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Support Copilot - AI-Powered Issue Lifecycle Management

Overview

Support Copilot is an AI-powered system designed to assist support executives throughout the entire issue lifecycle. The system analyzes incoming support issues, provides actionable insights, recommends communication templates, and monitors issue status to ensure timely resolution.

Features

🔍 Issue Intake & Analysis

  • Automatic Issue Classification: AI-powered severity detection (Low, Normal, High, Critical)
  • Customer History Analysis: Retrieves past issues and customer interaction patterns
  • Similar Issue Detection: Finds related issues and their resolutions using ML similarity matching
  • Critical Issue Monitoring: Flags unattended critical issues after 24 hours

💬 Support Executive Guidance

  • Smart Template Generation: AI-generated response templates based on issue context
  • Context-Aware Recommendations: Considers customer history, issue severity, and past resolutions
  • Multi-Category Templates: Initial response, status updates, escalations, resolutions, clarifications
  • Template Effectiveness Tracking: Monitors and improves template performance

📊 Conversation Summarization

  • Automatic Summarization: AI-powered conversation summaries for knowledge base updates
  • Action Item Extraction: Identifies pending tasks and follow-up requirements
  • Resolution Documentation: Comprehensive resolution summaries for future reference
  • Knowledge Base Integration: Updates searchable knowledge base with resolved issues

🚀 Performance & Integration

  • Real-time API: RESTful endpoints with <15 second response times
  • Scalable Architecture: Microservices-based design on AWS
  • Comprehensive Monitoring: Performance metrics, alerting, and health checks
  • Security: JWT authentication, encryption, and GDPR compliance

Technical Architecture

System Components

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   API Gateway   │    │  Load Balancer  │    │   Web Client    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
         ┌───────────────────────▼───────────────────────┐
         │              Core Services                    │
         │  ┌─────────────┐ ┌─────────────┐ ┌──────────┐ │
         │  │   Issue     │ │  Guidance   │ │Summary   │ │
         │  │  Analysis   │ │   Service   │ │ Service  │ │
         │  └─────────────┘ └─────────────┘ └──────────┘ │
         └───────────────────────┼───────────────────────┘
                                 │
         ┌───────────────────────▼───────────────────────┐
         │              Data Layer                       │
         │  ┌─────────────┐ ┌─────────────┐ ┌──────────┐ │
         │  │   MySQL     │ │    Redis    │ │OpenSearch│ │
         │  │  Database   │ │    Cache    │ │ Vector   │ │
         │  └─────────────┘ └─────────────┘ └──────────┘ │
         └───────────────────────────────────────────────┘

Technology Stack

  • Backend: Python 3.11, Flask, SQLAlchemy
  • Database: MySQL 8.0 with connection pooling
  • Cache: Redis for performance optimization
  • AI/ML: OpenAI GPT-4/3.5-turbo, scikit-learn
  • Search: Vector similarity using embeddings
  • Infrastructure: AWS ECS Fargate, RDS, ElastiCache
  • Monitoring: CloudWatch, Prometheus, Grafana
  • Security: JWT authentication, bcrypt hashing

Quick Start

Prerequisites

  • Python 3.11+
  • MySQL 8.0+
  • Redis 6.0+
  • OpenAI API Key
  • Docker (optional)

Local Development Setup

  1. Clone the repository

    git clone <repository-url>
    cd support-copilot
  2. Install dependencies

    pip install -r requirements.txt
  3. Set up environment variables

    cp .env.example .env
    # Edit .env with your configuration
  4. Initialize database

    mysql -u root -p < database_schema.sql
  5. Run the application

    python app.py

Docker Setup

  1. Start all services

    docker-compose up -d
  2. Check service health

    docker-compose ps
    curl http://localhost:5000/health

AWS Deployment

  1. Deploy infrastructure

    aws cloudformation create-stack \
      --stack-name support-copilot-prod \
      --template-body file://aws_deployment.yml \
      --parameters ParameterKey=Environment,ParameterValue=production \
                   ParameterKey=OpenAIAPIKey,ParameterValue=your-api-key \
      --capabilities CAPABILITY_IAM
  2. Build and push Docker image

