Roadmap Navigation
AI Call Quality Platform - Roadmap
🚀 What's Next: Platform Roadmap
Transform the AI Call Quality Platform from a reactive analysis tool into an intelligent, self-optimizing system that automatically identifies call quality issues, generates targeted prompt improvements, deploys changes to production, and continuously measures impact through comprehensive monitoring and feedback loops.
This roadmap outlines our journey from manual analysis (where we are today) to a fully automated prompt optimization pipeline (our target state by Q1 2026). Each phase builds upon the previous, creating a compound effect that dramatically improves call quality while reducing manual effort by 80%.
📑 Quick Navigation
Use the left navigation panel to explore different sections of our roadmap:
📋 Platform Vision & Strategic Context
The AI Call Quality Platform currently excels at identifying problems through intent-based analysis and priority classification. However, the journey from "problem identified" to "problem solved" still requires significant manual effort. Our vision is to close this loop completely.
Current State (Manual Process): When we identify that 45 calls are failing with "Reschedule" intent due to unclear bot responses about appointment availability, a human analyst must: (1) manually review call transcripts, (2) manually run Catalyst analysis on sample calls, (3) manually aggregate findings, (4) manually write new prompt text, (5) manually test changes, (6) manually deploy to production, and (7) manually check if the fix worked. This takes 2-3 days.
Target State (Automated Process): The same scenario is detected automatically, all 45 calls are analyzed by Catalyst in batch, cumulative prompt changes are generated automatically, code is deployed through CI/CD pipeline with A/B testing, and monitoring metrics confirm the fix worked - all within 24 hours with minimal human oversight. The analyst only reviews the proposed changes before deployment.
- Reduce manual intervention by 80% - Automate the entire workflow from analysis to deployment, freeing analysts to focus on strategic insights rather than repetitive tasks. Current: 16 hours manual work per priority. Target: 3 hours review time.
- Accelerate time-to-fix from 2-3 days to 24 hours - Compress the entire cycle through automation and parallel processing. This means customer-impacting issues get resolved 3x faster, directly improving satisfaction and revenue.
- Measure impact with data-driven confidence - Replace gut feelings with concrete A/B testing metrics. Know exactly which prompt changes work (deploy more), which don't (rollback quickly), and why.
- Enable continuous learning and improvement - Build feedback loops so successful patterns automatically inform future recommendations. The system gets smarter with every deployment.
- Scale to handle 10x call volume - Current manual process can't scale beyond 500 calls/day. Automated pipeline can handle 5,000+ calls/day without additional headcount.
By automating the prompt optimization pipeline, we estimate a -50% reduction in call dropout rates (currently 12%, target 6%), translating to approximately $2.4M annual revenue recovery based on current call volumes and average order values. Additionally, the 80% reduction in manual effort represents approximately $180K in annual labor cost savings.
🔄 End-to-End Workflow
Intent-Based Call Analysis
What it does: The system analyzes 500+ customer service calls daily from yesterday's call data (Central Time), using GPT-4 to understand the customer's primary intent behind each call. Calls are automatically classified into categories like "Reschedule", "Unknown Intent", "Cancel Appointment", "Ask Question", etc.
Why it matters: Instead of analyzing calls randomly, we group them by intent first. This reveals patterns - for example, if 120 calls all had "Reschedule" intent and 45 of them failed, that's a specific problem we can fix with targeted prompt improvements.
- Daily call transcripts from Sears scheduler bot (500-800 calls)
- Call metadata: duration, timestamp, outcome (success/failure), dropout stage
- Bot conversation history with user inputs and bot responses
- Intent categories with associated call IDs (e.g., "Reschedule: 120 calls, 45 failed")
- Quality metrics per intent: success rate, dropout rate, average duration
- Failure pattern analysis showing where in the conversation calls typically fail
- Total Calls: 120
- Successful: 75 (62.5%)
- Failed: 45 (37.5%)
- Common Failure: Bot couldn't understand date preferences (28 calls)
- Call IDs: ["call_abc123", "call_def456", ...]
