then the system logs an alert and pauses forecasting until retraining completes.

API Error Handling and Validation

Given invalid or incomplete request payloads to the forecasting API, When the request is processed, Then the API returns a 4xx error with a descriptive message.

Forecast Accuracy Monitoring

Given actual CSAT scores become available, When compared against predicted values, Then the system computes mean absolute percentage error (MAPE) weekly and logs a report if MAPE exceeds 5%.

Trend Visualization Dashboard

"As a support lead, I want to see visual forecasts of CSAT trends alongside historical data so that I can quickly interpret predictions and share insights with my team."

Description

Create an interactive dashboard within PulseDesk that displays forecasted CSAT trends, confidence intervals, and historical performance side by side. The dashboard will offer filtering by time period, team, and ticket category, enabling leads to drill down into projected satisfaction metrics.

Acceptance Criteria

Overall CSAT Trend Overview

Given the user opens the Trend Visualization Dashboard When no filters are applied Then the dashboard displays the combined historical CSAT trend and forecasted CSAT trend for the default time period

Time Period Filter Application

Given the user selects a custom time period filter When the filter is applied Then the dashboard updates both historical and forecasted CSAT charts to reflect data within the selected range

Team-Based Trend Drill-Down

Given the user chooses a specific support team from the team filter dropdown When the team is selected Then the dashboard displays CSAT trends and forecast data only for that team, and updates confidence intervals accordingly

Ticket Category Breakdown

Given the user applies a ticket category filter When a category is selected Then the historical and forecasted CSAT metrics update to show trends specific to that ticket category

Confidence Interval Display

Given the dashboard is rendering forecast data When confidence intervals are toggled on Then shaded bands representing upper and lower confidence bounds are displayed around the forecasted CSAT line

Proactive Alert Notifications

"As a customer support lead, I want to receive alerts when CSAT forecasts exceed risk thresholds so that I can take proactive measures before satisfaction significantly changes."

Description

Implement a notification system that triggers alerts when forecasted CSAT drops or spikes cross predefined thresholds. Alerts can be delivered via email, in-app notifications, or integrated chat channels, allowing support leads to respond promptly to anticipated changes.

Acceptance Criteria

Email Alert Triggered by CSAT Drop

Given a support lead has configured a CSAT drop threshold at 10% When the predictive analytics engine forecasts a drop exceeding 10% within the next 24 hours Then an email alert is automatically sent to the support lead’s registered email address with the forecast details and recommended actions

In-App Notification for CSAT Spike

Given the support lead is logged into PulseDesk’s dashboard When the forecasted CSAT spike exceeds the predefined upper threshold Then an in-app notification banner appears immediately with the spike percentage and a link to view detailed analytics

Chat Channel Alert Integration

Given the support lead has linked a Slack channel for alerts When a forecasted CSAT threshold breach (drop or spike) occurs Then a formatted message is posted in the linked Slack channel containing the alert type, forecast value, timestamp, and link to the forecast report

Threshold Configuration Validation

Given a support lead is defining alert thresholds in the settings panel When they input a threshold value outside the allowable range (0–100%) Then the system prevents saving, displays an inline error message, and highlights the invalid field

Alert Delivery Retry Mechanism

Given an email alert delivery attempt fails due to a transient error (e.g., SMTP timeout) When the system detects the delivery failure Then the system retries sending the alert up to 3 times at 5-minute intervals and logs each attempt

Resource Recommendation Engine

"As a support operations manager, I want actionable resource allocation suggestions based on CSAT forecasts so that I can optimize staffing and workflows ahead of anticipated demand."

Description

Build a recommendation module that analyzes forecasted CSAT shifts and suggests resource allocation adjustments—such as shifting agents between queues or initiating workflow automations—to mitigate predicted dips or capitalize on positive trends.

Acceptance Criteria

Identify CSAT Dip Predictions

Given the forecast indicates a CSAT decline of 10% or more in the next hour, when the recommendation module runs, then it suggests reallocating at least two agents to the impacted queues.

Capitalize on Positive CSAT Trends

Given the forecast shows a CSAT improvement of 5% or more in the next two hours, when the recommendation module runs, then it suggests reducing automated follow-ups by 20% and reallocating agents to higher-priority tasks.

Workflow Automation Trigger for Predicted Dips

When a predicted CSAT dip exceeds the defined threshold, then the engine automatically initiates a workflow that escalates high-value tickets to senior agents within 5 minutes.

Recommendation Generation Latency

Given a batch of forecasted CSAT trends, when the recommendation engine processes them, then it generates and logs all resource adjustment suggestions within 3 seconds.

In-App Recommendation Notification

When new allocation recommendations are generated, then the user interface displays a real-time notification badge and provides detailed recommendation insights within 2 seconds of generation.

Spotlight Leaderboard

Presents a dynamic, filterable leaderboard showcasing top-performing agents by CSAT scores, response times, and improvement rates, motivating teams through friendly competition and recognition.

Requirements

Real-time Metrics Aggregation

"As a support lead, I want the leaderboard to update automatically with the latest agent metrics so that I can trust that I’m reviewing current performance at any moment."

Description

Implement a back-end service that collects, processes, and normalizes CSAT scores, response times, and improvement rates in real time from all active support channels, ensuring the leaderboard always reflects the latest performance data without manual refresh.

Acceptance Criteria

Continuous Data Ingestion from Support Channels

Given the data ingestion service is running When new chat or ticket events are generated from any active support channel Then the service must ingest and store each raw event within 2 seconds of its creation

Consistent Metrics Normalization

Given ingested raw data for CSAT scores, response times, and improvement rates When the normalization logic is applied Then all CSAT values must be percentages between 0 and 100, response times in seconds with two-decimal precision, and improvement rates as percentages with one-decimal precision

Instant Leaderboard Data Refresh

Given normalized performance metrics are available When an agent’s CSAT, response time, or improvement rate changes Then the leaderboard API must reflect the updated rankings within 5 seconds without requiring a manual refresh

Robust Error Handling and Failover Mechanism

Given a failure or timeout from any support channel API during ingestion When the service attempts to retrieve data Then it must retry up to 3 times with exponential backoff, log each failure, and continue processing other channels without data loss

High Throughput and Low Latency Processing

Given a peak load of 1000 events per second per channel When processing incoming events through the ingestion and normalization pipeline Then 95% of events must complete ingestion to normalized output within 3 seconds

Dynamic Filtering and Sorting

"As a support manager, I want to filter and sort the leaderboard by specific teams and time periods so that I can pinpoint performance trends and identify top contributors in a given context."

Description

Provide flexible filter and sort controls allowing users to segment the leaderboard by date range, team, ticket type, or custom tags, and to order results by any performance metric, enabling focused analysis of agent performance across different dimensions.