    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <account>.dkr.ecr.us-east-1.amazonaws.com
    docker build -t support-copilot .
    docker tag support-copilot:latest <account>.dkr.ecr.us-east-1.amazonaws.com/support-copilot:latest
    docker push <account>.dkr.ecr.us-east-1.amazonaws.com/support-copilot:latest

API Documentation

Authentication

All API endpoints require JWT authentication. Obtain a token by logging in:

curl -X POST http://localhost:5000/auth/login \
  -H "Content-Type: application/json" \
  -d '{"email": "[email protected]", "password": "admin123"}'

Include the token in subsequent requests:

curl -H "Authorization: Bearer <token>" http://localhost:5000/api/issues

Core Endpoints

Issue Analysis

# Analyze new issue
POST /api/issues/analyze
{
  "customer_id": 1,
  "title": "Login authentication failure",
  "description": "Users cannot log in after recent update",
  "category": "Technical",
  "product_area": "Authentication"
}

# Get similar issues
GET /api/issues/{issue_id}/similar?limit=5

# Get customer history
GET /api/customers/{customer_id}/history

Guidance & Templates

# Generate response template
POST /api/guidance/template
{
  "issue_id": 123,
  "message_content": "I need help with login issues",
  "context": "Customer is frustrated"
}

# Get available templates
GET /api/guidance/templates?category=initial_response&severity=High

Conversation Management

# Generate conversation summary
POST /api/issues/{issue_id}/summarize

# Update issue status
PUT /api/issues/{issue_id}/status
{
  "status": "Resolved"
}

Monitoring & Alerts

# Get critical alerts
GET /api/alerts/critical

# Acknowledge alert
POST /api/alerts/{alert_id}/acknowledge

# Get dashboard analytics
GET /api/analytics/dashboard

Configuration

Environment Variables

# Database Configuration
DB_HOST=localhost
DB_PORT=3306
DB_USER=support_user
DB_PASSWORD=support_password
DB_NAME=support_copilot

# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=

# AI Configuration
OPENAI_API_KEY=your-openai-api-key
OPENAI_MODEL=gpt-3.5-turbo

# Security
SECRET_KEY=your-secret-key
JWT_EXPIRY_HOURS=24

# Performance
MAX_RESPONSE_TIME_SECONDS=15
RATE_LIMIT_PER_MINUTE=100

# AWS Configuration (for production)
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_REGION=us-east-1
S3_BUCKET=support-copilot-files

Feature Flags

# Enable/disable features
ENABLE_AI_ANALYSIS=True
ENABLE_SIMILARITY_SEARCH=True
ENABLE_AUTO_SUMMARIZATION=True
ENABLE_METRICS=True

Database Schema

Core Tables

  • customers: Customer information and tier management
  • issues: Issue tracking with severity, status, and metadata
  • conversations: Message history and sentiment analysis
  • message_templates: Reusable response templates
  • issue_resolutions: Resolution documentation and satisfaction
  • similar_issues: ML-powered issue similarity mapping
  • critical_alerts: Alert management and escalation
  • support_executives: Team member management

Key Relationships

customers (1) ──── (many) issues
issues (1) ──── (many) conversations
issues (1) ──── (1) issue_resolutions
issues (many) ──── (many) similar_issues
issues (1) ──── (many) critical_alerts

Testing

Run Tests

# Run all tests
pytest test_api.py -v

# Run specific test categories
pytest test_api.py::TestSupportCopilotAPI -v
pytest test_api.py::TestPerformance -v
pytest test_api.py::TestIntegration -v

# Run with coverage
pytest test_api.py --cov=. --cov-report=html

Test Categories

  • Unit Tests: Individual component testing
  • Integration Tests: End-to-end workflow testing
  • Performance Tests: Response time validation
  • Security Tests: Authentication and authorization
  • API Tests: Endpoint functionality and error handling