Priority Classification & Recommendations
What it does: Within each intent category, calls are further grouped into priority classes (P1-Critical, P2-High, P3-Medium) based on business impact. P1 issues affect the most calls and have highest revenue impact. Each priority class gets a specific, actionable recommendation card.
Why it matters: Not all call failures are equally important. If 2 calls failed because the bot couldn't handle a rare edge case, that's P3-Medium. If 45 calls failed because the bot's date parsing logic is broken, that's P1-Critical and should be fixed immediately. Priority classification ensures we work on what matters most first.
- P1-Critical: ≥30 calls affected, high revenue impact, broken core functionality
- P2-High: 10-29 calls affected, moderate revenue impact, degraded UX
- P3-Medium: <10 calls affected, low revenue impact, edge cases
Each priority gets a recommendation card with:
- Priority badge (P1/P2/P3) with visual color coding
- Call count and revenue impact estimate
- Root cause analysis (what's actually broken)
- Recommended fix (high-level description)
- "Run Catalyst" button to analyze all calls in this priority
Problem: Bot fails to parse customer date preferences like "next Tuesday" or "the 15th". Customers get frustrated and hang up.
Revenue Impact: ~$6,750 (45 calls × $150 avg order value)
Recommendation: Improve date parsing in system prompt. Add examples of relative dates ("next week", "tomorrow", specific dates). Enhance context awareness for month/year inference.
Next Step: Run automated Catalyst analysis on all 45 calls to generate specific prompt changes.
Automated Catalyst Analysis (Per-Priority)
What it does: The Catalyst engine is our AI-powered prompt improvement tool that analyzes individual call transcripts and recommends specific prompt changes to fix identified issues. Currently, analysts run Catalyst manually on sample calls. Phase 3 automates this completely - when you click "Run Catalyst" on a P1-Critical priority with 45 calls, the system processes all 45 calls automatically in batch mode.
Why it matters: Manual Catalyst analysis is time-intensive. Analyzing 45 calls manually takes ~6-8 hours. Automated batch processing completes the same work in 15-20 minutes, running calls in parallel. More importantly, it analyzes every single call in the priority, not just a sample, giving us complete data.
- Priority class selection (e.g., "P1-Critical: Date Understanding Issues")
- N call IDs associated with this priority (e.g., 45 calls)
- Current bot prompt text and system instructions
- Call transcripts with full conversation history
- Job Queue Creation: System creates a background job with 45 call analysis tasks
- Parallel Processing: Run Catalyst on call #1, #2, #3... #45 in parallel (5-10 concurrent)
- Per-Call Analysis: For each call, Catalyst identifies:
- What went wrong in the conversation
- Which part of the prompt caused the issue
- Specific text changes to fix it
- Confidence score (high/medium/low)
- Aggregation: Combine all 45 individual analyses to find patterns
- Frequency Analysis: If 38 out of 45 calls recommend the same change, that's a high-confidence fix
- Document Generation: Create cumulative prompt changes with prioritized recommendations
Cumulative Prompt Changes Document includes:
- High-Confidence Changes (recommended by 70%+ of calls): Must-do fixes
- Medium-Confidence Changes (40-69%): Consider including
- Low-Confidence Changes (<40%): Edge cases, review carefully
- Specific text modifications: Exact before/after prompt text
- Implementation instructions: Where in the codebase to make changes
- Expected impact: Estimated improvement in success rate
Issue: Bot doesn't understand relative dates
Current Prompt: "Ask the customer for their preferred appointment date."
Recommended Change: "Ask the customer for their preferred appointment date. If they use relative terms like 'next Tuesday', 'tomorrow', or 'the 15th', confirm the exact date by stating the full date (e.g., 'So that would be Tuesday, December 3rd, 2024 - is that correct?')"
Expected Impact: Fix 32-36 of the 45 failed calls (~80% reduction)
MEDIUM CONFIDENCE (22/45 calls = 49%)
Issue: Bot doesn't handle timezone ambiguity
Recommendation: Add timezone clarification for customers in different regions...