Acceptance Criteria

Filter by Date Range Scenario

Given the user opens the Spotlight Leaderboard with default full data When the user selects a valid start date and end date Then the leaderboard entries update to display only agents with tickets within the selected date range And performance metrics recalculate based on the filtered data

Sort by Performance Metric Scenario

Given the user is viewing the leaderboard When the user selects a performance metric (e.g., Response Time) and sort order (ascending or descending) Then the leaderboard rearranges entries accordingly and the first page of results is displayed

Filter by Team Membership Scenario

Given multiple teams appear in the leaderboard When the user filters by a specific team Then only agents assigned to that team are displayed And existing date and sort filters remain applied

Filter by Custom Tags Scenario

Given agents on the leaderboard have custom tags applied When the user selects one or more custom tags in the filter panel Then only agents with all selected tags are shown And the count of visible agents reflects the filtered selection

Combine Multiple Filters Scenario

Given the user has applied date range, team, and custom tag filters When the user adds a sort by CSAT score Then the leaderboard displays agents matching all selected filters in the specified sort order and active filter badges are shown

Interactive Leaderboard UI

"As an agent, I want an intuitive leaderboard interface that shows my current standing and metric breakdowns so that I can quickly understand where I excel and where I need to improve."

Description

Design and build a responsive, user-friendly UI component that displays ranked agent entries with avatars, key metrics, and visual indicators for rank changes, supporting hover details and mobile compatibility to engage users and enhance usability.

Acceptance Criteria

Agent views real-time leaderboard on desktop

Given the support lead is logged into PulseDesk on a desktop browser When the Spotlight Leaderboard component loads Then the leaderboard displays within 2 seconds with ranked agent entries, avatars, key metrics (CSAT, response time, improvement rate), and visual indicators for rank changes

Agent filters leaderboard by CSAT scores

Given the support lead clicks the CSAT filter option When the filter is applied Then only agents with CSAT scores within the selected range are displayed, list order updates accordingly, and total count reflects filtered results

Agent accesses leaderboard on mobile device

Given the support lead opens PulseDesk on a mobile device When the Spotlight Leaderboard is displayed Then the UI adapts to mobile screen size, entries remain readable, avatars and metrics align correctly, and touch interactions function without overflow or clipping

Agent observes rank change indicators

Given updated ticket data is received in real-time When an agent’s rank changes Then a green upward arrow or red downward arrow appears next to their rank, with a tooltip on hover or tap explaining the change

Agent hovers or taps an entry for detailed metrics

Given the support lead hovers over (desktop) or taps (mobile) an agent’s entry When the hover or tap occurs Then a detail panel appears showing recent ticket count, average resolution time, and monthly improvement trend within 1 second

Configurable Ranking Criteria

"As a product owner, I want to configure how metrics are weighted in the leaderboard calculation so that we can align the ranking criteria with evolving business goals."

Description

Enable administrators to customize which metrics (CSAT, response time, improvement rate, or weighted combinations) determine leaderboard rankings, with an intuitive settings panel to define weightings and thresholds for each metric.

Acceptance Criteria

Accessing the Ranking Configuration Panel

Given an administrator is logged into PulseDesk and navigates to the Spotlight Leaderboard settings When they select “Configure Ranking Criteria” Then a panel displays with options for CSAT, response time, improvement rate, and custom weighted combinations

Configuring Metric Weights

Given the ranking configuration panel is open When the administrator adjusts weight sliders or inputs percentages for each metric and the total equals 100% Then the system enables the “Save” button and displays a real-time summary of weighted contributions

Defining Metric Thresholds

Given the administrator views threshold settings When they enter minimum and maximum values for CSAT, response time, or improvement rate Then invalid entries outside allowed ranges trigger inline validation errors and valid entries are accepted

Previewing Leaderboard with Configured Criteria

Given the administrator has set weights and thresholds When they click “Preview” Then the leaderboard updates instantly using sample ticket data to reflect new ranking order and highlights any agents falling below defined thresholds

Persisting and Applying Configuration

Given the administrator saves their customized ranking criteria When they refresh the page or return later Then the previously configured weightings and thresholds are persisted and applied to the live Spotlight Leaderboard for all users

Performance Recognition Notifications

"As an agent, I want to receive notifications when I top the leaderboard so that I feel recognized for my performance and stay motivated."

Description

Implement an automated notifications system that flags top-performing agents weekly and monthly, sending in-app alerts and email summaries to celebrate achievements and encourage healthy competition.

Acceptance Criteria

Weekly In-App Recognition Notification

Given it is the first business day after week-end and weekly performance metrics are available When the automated notification job executes Then the top five agents by CSAT score and response time receive an in-app alert showing their rank, CSAT score, response time, and improvement rate for the week

Weekly Email Recognition Summary

Given weekly performance metrics have been calculated When the weekly email summary job runs Then each of the top five agents receives an email within 15 minutes containing their name, rank, CSAT score, response time, improvement rate, and a congratulatory message; and the email includes an unsubscribe link

Monthly In-App Recognition Notification

Given it is the first business day of the new month and monthly performance metrics are available When the monthly notification job executes Then the top ten agents by combined CSAT score and response time receive an in-app alert showing their rank, monthly averages, improvement rate, and comparison to the previous month

Monthly Email Recognition Summary

Given monthly performance metrics have been calculated When the monthly email summary job runs Then each of the top ten agents receives an email within 30 minutes containing their rank, monthly CSAT average, response time average, improvement rate versus previous month, and a link to detailed performance dashboard

Notification Preferences Configuration

Given an agent accesses their notification settings When they enable or disable weekly or monthly in-app or email notifications Then the system saves their preferences and only sends notifications according to their selected channels and frequencies

Role-based Access Control

"As an administrator, I want to restrict leaderboard data access based on user roles so that sensitive performance metrics remain secure and only visible to authorized personnel."

Description

Integrate leaderboard visibility and interaction permissions with existing user roles to ensure that support leads, managers, and agents see appropriate data scopes and settings, preventing unauthorized access to sensitive performance information.

Acceptance Criteria

Support Lead Viewing All Agent Metrics

Given I am a user with the support lead role, when I navigate to the Spotlight Leaderboard page, then I see metrics for all agents (CSAT scores, response times, and improvement rates) across all teams.

Manager Adjusting Leaderboard Filters

Given I am a user with the manager role assigned to Team A, when I apply filters on the Spotlight Leaderboard, then only Team A's agent metrics are displayed and my filter selections persist across sessions.

Agent Restricted from Viewing Sensitive Data

Given I am a user with the agent role, when I access the Spotlight Leaderboard, then I only see my own performance metrics and cannot view other agents’ data or any management settings.

Unauthorized User Access Denied

Given I am a user without any leaderboard role, when I attempt to access the Spotlight Leaderboard URL directly, then I receive a 403 Forbidden error and the leaderboard page does not load.

