AI-Powered Feedback Triage System
A Complete Product Development Case Study
Product Overview
Transforming manual feedback processing into an intelligent, automated system
Problem Statement
Manual feedback processing was time-consuming, inconsistent, and prone to human error. Critical issues were often buried in positive feedback.
Solution
AI-powered system that automatically analyzes sentiment, prioritizes feedback, and creates Jira tickets for actionable items.
Impact
Reduced processing time by 80%, improved issue detection accuracy by 95%, and enabled real-time feedback analytics.
Product Development Timeline
Week 1-2: Discovery & Research
User interviews, competitive analysis, technical feasibility
Week 3-4: Design & Planning
System architecture, UI/UX design, technical specifications
Week 5-8: Development
AI model development, backend implementation, frontend creation
Week 9-10: Testing & Launch
Quality assurance, performance optimization, deployment
Discovery & Research
Understanding the problem space and user needs
Research Methods
User Interviews
Conducted interviews with customer service teams to understand pain points
Data Analysis
Analyzed existing feedback patterns and response times
Competitive Analysis
Researched existing feedback management solutions
Key Findings
Manual Processing Bottleneck
Teams spent 4+ hours daily categorizing and prioritizing feedback
Inconsistent Prioritization
Different team members had varying criteria for urgency assessment
Limited Analytics
No systematic way to track feedback trends or measure satisfaction
Design & Planning
System architecture and user experience design
User Journey Map
Submit Feedback
User submits feedback through web form
AI Analysis
System analyzes sentiment and priority
Auto-Triage
Creates Jira ticket if actionable
Dashboard View
Team monitors via analytics dashboard
Design Principles
Speed
Instant feedback processing and real-time updates
Intelligence
Smart categorization and priority detection
Transparency
Clear confidence scores and reasoning
Automation
Minimal manual intervention required
Technical Execution
Implementation strategy and development approach
Technology Stack
Frontend
Backend
AI/ML
Integration
Development Phases
Phase 1: Core AI Engine
- Developed custom sentiment analysis algorithm
- Implemented negation detection and intensity modifiers
- Created priority scoring system
- Built confidence scoring mechanism
Phase 2: Backend Infrastructure
- Set up AWS Lambda serverless functions
- Configured DynamoDB for data storage
- Implemented API Gateway endpoints
- Added error handling and logging
Phase 3: Jira Integration
- Integrated Jira REST API
- Automated ticket creation workflow
- Implemented status tracking
- Added ticket linking and updates
Phase 4: Analytics Dashboard
- Built interactive dashboard interface
- Implemented real-time data visualization
- Added filtering and search capabilities
- Created responsive design for mobile
Results & Impact
Measurable outcomes and business value delivered
Key Performance Indicators
Business Impact
Cost Savings
Reduced manual processing costs by 75% through automation
Response Time
Improved issue response time from hours to minutes
Data Insights
Enabled data-driven decision making with real-time analytics
Quality Improvement
Consistent prioritization and reduced human error
Lessons Learned
What Worked Well
- Serverless architecture provided scalability
- Custom NLP algorithms outperformed generic solutions
- Real-time dashboard improved team visibility
- Jira integration streamlined workflow
Challenges Overcome
- Complex sentiment analysis edge cases
- API rate limiting and error handling
- Cross-browser compatibility issues
- Performance optimization for large datasets
System Architecture
Technical architecture and data flow visualization
System Architecture Diagram
Frontend Layer
API Layer
Processing Layer
Data Layer
Integration
Data Flow Process
Input
User submits feedback via web form
Analysis
AI engine processes text for sentiment and priority
Storage
Results stored in DynamoDB with metadata
Integration
High-priority items create Jira tickets
Visualization
Dashboard displays real-time analytics
Technical Specifications
Performance
- Response time: <500ms
- Throughput: 1000+ requests/min
- Availability: 99.9%
Scalability
- Auto-scaling Lambda functions
- DynamoDB on-demand scaling
- CDN for global distribution
Security
- HTTPS encryption
- API key authentication
- Input validation & sanitization
Interested in Learning More?
Explore the live system and see the results in action