Built using Bull queue (Redis), Node.js workers, GPT-4 for Catalyst analysis, and MongoDB for storing results. Progress tracking UI shows real-time status (e.g., "Processing: 23/45 calls complete").
Prompt Engineering & Code Generation
What it does: Takes the cumulative prompt changes document from Phase 3 and transforms it into actual, deployable code. This is where natural language recommendations ("improve date parsing") become concrete code changes in the scheduler bot's prompt files. The system generates exact text to add/modify/remove, identifies the correct file locations, and creates a reviewable diff.
Why it matters: The gap between "we know what to fix" and "the fix is implemented" is where manual errors occur. An analyst might misinterpret a recommendation, edit the wrong prompt section, or introduce syntax errors. Automated code generation eliminates these errors and includes built-in validation.
- Cumulative prompt changes document from Phase 3
- Current codebase state (prompt files, system instructions)
- Bot prompt template structure and syntax rules
- Deployment metadata (intent, priority class, timestamp)
- File Location Mapping: Identify which prompt files need changes (e.g.,
prompts/scheduler-system.txt) - Text Transformation: Convert recommendations to exact code:
- "Improve date parsing" → Actual prompt text with examples
- Preserve existing prompt structure and tone
- Maintain consistency with style guide
- Diff Generation: Create before/after comparison with syntax highlighting
- Validation: Check for:
- Syntax errors (malformed JSON, broken templates)
- Length limits (OpenAI token limits)
- Required field presence (must have system prompt)
- Compatibility with bot framework
- Patch File Creation: Generate git-compatible patch file for version control
- Interactive Diff Viewer: Side-by-side before/after comparison with line numbers
- Git Patch File: Ready to apply with
git apply prompt-fix-reschedule-p1.patch - Rollback Script: Automatic revert if deployment fails
- Change Summary: Human-readable description of all modifications
- Test Cases: Suggested test scenarios to validate changes work
+++ b/prompts/scheduler-system.txt
@@ -15,7 +15,12 @@
You are a helpful scheduling assistant.
- Ask the customer for their preferred appointment date.
+ Ask the customer for their preferred appointment date.
+ If they use relative terms like 'next Tuesday', 'tomorrow',
+ or 'the 15th', confirm the exact date by stating the full
+ date (e.g., 'So that would be Tuesday, December 3rd, 2024
+ - is that correct?').
Wait for customer confirmation before proceeding.
While code generation is automated, a human analyst must review and approve the changes before deployment. This ensures AI-generated code aligns with business requirements and doesn't introduce unintended side effects.
Deployment Pipeline & Version Control
What it does: Automates the entire deployment process from code approval to production release. Once an analyst approves the generated code changes from Phase 4, this system handles git commits, staging deployment, automated testing, A/B test configuration, production rollout, and monitoring setup - all without manual intervention.
Why it matters: Manual deployments are error-prone and slow. An analyst might forget to tag the deployment, misconfigure A/B testing, or skip staging validation. Automated pipelines enforce best practices every time, reduce deployment time from 4 hours to 15 minutes, and create perfect audit trails for compliance.