Role Change Reflects in Access Permissions

Given my user role is changed from agent to support lead, when I next access the Spotlight Leaderboard, then I immediately see the expanded metrics and filter options available to support leads without requiring additional configuration.

DrillDown Analytics

Enables users to click into any CSAT graph to explore granular details—such as individual interactions, customer segments, or support channels—so teams can pinpoint the exact drivers behind satisfaction trends.

Requirements

Interactive CSAT Graph Drill-Down

"As a support lead, I want to click on any segment of the CSAT graph to see the specific interactions behind the data so that I can identify trends and address issues faster."

Description

Enables users to click on any point or segment in the CSAT overview graph to reveal a secondary view showing a table of individual ticket interactions that contributed, including timestamps, ratings, and agent names. This interactive functionality integrates seamlessly with the existing analytics dashboard, allowing support teams to quickly identify patterns and outliers driving satisfaction trends, reducing time spent on manual data queries and improving decision-making precision.

Acceptance Criteria

CSAT Graph Drill-Down Access

Given the user is on the CSAT overview graph, when the user clicks on a specific data point or segment, then a secondary drill-down table view is displayed.

Drill-Down Table Data Accuracy

The drill-down table displays only interactions contributing to the selected CSAT segment, with correct timestamps, ratings, and agent names matching source data.

Drill-Down Table Performance

When the user requests a drill-down on datasets up to 500 records, the table loads and renders fully within 2 seconds.

Drill-Down Table Column Functionality

Users can sort the drill-down table by timestamp, rating, and agent name in both ascending and descending order without data discrepancies.

Drill-Down Table Pagination

For datasets exceeding 50 records, pagination controls are displayed and allow navigation between pages while preserving current sort and filter settings.

Filter & Segment Drill-Down Controls

"As an analyst, I want to filter drill-down results by date range, customer segment, or support channel so that I can focus on the most relevant subset of data."

Description

Provides in-dashboard controls for applying filters and segment selections—such as timeframe, customer attributes, and support channels—directly within the drill-down view. The requirement ensures that users can refine analysis on-the-fly without leaving the detailed view, enhancing efficiency and enabling targeted investigation of satisfaction drivers.

Acceptance Criteria

Apply Timeframe Filter in Drill-Down View

Given the user is viewing a CSAT drill-down graph, when the user selects a specific date range from the timeframe control, then the graph updates to display only interactions within the selected date range within 2 seconds without navigating away from the detailed view.

Filter by Customer Attribute

Given the user is in the drill-down view, when the user selects a customer attribute (e.g., VIP status or industry segment) from the segment control, then the drill-down data refreshes to include only interactions matching the selected attribute and all metrics update accordingly.

Filter by Support Channel

Given the drill-down controls are visible, when the user selects one or more support channels (e.g., live chat, email, phone) and applies the filter, then the detailed view shows only interactions from the selected channels and the data tables and graphs reflect those channels exclusively.

Combine Multiple Filters

Given multiple filter controls are available in the drill-down view, when the user applies a combination of timeframe, customer attribute, and support channel filters, then the displayed data reflects the intersection of all selected filter criteria accurately and loads within 2 seconds.

Clear All Filters

Given one or more filters have been applied in the drill-down view, when the user clicks the "Clear Filters" button, then all filters reset to their default state and the drill-down view returns to showing unfiltered data.

Detailed Interaction Context Panel

"As a customer support agent, I want to view metadata and comments for each ticket directly within the drill-down view so that I can understand the context without switching screens."

Description

Displays a context panel alongside drill-down tables that surfaces key metadata for each interaction—such as CSAT comment, interaction duration, and automated workflow triggers—providing immediate context and reducing the need to navigate away for additional details.

Acceptance Criteria

Accessing Context Panel from Drill-Down Table

Given a user has drilled into a CSAT graph and views the drill-down table, when the user clicks on an interaction row, then a context panel appears displaying key metadata for that interaction immediately adjacent to the table.

Validating CSAT Comment Visibility

Given the context panel is open for an interaction with a CSAT comment, when the panel loads, then the exact text of the CSAT comment is visible without truncation or scrollbars.

Verifying Interaction Duration Display

Given the context panel is open for any interaction, when viewing the panel, then the total interaction duration is accurately displayed in minutes and seconds.

Confirming Automated Workflow Trigger Details

Given an interaction has automated workflows involved, when the context panel loads, then all automated workflow triggers associated with that interaction are listed with their names and statuses.

Ensuring Panel Performance Under Load

Given a drill-down table with more than 1000 interactions, when a user opens the context panel for any interaction, then the panel loads fully within two seconds without impacting table responsiveness.

Multi-Channel Comparison Drill-Down

"As a support operations manager, I want to compare satisfaction trends across chat, email, and phone in a single drill-down view so that I can allocate resources where they will have the greatest impact."

Description

Enables side-by-side comparison of CSAT drivers across support channels (chat, email, phone) within the drill-down interface. This feature automatically aligns timeframes and segments to allow users to identify which channels are performing better and uncover channel-specific issues.

Acceptance Criteria

Side-by-Side Channel Comparison Initialization

Given the user opens the drill-down interface with no channels selected When the interface loads Then chat, email, and phone channels are displayed side by side with aligned timeframes and default segment ‘All Customers’ And each graph’s scale dynamically adjusts to show the full range of CSAT values across channels

Custom Timeframe Alignment for Multiple Channels

Given the user selects a custom start and end date When the selection is applied Then all chosen channels update to reflect the same timeframe And the comparative graphs refresh within 2 seconds showing synchronized data points

Segment-Specific Channel Performance Drill-Down

Given the user selects a customer segment filter (e.g., Premium Subscribers) When the filter is applied across chat and email channels Then both channel graphs update to only display CSAT data for the selected segment And the timeframes remain aligned

Interactive Tooltip Display for Channel Data Points

Given the user hovers over any data point in the comparison graph When the tooltip appears Then it shows channel name, date/time, CSAT score, number of interactions, and segment And the tooltip aligns near the cursor without obstructing other data

Export of Channel Comparison Data

Given the user clicks the export button When the export completes Then a downloadable CSV file is generated containing aligned timestamps, CSAT scores, interaction counts, and channel identifiers for all displayed channels

Drill-Down Export & Sharing

"As a support director, I want to export and share drill-down analytics with my team so that everyone stays informed and can act on the insights."

Description

Allows users to export drill-down results—including filtered graphs, tables, and context panels—to CSV or PDF, and to generate shareable links that preserve filter settings. This capability ensures insights can be easily shared with stakeholders and incorporated into reports.

Acceptance Criteria

Export Filtered Drill-Down Data as CSV

Given a user has applied custom filters to a CSAT graph, When they select the “Export to CSV” option, Then the system downloads a CSV file that contains only the filtered data with correct column headers and values within 10 seconds.