Monitoring & Observability

Health Checks

# Application health
GET /health

# Database health
curl http://localhost:5000/health | jq '.database_status'

# Cache health
curl http://localhost:5000/health | jq '.cache_status'

Metrics

  • Response Time: API endpoint performance
  • Error Rate: Failed request percentage
  • Issue Volume: Daily issue creation trends
  • Resolution Time: Average time to resolve issues
  • Customer Satisfaction: Resolution quality metrics
  • AI Performance: Model accuracy and response times

Alerting

  • Response time > 15 seconds
  • Error rate > 1%
  • Critical issues unattended > 24 hours
  • Database connection failures
  • High memory/CPU usage

Security

Authentication & Authorization

  • JWT-based stateless authentication
  • Role-based access control (Admin, Support, Read-only)
  • API key management for external integrations
  • Session timeout and token refresh

Data Protection

  • Encryption at rest (database, file storage)
  • Encryption in transit (TLS 1.3)
  • PII data anonymization
  • GDPR compliance features
  • Audit logging for all operations

Infrastructure Security

  • VPC isolation with private subnets
  • Security groups with minimal access
  • WAF protection against common attacks
  • Regular security updates and patches

Performance Optimization

Caching Strategy

  • Customer Data: 5-minute cache for frequently accessed profiles
  • Issue Analysis: 30-minute cache for analysis results
  • Templates: 2-hour cache for template data
  • Similar Issues: 1-hour cache for similarity results

Database Optimization

  • Connection pooling for concurrent requests
  • Read replicas for query-heavy operations
  • Proper indexing on frequently queried columns
  • Query optimization and monitoring

API Performance

  • Async processing for long-running operations
  • Request/response compression
  • CDN for static content delivery
  • Load balancing across multiple instances

Troubleshooting

Common Issues

  1. Database Connection Errors

    # Check database status
    mysql -u root -p -e "SELECT 1"
    
    # Verify connection parameters
    echo $DB_HOST $DB_PORT $DB_USER
  2. Redis Connection Issues

    # Test Redis connectivity
    redis-cli ping
    
    # Check Redis logs
    docker logs support_copilot_redis
  3. OpenAI API Errors

    # Verify API key
    curl -H "Authorization: Bearer $OPENAI_API_KEY" \
         https://api.openai.com/v1/models
    
    # Check rate limits
    curl -I https://api.openai.com/v1/chat/completions
  4. High Response Times

    # Check system resources
    docker stats
    
    # Monitor database queries
    mysql -e "SHOW PROCESSLIST"
    
    # Check cache hit rates
    redis-cli info stats

Logs

# Application logs
tail -f logs/support_copilot.log

# Error logs
tail -f logs/support_copilot_errors.log

# Performance logs
tail -f logs/support_copilot_performance.log

# Docker logs
docker-compose logs -f api

Contributing

Development Workflow

  1. Fork the repository
  2. Create a feature branch
  3. Make changes with tests
  4. Run test suite
  5. Submit pull request

Code Standards

  • Follow PEP 8 style guidelines
  • Add docstrings to all functions
  • Include unit tests for new features
  • Update documentation for API changes

Commit Guidelines

feat: add new issue analysis endpoint
fix: resolve database connection timeout
docs: update API documentation
test: add integration tests for templates
refactor: optimize similarity search algorithm

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For technical support or questions:

Roadmap

Version 2.0 (Q2 2024)

  • Multi-language support
  • Advanced sentiment analysis
  • Predictive issue escalation
  • Customer satisfaction prediction

Version 2.1 (Q3 2024)

  • Voice-to-text integration
  • Real-time collaboration features
  • Advanced analytics dashboard
  • Mobile application support

Version 3.0 (Q4 2024)

  • Multi-tenant architecture
  • Advanced AI models (GPT-4, Claude)
  • Workflow automation
  • Third-party integrations (Slack, Teams, Jira)

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