- Approved code changes from Phase 4 (git patch file)
- Deployment configuration (A/B split %, rollout strategy)
- Baseline metrics from current production (for comparison)
- Deployment metadata (intent, priority, analyst approval timestamp)
- Git Commit & Tag:
- Create feature branch:
fix/reschedule-p1-date-parsing - Commit with metadata: intent, priority, Catalyst run ID
- Tag:
v1.2.3-reschedule-p1-20241127
- Create feature branch:
- Staging Deployment:
- Deploy to staging environment
- Run automated test suite (synthetic call scenarios)
- Validate prompt length, response times, success rates
- If tests fail, auto-rollback and notify analyst
- A/B Test Configuration:
- Setup: 20% of calls get new prompt, 80% get old prompt
- Configure metric tracking for both groups
- Set automatic rollback triggers (if new prompt performs worse)
- Production Deployment:
- Gradual rollout: 20% → 50% → 100% over 48 hours
- Monitor error rates at each stage
- Automatic pause if anomalies detected
- Monitoring Tag Creation:
- Tag all calls with deployment ID for tracking
- Enable before/after comparison in Phase 6
- Store baseline metrics for success measurement
- Deployment Record: Complete audit trail with timestamps, approvals, test results
- Git History: Full version control with ability to revert to any previous version
- A/B Test Dashboard: Real-time comparison of old vs new prompt performance
- Rollback Script: One-click revert if issues arise
- Notification System: Slack/email alerts at each deployment stage
Split: 20% new prompt (Variant B) vs 80% control (Variant A)
Baseline Metrics (Variant A):
- Success Rate: 62.5% (75/120 calls)
- Avg Duration: 3m 42s
- Dropout Stage: Date confirmation (70% of failures)
Target Metrics (Variant B):
- Success Rate: >80% (statistically significant improvement)
- Avg Duration: <4 minutes (no regression)
- Dropout Stage: Date confirmation <40% of failures
Auto-Rollback Trigger: If Variant B success rate <60% after 50 calls
All deployments require two-factor approval (analyst + manager for P1-Critical), maintain complete audit logs for SOC 2 compliance, and include automated PII scrubbing for test scenarios. Rollback capability available for 30 days.
Real-Time Monitoring & Impact Analysis
What it does: After a prompt change deploys to production (Phase 5), this system continuously monitors its performance through the dedicated "Monitoring" dashboard tab. It compares the new prompt's metrics against baseline performance, tracks A/B test results, detects anomalies, and generates automated impact reports showing exactly how much the change improved (or hurt) call quality.
Why it matters: Without monitoring, we're flying blind. Did our prompt change actually fix the date parsing issue? Did it accidentally break something else? Monitoring provides data-driven answers. It also creates accountability - we can show stakeholders concrete ROI like "This deployment reduced call dropouts by 42%, recovering $4,200 in daily revenue."
- Call Dropout Rate: % of calls that end prematurely
- Tracked before deployment (baseline) vs after deployment
- Broken down by dropout stage (greeting, intent capture, scheduling, confirmation)
- Intent Success Rate: % of calls that complete their intended action
- For "Reschedule" intent: did customer successfully reschedule?
- Separate tracking for each intent type
- Average Call Duration: Time from start to end
- Shorter is better (efficiency), but not at cost of quality
- Alert if duration increases >20% (possible new confusion)
- Revenue Impact: Estimated dollar value of improvements
- Recovered calls × average order value
- Daily and cumulative tracking
- Error Rate: Bot errors, API failures, timeout issues
- Critical for detecting regressions
- Automatic rollback if error rate >5%
- Customer Sentiment: Analysis of customer tone/satisfaction
- Positive, neutral, negative classification
- Detect if changes frustrate customers
- Deployment Timeline View: Visual timeline showing all deployments with before/after metrics
- A/B Comparison Charts: Side-by-side graphs comparing Variant A (old) vs Variant B (new)
- Statistical Significance Testing: Is the improvement real or just random chance?
- Drill-Down Analysis: Click any deployment to see:
- What changed (git diff)
- Who approved it
- All affected calls with transcripts
- Metric trends over time
- Anomaly Detection: Automatic alerts if metrics deviate significantly
- Rollback Button: One-click revert if deployment underperforms
Deployment ID: deploy_reschedule_p1_20241127_143022
Deployed: Nov 27, 2024 2:30 PM CT
Analysis Period: 48 hours (Nov 27-29, 240 calls in Variant B group)
BEFORE (Baseline - Variant A):
- Dropout Rate: 37.5% (45/120 calls)
- Success Rate: 62.5%
- Avg Duration: 3m 42s
- Revenue Lost: ~$6,750/day
AFTER (New Prompt - Variant B):
- Dropout Rate: 16.7% (40/240 calls) ↓ 55% reduction
- Success Rate: 83.3% ↑ 21 points improvement
- Avg Duration: 3m 28s ↓ 14s faster
- Revenue Recovered: ~$3,150/day, $22,050/week
Statistical Significance: p < 0.001 (highly significant)
Recommendation: Roll out to 100% of traffic ✅
ROI: Development cost $2,400 / Weekly recovery $22,050 = Payback in 0.5 days
System sends real-time Slack/email alerts for: (1) Deployment performance beating expectations, (2) Metrics declining below thresholds, (3) Error rate spikes, (4) Statistical significance achieved. Analysts receive daily summary reports for all active A/B tests.