Export Filtered Drill-Down Data as PDF

Given a user has refined drill-down results displayed as tables and graphs, When they choose “Export to PDF,” Then the system generates a PDF file that accurately renders the graphs and tables, preserves layout and context panels, and is available for download within 15 seconds.

Generate Shareable Link with Preserved Filters

Given a user has configured filter settings on a drill-down view, When they click “Generate Shareable Link,” Then the system produces a unique URL that, when accessed by others, opens the drill-down view with identical filters and display settings.

Validate Export File Integrity and Format Compliance

Given an exported CSV or PDF file, When QA validates the file, Then the file must open without errors in standard applications (e.g., Excel, Adobe Acrobat), contain all requested data fields, and adhere to predefined format specifications.

Shareable Link Access and Validity

Given a user creates a shareable link, When they distribute the link, Then recipients can access the drill-down view without requiring additional authentication for 30 days, and after 30 days the link must expire or require reauthorization.

Trend Alerts

Automatically notifies stakeholders when CSAT scores dip below predefined thresholds or fluctuate sharply, ensuring rapid response to emerging issues and safeguarding customer satisfaction.

Requirements

CSAT Threshold Configuration

"As a support lead, I want to configure CSAT thresholds and fluctuation limits so that I receive alerts only when meaningful changes occur."

Description

Enable support leads to define custom CSAT score thresholds and percentage change limits for triggering alerts. This includes setting absolute score dip values and relative fluctuation percentages, with validation to prevent invalid ranges.

Acceptance Criteria

Configuring New Absolute CSAT Threshold

Given the support lead navigates to the CSAT Threshold Configuration page When they enter a valid absolute score dip value between 0 and 100 Then the system saves the new threshold and displays it in the configuration list

Setting Relative CSAT Fluctuation Limit

Given the support lead selects percentage-based fluctuation When they input a valid percentage value between 0% and 100% Then the system stores the fluctuation threshold and reflects it in the alerts settings

Preventing Invalid Threshold Ranges

Given the support lead enters values outside the valid range (score <0 or >100, percentage <0% or >100%) When they attempt to save Then the system displays a validation error and disables the save button

Updating Existing CSAT Thresholds

Given existing thresholds are displayed When the support lead modifies a threshold value to another valid value Then the system updates the threshold and confirms the change with a success message

Threshold Persistence After Logout and Login

Given thresholds have been configured When the support lead logs out and logs back in Then previously saved thresholds are loaded and displayed correctly

Real-time Alert Dispatch

"As a support analyst, I want the system to send alerts in real time when CSAT dips so that I can respond to issues promptly."

Description

Automatically monitor incoming CSAT data and dispatch alerts immediately when defined thresholds are breached. The system should process data continuously and send notifications without manual intervention.

Acceptance Criteria

Alert Trigger on CSAT Drop

Given real-time CSAT monitoring is active When incoming CSAT score falls below predefined threshold Then system dispatches alert notification within 30 seconds

Notification Delivery Verification

Given an alert is generated When the notification service experiences transient failures Then system retries delivery up to three times and logs each attempt

Threshold Configuration Change Impact

Given support lead updates the CSAT threshold When new CSAT data is ingested Then alerts are evaluated against the updated threshold without requiring system restart

Concurrent Data Stream Handling

Given high-volume CSAT data streams When 1000 data points are processed per minute Then system processes and evaluates all data points with less than 1% processing error rate

No Alert on Stable CSAT

Given CSAT scores fluctuate within ±5% of threshold When data is processed Then no alerts are dispatched

Multi-channel Notification

"As a customer success manager, I want to receive CSAT alerts through my preferred channels so that I never miss critical notifications."

Description

Support sending trend alerts via email, SMS, and in-app notifications. Stakeholders can select preferred channels and configure fallback options if the primary channel fails.

Acceptance Criteria

Notification Channel Configuration

Given a stakeholder navigates to the Trend Alerts settings page When they select one or more notification channels (email, SMS, in-app) And click Save Then the system stores their channel preferences And displays a confirmation message

Email Notification Delivery

Given an alert is triggered for a CSAT drop When email is a configured channel Then the system sends an email notification to the stakeholder within 1 minute And the email contains the alert details including timestamp, CSAT score, and trend reason

SMS Notification Delivery

Given an alert is triggered and SMS is configured When the system attempts SMS delivery Then the message is sent via the SMS gateway and delivered within 1 minute And the stakeholder's phone number is correct and receives the alert content

In-app Notification Delivery

Given an alert is triggered and in-app notifications are enabled When the stakeholder is logged into PulseDesk Then a real-time notification banner appears in the UI within 5 seconds And the notification details are accessible in the notifications panel

Primary Channel Failure Fallback

Given an alert is triggered and the primary channel fails to deliver within 2 minutes When fallback channels are configured in order Then the system retries delivery via the next channel in the list And logs the failure and retry in the alert history

Alert Dashboard and History Log

"As a support lead, I want to view and filter past alerts on a dashboard so that I can analyze trends and follow up on unresolved issues."

Description

Provide a centralized dashboard displaying active alerts, historical occurrences, and resolution status. Include filtering by date range, threshold type, and stakeholder group for analysis and audit purposes.

Acceptance Criteria

Viewing Active Alerts on the Dashboard

Given the support lead navigates to the Alert Dashboard, when there are active alerts, then a real-time list displays each alert’s timestamp, CSAT score, breached threshold, and assigned stakeholder group.

Filtering Alerts by Date Range

Given the support lead selects a start and end date on the dashboard, when the filter is applied, then only alerts with occurrence dates within the specified range are displayed.

Filtering Alerts by Threshold Type

Given the support lead chooses one or more threshold types from the threshold filter menu, when the filter is applied, then the dashboard lists only alerts matching the selected threshold types.

Filtering Alerts by Stakeholder Group

Given the support lead selects a stakeholder group filter, when applied, then only alerts assigned to the chosen stakeholder group appear in the dashboard.

Viewing Historical Alert Details

Given the support lead clicks on an alert entry in the history log, when selected, then the system displays detailed information including alert timestamp, threshold breached, resolution status, resolution time, and any resolution notes.

Escalation Workflow Integration

"As a support manager, I want alerts to trigger escalation workflows so that critical CSAT issues are addressed by the right people at the right time."

Description

Integrate alerts with existing no-code workflow builder to automatically escalate incidents based on severity. Configure multi-level escalation rules to notify higher-level stakeholders if initial alerts are not acknowledged within a set timeframe.

Acceptance Criteria

Initial Alert Escalation Trigger

Given a CSAT alert is generated for a ticket and assigned to Level 1 stakeholder When Level 1 stakeholder does not acknowledge the alert within 30 minutes Then the system automatically escalates the alert to the Level 2 stakeholder

Multi-Level Escalation Notification

Given an unacknowledged alert has been escalated to Level 2 When an additional 30 minutes pass without acknowledgment from Level 2 Then the system automatically escalates the alert to Level 3 as configured

Customizable Escalation Timeframes

Given the support lead configures different timeframes for each escalation level in the workflow builder When alerts are generated Then each alert is escalated according to the custom timeframes specified for each level

Escalation Rule Configuration Interface

Given the support lead accesses the no-code workflow builder When they add, edit, or delete multi-level escalation rules Then changes are saved, validated, and reflected in the alert escalation behavior

Acknowledgement Stops Escalation

Given a stakeholder acknowledges an alert at any escalation level When acknowledgment is received Then the system halts any further escalations for that alert

Benchmark Builder

Allows teams to set custom benchmarks for CSAT performance—by product line, region, or support tier—and visualizes progress against these targets to drive accountability and continuous improvement.

Requirements

Custom Benchmark Configuration

"As a support lead, I want to configure custom CSAT benchmarks by product line, region, or support tier so that I can set clear performance targets for my team."

Description

A UI component that allows support leads to define performance benchmarks based on product line, region, or support tier. This feature lets users input target CSAT values, set timeframes and categories, and save custom benchmarks to be used across the PulseDesk platform.

Acceptance Criteria

Benchmark Creation Workflow

Given the support lead navigates to the Benchmark Builder UI, when they enter valid values for product line, region, support tier, target CSAT percentage, and timeframe, and click the Save button, then the system persists the new custom benchmark and displays it in the benchmarks list.

Existing Benchmark Editing

Given the support lead selects an existing custom benchmark from the list, when they update the target CSAT value or timeframe and click Save, then the system updates the benchmark record, triggers a success notification, and reflects changes in the analytics views.

Validation of Required Fields

Given the support lead attempts to save a custom benchmark without completing all mandatory fields, when they click Save, then the Save action is blocked, inline validation messages appear for each missing or invalid field, and the benchmark is not persisted.

Benchmark Persistence Across Sessions

Given a custom benchmark has been successfully created and saved, when the user logs out and logs back in to PulseDesk, then the previously saved custom benchmark appears unchanged in the Benchmark Builder list.

Benchmark Application to Reporting

Given one or more custom benchmarks are defined, when the support lead opens the CSAT performance dashboard, then the dashboard segments metrics by the defined product line, region, or support tier benchmarks and displays visual indicators showing actual performance versus targets.

Benchmark Segmentation Management

"As a support manager, I want to assign benchmarks to particular teams or agents so that each group only tracks goals relevant to their responsibilities."

Description

Ability to group and assign created benchmarks to specific teams or agents based on attributes like region or support tier. This ensures that each team sees relevant benchmarks and avoids data clutter.

Acceptance Criteria

Regional Support Team Assignment

Given a benchmark is created When the admin filters by region and assigns the benchmark Then only teams in the selected region see the benchmark and other teams do not

Support Tier Assignment

Given a benchmark exists When the admin groups by support tier and assigns the benchmark Then only agents in the chosen tier see the benchmark and can track progress

Agent-specific Benchmark Assignment

Given a benchmark is defined When the admin selects individual agents and assigns the benchmark Then only those agents have visibility and access to the assigned benchmark

Benchmark Reassignment and Removal

Given a benchmark already assigned When the admin updates or removes assignments Then the system immediately reflects the new assignments and unassigned teams or agents no longer see the benchmark

Benchmark Visibility Filter

Given multiple benchmarks across segments When an agent views their dashboard Then only benchmarks assigned to their region or support tier are displayed, eliminating unrelated benchmarks

Real-time Benchmark Dashboard

"As a support lead, I want to view real-time progress against benchmarks so that I can quickly identify areas needing attention and celebrate successes."

Description

A visual dashboard displaying real-time CSAT performance against defined benchmarks using charts, gauges, and progress bars. It includes filtering options and color-coded indicators for underperformance or achievement.

Acceptance Criteria

Applying Region Filter

Given the support lead selects the "Europe" region filter on the dashboard, when the filter is applied, then only CSAT performance data and benchmarks for the Europe region are displayed.

Support Tier Filtering

Given the support lead selects the "Premium" support tier filter, when the filter is applied, then the dashboard displays only CSAT metrics and benchmark comparisons for the Premium tier.

Real-time Data Refresh

Given new CSAT survey results arrive in the system, when data is processed, then the dashboard automatically updates within 5 seconds without requiring a manual page reload.

Benchmark Achievement Progress Bar

Given a defined CSAT benchmark of 90% for a product line, when current CSAT reaches or exceeds 90%, then the progress bar displays 100% completion and shows a green success indicator.

Visual Indicator Color Coding

Given the live CSAT performance value, when it falls below 80% of the benchmark, then the gauge turns red; when it is between 80% and 100%, it turns amber; and when it meets or exceeds 100%, it turns green.

Benchmark Alert Notifications

"As a support lead, I want to receive notifications when performance falls below or exceeds benchmarks so that I can take timely action or acknowledge top performance."

Description

Automated alerts and notifications sent via email or in-app when CSAT performance deviates from set benchmarks. Users can configure thresholds for low or high performance and choose notification channels.

Acceptance Criteria

Low CSAT Deviation Alert Triggered

Given a product line CSAT score falls below the configured low threshold, when the system completes its hourly performance evaluation, then an alert notification is automatically sent to the designated channels within 5 minutes. The notification includes product line, current CSAT score, threshold value, and timestamp. The notification is delivered via email and in-app based on user preferences. A record of the alert is logged in the audit trail with status 'sent'.

High CSAT Achievement Alert Triggered

Given a region or support tier CSAT score exceeds the configured high threshold, when the system performs its daily benchmark evaluation, then a notification is sent to the designated channels within 5 minutes. The notification content includes region or support tier, actual CSAT score, benchmark value, and timestamp. The notification is delivered via the user’s selected channels. A record of the alert is logged in the audit trail with status 'sent'.

Benchmark Threshold Configuration Update Notification

Given a user updates benchmark thresholds for a product line, when the new thresholds are saved, then an in-app confirmation and an email are sent to the administrator within 2 minutes. The confirmation includes old and new threshold values, the user who made the change, and a timestamp. A record of the configuration change is logged in the audit trail.

Notification Channel Preference Enforcement

Given a user has deselected email notifications for alerts, when any CSAT deviation alert is triggered, then no email is sent and the alert is only delivered in-app. The system respects the user’s channel preferences for all alert types. A log entry records the channels used for each alert.

Maintenance Window Alert Suppression

Given a scheduled maintenance window, when CSAT performance deviates from benchmarks during the window, then alerts are suppressed until the window ends. When maintenance ends, all suppressed alerts are sent in a single summary notification to the designated channels. The summary notification includes each alert’s context, timestamp, and the time it was originally triggered. A record of suppressed and summary alerts is stored in the audit trail.