Feedback Loop & Continuous Learning
What it does: Phase 7 closes the intelligence loop. It takes all the performance data from Phase 6 (which changes worked, which didn't, and why) and feeds it back into the Catalyst engine to make future recommendations smarter. Over time, the system learns which types of prompt changes are most effective for specific problems, becoming increasingly accurate and requiring less human oversight.
Why it matters: This transforms the platform from a static tool into a continuously improving AI system. Early deployments might achieve 60-70% success rate improvements. After 6 months of learning, the system might achieve 85-90% improvements because it's accumulated knowledge about what works. This is the difference between automation (doing tasks) and intelligence (getting better at tasks).
- Success Pattern Recognition:
- Track which Catalyst recommendations led to biggest improvements
- Example: "Date clarification prompts consistently reduce dropouts by 40-55%"
- Store these patterns in a knowledge base
- Failure Analysis & Auto-Rollback:
- If deployment performs worse than baseline, automatically roll back
- Analyze why the change failed (wrong problem diagnosis? poor implementation?)
- Flag similar future recommendations for extra scrutiny
- Confidence Scoring Enhancement:
- Catalyst recommendations get confidence scores (high/medium/low)
- Historical success rates adjust these scores over time
- Example: "Tone adjustment" recommendations historically succeed 45% of time → downgrade future tone recommendations to low confidence
- Prompt Template Library:
- Build library of proven prompt patterns for common issues
- "Date parsing problems? Use this template (92% success rate)"
- Accelerates future fixes by reusing what works
- Automated Optimization Suggestions:
- System proactively suggests improvements based on patterns
- "P2-High issues in 'Ask Question' intent show similar patterns to previously fixed P1 in 'Reschedule' - consider applying same fix?"
- Recommendation Accuracy: What % of Catalyst recommendations lead to measurable improvements?
- Time to Resolution: How quickly do we go from problem identified to problem fixed?
- Human Intervention Rate: How often do analysts need to override/modify recommendations?
- Compound Effect: Are newer deployments more successful than earlier ones?
Month 2: Similar date issues detected in "Cancel Appointment" intent. System automatically suggests: "This looks similar to the Reschedule fix from Month 1. Consider applying the same date clarification pattern." Analyst reviews, approves. Deployment succeeds with 62% improvement.
Month 3: System detects date ambiguity in "Ask Question" intent (lower priority). Automatically generates prompt fix using proven template, requests analyst approval. No manual Catalyst run needed - system already knows the solution.
Month 6: System has accumulated 20+ successful date-related fixes. Confidence score for date clarification recommendations increases from 75% to 94%. Future date issues get fixed in 6 hours instead of 24 hours because system skips exploratory analysis phase.
By Q3 2026 (6-9 months after full deployment), we target: (1) 85%+ of prompt fixes requiring <1 hour human review time, (2) 90%+ Catalyst recommendation success rate (up from initial 70%), (3) Automated detection and fixing of 60% of call quality issues with zero human intervention for low-risk changes, (4) Predictive alerts warning "Based on trends, we expect date parsing issues to spike next week - pre-deploy fix now?"