Historical Benchmark Trend Analysis

"As a support lead, I want to analyze historical CSAT trends against past benchmarks so that I can evaluate the effectiveness of process changes over time."

Description

Reporting feature that compares current CSAT performance against historical benchmarks over selected periods. Provides trend lines, percentage changes, and context to understand long-term performance improvements.

Acceptance Criteria

Selecting Comparison Periods

Given the user navigates to the Historical Benchmark Trend Analysis page and selects two date ranges for comparison, When both ranges are valid and non-overlapping, Then the system loads benchmark data for each period without error.

Visualizing Trend Lines

Given benchmark data is retrieved for the selected periods, When the analysis view renders, Then trend lines for each period display correctly on the chart with distinct colors and accurate time-axis labels.

Displaying Percentage Change

Given current and historical CSAT values for a benchmark, When the user hovers over a point on the trend line, Then the system shows the percentage change relative to the historical period in a tooltip.

Contextualizing Performance Changes

Given a percentage change exceeds the configurable threshold, When the trend line updates, Then the system highlights the change point in red or green and displays a contextual note explaining the deviation.

Exporting Trend Analysis Report

Given the trend chart is visible, When the user clicks the Export button, Then a downloadable report (PDF or CSV) of the trend analysis with charts and key metrics is generated within 5 seconds.

Improvement Planner

Generates tailored action plans and best-practice recommendations based on CSAT insights—such as training modules, workflow adjustments, or template optimizations—empowering teams to close the loop on satisfaction gaps.

Requirements

CSAT Data Aggregation Engine

"As a support manager, I want to view a unified set of CSAT data and related ticket details so that I can identify satisfaction trends and underlying issues quickly."

Description

Develop a robust engine to automatically collect and consolidate CSAT scores, feedback comments, and ticket metadata from live chat and ticketing systems, ensuring real-time data availability and data integrity for analysis.

Acceptance Criteria

Real-time Data Ingestion

Given a live chat session ends and a CSAT score is submitted, when processed by the engine, then the score and associated ticket metadata must be available in the unified data store within 30 seconds.

Feedback Comment Consolidation

Given multiple feedback comments for the same ticket from chat and ticketing systems, when aggregation occurs, then duplicate comments are merged into a single entry while preserving all unique feedback details.

Data Integrity Validation

Given aggregated CSAT records, when processing is complete, then the total number of records in the engine matches the sum of source records and any null or invalid fields are flagged in a validation report.

Cross-System Metadata Mapping

Given a ticket present in both live chat and ticketing systems with matching identifiers, when aggregation runs, then metadata fields (agent ID, timestamp, channel) are normalized into the unified schema without loss of accuracy.

API Failure Recovery

Given a temporary failure of the source system API during data ingestion, when the engine encounters an error, then it retries up to three times with exponential backoff and logs failures, alerting monitoring if all retries fail.

Insights-to-Recommendations Algorithm

"As a team lead, I want the system to suggest specific improvements based on satisfaction patterns so that I can address recurring problems efficiently."

Description

Implement an AI-driven algorithm that analyzes aggregated CSAT insights, identifies patterns or gaps, and generates tailored best-practice recommendations such as training modules, workflow tweaks, and template optimizations.

Acceptance Criteria

Identify Key Satisfaction Gaps for Training Recommendations

Given aggregated CSAT data with at least 100 tickets from the last 30 days, when the algorithm runs, then it identifies the top three satisfaction dimensions scoring below 70% and maps each to a relevant training module.

Suggest Workflow Adjustments Based on CSAT Trends

Given weekly CSAT trends indicating a decline in first response time satisfaction by more than 10%, when recommendations are generated, then the algorithm provides at least two actionable workflow adjustments aimed at reducing response times.

Generate Template Optimizations for Low-Scoring Interactions

Given support ticket templates with average CSAT scores below 75%, when the optimization process is executed, then the algorithm outputs three optimized template variations with suggested phrasing improvements that correlate to a projected CSAT uplift of at least 5%.

Prioritize Recommendations by Impact Score

Given multiple generated recommendations, when the recommendations list is finalized, then each recommendation is scored by expected CSAT uplift and sorted in descending order, with the top recommendation showing an uplift projection of at least 5%.

Ensure Recommendation Relevance for New Support Agents

Given onboarding metrics for new agents whose CSAT averages fall below the team benchmark, when tailored recommendations are provided, then at least two recommendations include step-by-step guidance suitable for non-technical users and require no engineering support.

Action Plan Composer

"As a support lead, I want to build and tailor an action plan from recommended items so that my team can take clear, structured steps to improve CSAT scores."

Description

Create an intuitive interface for composing and customizing action plans, allowing users to select recommended items, adjust timelines, assign responsibilities, and set milestones for implementing improvement steps.

Acceptance Criteria

Selecting a Recommended Improvement Item

Given the user views the list of recommended improvement items, When the user selects an item, Then the selected item is added to the action plan composition panel and highlighted.

Customizing Action Plan Timeline

Given the action plan timeline interface is displayed, When the user adjusts start and end dates using the date picker, Then the timeline updates to reflect the new dates in the plan overview.

Assigning Responsibilities to Team Members

Given the list of team members is available, When the user assigns a responsibility to a team member for a specific improvement step, Then the assignment is saved and visible under the step details.

Setting Milestones for Improvement Steps

Given the milestones section in the action plan, When the user creates a milestone with a date and description, Then the milestone is added to the plan’s milestone timeline and visible in the milestones list.

Saving and Exporting the Composed Action Plan

Given the action plan is fully composed, When the user clicks the save or export button, Then the plan is persisted to the database and an export file (PDF or CSV) is generated and available for download.

Template Library Integration

"As a support specialist, I want to access and modify best-practice templates directly within the planner so that I can deploy solutions without starting from scratch."

Description

Integrate a library of pre-built templates and training modules linked to common CSAT issues, enabling users to quickly apply proven solutions and customize content for their organization’s processes.

Acceptance Criteria

Template Application from Library

Given the template library is accessible When the user selects a template linked to a CSAT issue Then the chosen template is applied to the improvement plan and all relevant sections are populated with template content

Template Customization per Organization

Given a selected template When the user edits content fields, adjusts steps, or updates placeholders and saves the template Then the customized version is stored under the user’s organization and available for reuse

Template Search by CSAT Keyword

Given the search interface is displayed When the user enters a CSAT issue keyword Then templates tagged with matching keywords are returned within two seconds, sorted by relevance

Template Preview Rendering

Given a selected template When the user clicks the preview button Then a rendered view displays the complete template content with correct formatting and resolved placeholders without modifying the improvement plan

Template Categorization

Given multiple templates in the library When the user creates custom categories and assigns templates via drag-and-drop or tag selection Then templates appear under the appropriate categories in the library view

Feedback Loop & Progress Tracking

"As a support manager, I want to monitor the impact of our action plans and receive updated recommendations so that we continuously close the loop on satisfaction gaps."

Description

Build a feedback mechanism and dashboard to track implementation progress, collect post-implementation CSAT metrics, and refine future recommendations based on outcomes and user feedback.

Acceptance Criteria

Progress Dashboard Accessibility

Given a logged-in Support Lead When they navigate to the Improvement Planner dashboard Then they should see a real-time progress tracker displaying implementation status for each recommendation, including percentage complete and latest CSAT metrics

Implementation Status Update Logging

Given a support agent marks a recommendation as implemented When the agent submits the update Then the dashboard logs the timestamp, agent ID, and updated status, and the progress bar reflects the change within 5 seconds

CSAT Metric Integration

Given post-implementation period defined When new CSAT scores are collected Then the system automatically associates these scores with the implemented recommendation and updates the dashboard trend line

User Feedback Collection

Given a recommendation marked as implemented When the system prompts for feedback Then the support lead can submit qualitative feedback through a form and the submission is stored and viewable on the dashboard

Recommendation Refinement

Given collected CSAT scores and user feedback When a support lead requests refined recommendations Then the system generates updated best-practice suggestions reflecting past performance within 10 seconds

Product Ideas

Innovative concepts that could enhance this product's value proposition.

Ticket Turbocharge

Summarizes ticket history and suggests tailored responses using sentiment analysis, cutting agent drafting time by 30%.

Idea

FlowForge Templates

Delivers industry-tailored workflow blueprints that teams import in one click, jumpstarting support processes instantly.

Idea

ChatterSync Dashboard

Visualizes live-chat sentiment across channels in real time, highlighting trending issues for proactive support.

Idea

AutoRoute AI

Analyzes ticket content and customer priority to assign issues to the best-fit agent instantly, boosting resolution speed.

Idea

PulseScore Insights

Tracks and graphs CSAT scores per interaction, revealing satisfaction trends and pinpointing top-performing agents.

Idea

Press Coverage

Imagined press coverage for this groundbreaking product concept.

PulseDesk Unveils Next-Generation No-Code Workflow Builder to Slash Support Resolution Times

Imagined Press Article

SAN FRANCISCO, CA – May 15, 2025 – PulseDesk, the unified support platform empowering non-technical SaaS teams, today announced the launch of its next-generation no-code workflow builder. Designed to reduce manual handoffs and accelerate ticket resolution by up to 50%, the intuitive builder allows support leads and automation enthusiasts to design, test, and deploy end-to-end customer support flows without writing a single line of code. Modern support organizations face constant pressure to balance speed, accuracy, and personalization. Traditional ticketing systems often require technical resources to build or modify workflows, resulting in delays and bottlenecks. PulseDesk’s no-code workflow builder addresses these challenges by providing an all-in-one canvas where users can drag and drop triggers, branching logic, automated actions, and integrations with popular SaaS tools. Whether routing high-priority tickets based on sentiment, escalating urgent issues to senior agents, or sending follow-up surveys automatically, teams can configure and adapt workflows in minutes, not days. Key features of the no-code workflow builder include: • Drag-and-Drop Interface: A visual canvas for assembling complex support processes with prebuilt blocks, conditional logic, and integrations for CRMs, messaging platforms, and analytics tools. • Template Library: A repository of industry-tailored blueprints—ranging from onboarding flows for new customers to escalations for enterprise accounts—that can be imported, customized, and saved as new templates in a single click. • Rapid Preview Mode: A sandbox environment enabling teams to simulate workflows end-to-end, verifying each step before publishing to production, minimizing errors and ensuring consistent customer experiences. • Version Vault: Automatic version control for every workflow, allowing support leads to compare changes, restore previous iterations, or branch off new variants without risking live operations. “Our mission at PulseDesk has always been to put the power of automation into the hands of support professionals,” said Maya Patel, Chief Product Officer at PulseDesk. “With the new no-code workflow builder, we’re removing the technical barriers that slow down innovation. Non-technical team members can now take full ownership of their support processes, rapidly iterate based on real-time feedback, and respond to changing customer needs without calling in engineering resources.” Early adopters have already reported significant improvements in efficiency and customer satisfaction. Beta customer NextWave Software, a fast-growing B2B SaaS provider, implemented PulseDesk’s workflow templates for trial onboarding and reported a 60% reduction in average handling time, alongside a 20% increase in first-contact resolution rates. “Before PulseDesk, every change to our support process required a ticket to IT, which could take days or weeks,” explained Liam Chen, Support Operations Manager at NextWave Software. “Now, our support strategists and automation enthusiasts collaborate directly in the builder. We’ve launched new escalation paths for high-stakes enterprise clients and automated routine follow-ups in under an hour—something that used to take months.” The no-code workflow builder is available immediately to all PulseDesk customers on the Professional and Enterprise plans. Existing users can access the new builder via the PulseDesk dashboard without additional installation or configuration. New customers can sign up for a free 14-day trial to explore the platform and experience the full suite of features, including Context Capsule, ToneCraft, and Action Blueprint. PulseDesk will host a live product webinar on June 5, 2025, featuring a deep dive into the workflow builder, live demonstrations, and best practices from customer success teams. Registration is open on the PulseDesk website. About PulseDesk PulseDesk is the unified support platform that empowers SaaS businesses to deliver faster, more personalized customer service at scale. By combining live chat, ticketing, and no-code workflow automation in one intuitive interface, PulseDesk helps teams reduce resolution times by up to 50%, boost collaboration, and elevate customer satisfaction. Trusted by hundreds of high-growth software companies worldwide, PulseDesk is headquartered in San Francisco, CA. Media Contact: Jordan Reyes Director of Communications, PulseDesk press@pulsedesk.com (415) 555-0132

PulseDesk Introduces AI-Powered Context Capsule and ToneCraft to Elevate Customer Engagement