🛠️ Technical Architecture & Requirements
⚙️ Backend Services
- Node.js/Express API - RESTful endpoints
- Catalyst Engine - Batch processing service
- Job Queue - Bull/Redis for async tasks
- Prompt Diff Engine - Code generation
- Deployment Service - Git automation
🤖 AI/ML Stack
- OpenAI GPT-4 - Catalyst analysis
- Claude/GPT-4 - Prompt engineering
- Embedding Models - Similarity search
- Vector Database - Pattern matching
- ML Pipeline - Impact prediction
💾 Data & Storage
- PostgreSQL - Relational data, metrics
- MongoDB - Call transcripts, analysis
- Redis - Caching, job queue
- S3/GCS - Artifact storage
- TimescaleDB - Time-series metrics
🚀 DevOps & Infrastructure
- Docker/Kubernetes - Containerization
- GitHub Actions - CI/CD pipeline
- Terraform - Infrastructure as code
- DataDog/NewRelic - APM monitoring
- Grafana - Metrics visualization
✅ Testing & Quality
- Jest/Mocha - Unit testing
- Cypress - E2E testing
- A/B Testing Framework - Split.io
- Synthetic Monitoring - Call simulation
- Regression Suite - Automated QA
🔒 Security & Compliance
- OAuth 2.0 - Authentication
- RBAC - Role-based access
- Audit Logging - Change tracking
- PII Protection - Data anonymization
- SOC 2 Compliance - Security standards
📅 Development Timeline & Milestones
The timeline follows an agile, iterative approach. Each phase builds upon the previous and delivers working software. We prioritize early wins (Phase 3-4) to demonstrate value before tackling complex infrastructure (Phase 5-7). Total estimated timeline: 12 weeks from kickoff to full deployment.
Key Milestone: Analyst clicks "Run Catalyst" on P1 with 45 calls, system processes all automatically in 15-20 minutes
Success Criteria: 45 calls analyzed with 95%+ Catalyst completion rate, cumulative report generated
Key Milestone: Analyst reviews Catalyst output, system generates deployable code with visual diff
Success Criteria: Generated code passes validation 100% of time, analyst review takes <15 minutes
Key Milestone: Approved code auto-deploys to production with A/B split and monitoring tags
Success Criteria: Zero-touch deployment for P2/P3, two-approval deployment for P1, <15 min deploy time
Key Milestone: Dashboard shows real-time A/B test results with statistical significance calculations
Success Criteria: Track 10+ metrics per deployment, generate impact reports within 24 hours
Key Milestone: System recommends proven fix for new issue based on historical success patterns
Success Criteria: Recommendation accuracy >70%, time-to-fix reduces 40% month-over-month
Expected Outcomes:
- 80% reduction in manual prompt engineering effort (from 16 hours to 3 hours per priority)
- 3x faster time-to-fix (from 2-3 days to 24 hours)
- 50%+ reduction in call dropout rates across all intents
- $2.4M annual revenue recovery from improved call success rates
- Self-improving system that gets better with each deployment
Risk Buffer: Timeline includes 2-week contingency for unexpected technical challenges or scope adjustments.
📊 Success Metrics & KPIs
These metrics define success for the automated prompt optimization platform. Each metric is measurable, time-bound, and directly tied to business value. We track these monthly and report progress to stakeholders.
Target: 6% dropout rate (30/500 calls daily)
Impact: 30 additional successful calls per day = $4,500 daily revenue recovery
Target: 80% automated (3 hours review time per priority)
Impact: 13 hours saved per fix = $180K annual labor cost savings
Target: <24 hours for all P1-Critical issues
Impact: 3x faster resolution = reduced customer frustration, better retention
Target: 87.5% success rate (25 percentage point improvement)
Impact: Higher customer satisfaction, increased lifetime value
Additional Tracking Metrics
Platform is considered successful if: (1) All four primary KPIs meet or exceed targets by Q2 2026, (2) Platform pays for itself within 3 months through revenue recovery and cost savings, (3) Analysts rate the system 8/10 or higher for usefulness, (4) System handles 10x current call volume without performance degradation, (5) Zero critical security incidents or data breaches.
⚠️ Risks & Mitigation Strategies
Every complex system has risks. We identify them proactively and implement mitigation strategies to minimize impact. Risk management is continuous - we reassess monthly and adjust strategies as needed.
Description: An AI-generated prompt change intended to fix "Reschedule" issues accidentally breaks "Cancel Appointment" functionality. Customers who could successfully cancel before now encounter errors. Call dropout rate increases instead of decreasing.