Imagined Press Article

NEW YORK, NY – May 15, 2025 – PulseDesk, the market-leading SaaS customer support platform, today announced the general availability of two groundbreaking AI-powered features—Context Capsule and ToneCraft—that bring unprecedented speed and empathy to every customer interaction. The new capabilities are designed to help support teams instantly grasp ticket history and sentiment, and deliver responses that align with both customer emotions and brand voice. In today’s fast-paced digital economy, support agents are often overwhelmed by fragmented customer data and tight response deadlines. Context Capsule addresses these challenges by automatically summarizing every ticket’s critical interactions into a concise, three-sentence overview that highlights key customer details, recent communications, and outstanding tasks. Agents can now onboard themselves onto any ticket in seconds, eliminating the need to scroll through lengthy message threads or manually piece together context. Complementing Context Capsule, ToneCraft harnesses advanced sentiment analysis and natural language generation to draft empathetic, professional, or urgent responses tailored to each customer’s mood. By analyzing customer sentiment in real time, ToneCraft suggests response tones and phrasing that resonate, ensuring every message feels genuine and on-brand. Support professionals can choose from multiple AI-generated drafts or customize templates to fit specific scenarios, cutting drafting time in half while maintaining a human touch. “We believe that great support is both efficient and heartfelt,” said Rajiv Malhotra, CEO of PulseDesk. “Context Capsule and ToneCraft are transformative because they free agents from repetitive tasks and empower them to focus on problem-solving and building rapport with customers. Early tests show that teams using these features have improved CSAT scores by up to 15% while reducing average response times by 40%. That’s a win for support operations and a win for customers.” Feature highlights include: • Context Capsule: A dynamic snippet generator that produces a three-sentence summary of ticket history, offering agents an instant snapshot of customer interactions, key issues, and unresolved actions. • ToneCraft: AI-driven response drafts customized to match customer sentiment—options include empathetic, professional, or urgent tones—complete with brand voice alignment and suggested phrasing. • Reply Palette Integration: Both Context Capsule and ToneCraft seamlessly integrate with PulseDesk’s Reply Palette to deliver three distinct, ready-to-send response options. Agents can preview tone, edit as needed, and send with one click. • Real-Time Notifications: ToneCraft flags high-sensitivity tickets where sentiment is deteriorating, and provides agents with tone-adjustment recommendations to turn around negative experiences before escalation. PulseDesk worked closely with a select group of enterprise customers during the beta program to refine accuracy and usability. Support teams at CloudWave Logistics and DataForge Technologies reported notable improvements in agent confidence and productivity. “Agents spend too much time reading through long histories and worrying about how to phrase replies,” remarked Helena Marks, Head of Customer Support at DataForge Technologies. “With Context Capsule, I can see the entire thread in one glance. And with ToneCraft, new agents draft empathetic, brand-compliant messages immediately. We’re seeing faster onboarding for new hires and higher quality responses across the board.” Both Context Capsule and ToneCraft are included at no additional cost for PulseDesk Professional and Enterprise customers. New customers can experience these AI-driven tools during a free 14-day trial, along with full access to PulseDesk’s unified chat, ticketing, and automation suite. PulseDesk will feature live demos of Context Capsule and ToneCraft at the upcoming Customer Experience Conference in Chicago on June 10, 2025. Attendees can schedule one-on-one consultations at booth #42. About PulseDesk PulseDesk delivers a unified customer support platform that combines live chat, ticketing, and AI-driven workflow automation. By streamlining agent workflows and elevating every touchpoint with customers, PulseDesk helps SaaS companies achieve faster resolution times, higher CSAT scores, and sustainable growth. The company is headquartered in New York, NY, with offices in London and Singapore. Media Contact: Avery Lin Senior Public Relations Manager, PulseDesk media@pulsedesk.com (212) 555-0298

PulseDesk Expands Analytics Suite with Sentiment Heatmap and TrendSpotter for Proactive Support Management

Imagined Press Article

LONDON, UK – May 15, 2025 – PulseDesk, the leading provider of unified customer support software, today announced the expansion of its analytics suite with two powerful modules—Sentiment Heatmap and TrendSpotter—designed to help support leaders stay ahead of customer needs and prevent issues before they escalate. By combining real-time sentiment visualization with advanced topic detection, these features empower teams to allocate resources strategically and drive proactive improvements. As customer expectations evolve, reactive support models are no longer sufficient. Support leads and feedback analysts need actionable insights that reveal emerging pain points and highlight opportunities to delight customers. PulseDesk’s new analytics modules transform raw chat and ticket data into intuitive visualizations and recommendations that unlock deeper understanding and faster decision-making. Sentiment Heatmap delivers a color-coded matrix displaying customer sentiment across all live-chat channels, time zones, or support tiers. Teams can instantly identify patterns of positive or negative experiences and zoom into specific channels or agents to pinpoint root causes. Meanwhile, TrendSpotter uses natural language processing to analyze incoming chat transcripts, surface recurring keywords and topics, and group similar conversations into thematic clusters. These insights enable support managers to update knowledge bases, refine workflows, and launch targeted initiatives before issues become widespread. Key capabilities include: • Real-Time Sentiment Overlay: The Sentiment Heatmap updates dynamically, reflecting shifts in customer mood as they happen and enabling immediate intervention for at-risk interactions. • Channel Comparison: Support leads can compare sentiment trends across multiple channels—web chat, in-app messaging, social media—to optimize staffing and channel strategies. • Thematic Clustering: TrendSpotter automatically organizes conversations into clusters by topic, urgency, or customer segment, highlighting high-frequency issues that warrant quick action. • Spike Alerts: Customizable notifications for sudden surges in negative sentiment or topic volume, ensuring stakeholders receive timely updates and can mobilize cross-functional teams. “We’re moving from a one-ticket-at-a-time mindset to a holistic view of customer sentiment and behavior,” said Elena Rossi, Vice President of Analytics at PulseDesk. “With Sentiment Heatmap and TrendSpotter, support organizations can see the full picture and intervene proactively—whether by reallocating agents to busy channels or updating self-service resources to address trending questions. The result is more efficient operations and happier customers.” Beta testers, including innovative SaaS companies GreenTech Innovations and MarketSync, reported a marked reduction in negative sentiment spikes and faster resolution of systemic issues. “Before PulseDesk’s analytics expansion, we were firefighting one issue after another,” shared Oliver Grant, Customer Success Director at MarketSync. “Now we’re alerted to rising concerns in real time and can deploy targeted fixes—like refining our password reset flow—before tickets flood in. It’s a game-changer for maintaining SLAs and boosting customer confidence.” Sentiment Heatmap and TrendSpotter are available immediately for all PulseDesk Enterprise customers. Organizations on the Professional plan can upgrade to access these advanced analytics modules or explore them during a 30-day trial. In addition, PulseDesk will offer tailored onboarding sessions and best-practice workshops to help teams maximize the impact of these insights. To learn more about PulseDesk’s analytics suite, download the whitepaper “Proactive Support Strategies for SaaS Leaders” from the PulseDesk website or register for the upcoming webinar on June 12, 2025. About PulseDesk PulseDesk is a unified support platform that combines live chat, ticketing, and intelligent analytics to help SaaS companies deliver exceptional customer experiences at scale. Trusted by hundreds of forward-thinking organizations, PulseDesk provides the tools and insights teams need to resolve issues faster, foster loyalty, and drive long-term growth. Headquartered in London, UK, PulseDesk operates global offices in San Francisco and Singapore. Media Contact: Serena Patel Global Communications Lead, PulseDesk press.eu@pulsedesk.com +44 (0)20 7946 0123

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