Probability: Medium-High (especially in early deployments)
Impact: High (customer frustration, revenue loss, team credibility damage)
Mitigation Strategy:
- Automated Testing Suite: 100+ synthetic test scenarios covering all intents, run before every deployment
- Staging Environment: All changes deployed to staging first, validated with test calls
- A/B Testing: New prompts start at 20% traffic, not 100%, limiting blast radius
- Statistical Monitoring: Automatic alerts if any metric degrades >10% in A/B group
- One-Click Rollback: Instant revert capability available 24/7, restores previous working state in <2 minutes
- Human Review Gate: P1-Critical changes require dual analyst approval before deployment
Description: Call transcripts are incomplete, mislabeled, or contain corrupted data. Catalyst analyzes bad data and generates incorrect recommendations. Example: Bot transcript shows "Customer said: [AUDIO_ERROR]", leading to wrong diagnosis.
Probability: Medium (data pipelines have ~2-5% error rate)
Impact: Medium (wasted effort on bad fixes, potential prompt degradation)
Mitigation Strategy:
- Data Validation Pipeline: Reject transcripts with missing fields, too short/long duration, or error markers
- Confidence Scoring: Catalyst assigns confidence levels; low-confidence recommendations flagged for extra scrutiny
- Manual Review Threshold: If >30% of calls in a priority have data quality issues, require human review before processing
- Data Quality Dashboard: Track transcript completeness, error rates, data freshness metrics
- Upstream Monitoring: Alert on data pipeline failures, missing feeds, format changes
Description: Platform designed for 500 calls/day struggles when call volume spikes to 2,000 calls/day during peak season (Black Friday, holidays). Catalyst queue backlogs, processing takes 8+ hours instead of 20 minutes. Analysts can't get timely analysis.
Probability: Medium (call volume variability is expected)
Impact: Medium (delayed fixes, but not system failure)
Mitigation Strategy:
- Distributed Processing: Catalyst workers run in parallel across multiple servers; easily scale horizontally
- Queue-Based Architecture: Bull/Redis queue handles bursts gracefully; jobs wait in queue rather than failing
- Cloud Auto-Scaling: Kubernetes automatically adds worker pods when queue depth >100 jobs
- Priority Queue System: P1-Critical analyses jump to front of queue, ensuring urgent issues processed first
- Load Testing: Quarterly performance tests simulate 5,000 calls/day to validate headroom
- Caching Strategies: Cache Catalyst results for similar calls, reducing redundant API calls to OpenAI
- Cost Alerts: Monitor OpenAI API costs; alert if spending exceeds budget (prevents runaway costs)
Description: We deploy a prompt change that technically "succeeds" (call completes) but creates terrible customer experience. Example: Bot becomes overly verbose, calls take 8 minutes instead of 4. Success rate looks good, but customers are frustrated by wasted time.
Probability: Medium (easy to miss non-obvious regressions)
Impact: Medium-High (degraded UX, customer churn, brand damage)
Mitigation Strategy:
- Comprehensive Metric Coverage: Track 10+ metrics per deployment (duration, sentiment, error rate, etc.), not just success rate
- Anomaly Detection: ML models detect unusual patterns (e.g., "calls are succeeding but taking 2x longer - investigate")
- Customer Sentiment Analysis: Analyze call transcripts for frustration indicators ("I don't understand", "This is taking too long")
- Holistic Success Definition: Define "success" as: call completes + duration <5 min + positive sentiment + no errors
- Automated Alerting: Real-time alerts for any metric deviating >20% from baseline
- Human Spot Checks: Analysts manually review 5 random calls per deployment to catch issues metrics miss
- Quarterly Metric Review: Reassess what we're measuring, add new metrics as needed
- Regular Risk Reviews: Monthly team meeting to discuss new risks, reassess probabilities, update mitigation plans
- Incident Response Plan: Documented playbook for critical failures (who to call, how to roll back, communication protocols)
- Post-Mortem Process: After any production incident, write detailed post-mortem with root cause, lessons learned, prevention steps
- Security Audits: Quarterly penetration testing, annual SOC 2 audit, continuous vulnerability scanning
- Disaster Recovery: Daily backups, tested recovery procedures, documented RTO (Recovery Time Objective) of <4 hours