Executive Overview
The StateSet Synthetic Data Studio is an agentic AI platform that combines cutting-edge machine learning techniques with enterprise-grade infrastructure. Built around the innovative Group Relative Policy Optimization (GRPO) algorithm, the platform enables organizations to train, optimize, and deploy sophisticated conversational AI agents.Key Architectural Principles
Microservices-based
Modular, scalable, and maintainable architecture
Cloud-native
Kubernetes-ready with auto-scaling capabilities
Event-driven
Real-time processing with WebSocket support
API-first
RESTful APIs with GraphQL support planned
Security-first
Multi-layer security with encryption and authentication
Performance-optimized
Sub-200ms API response times at scale
System Architecture
High-Level Architecture
Component Communication
- Synchronous Flow
- Asynchronous Flow
Technology Stack
Frontend Stack
Core Technologies
- Framework: React 18 with TypeScript
- State Management: Redux Toolkit + RTK Query
- UI Components: Ant Design (antd)
- Styling: Tailwind CSS + Custom CSS
- Build Tools: Create React App with Craco
Supporting Libraries
- Real-time: Socket.io Client
- Charts: Recharts, Apache ECharts
- Code Editor: Monaco Editor
- Forms: React Hook Form
- Testing: Jest + React Testing Library
Backend Stack
Core Technologies
- Framework: FastAPI (Python 3.9+)
- ASGI Server: Uvicorn
- Database: PostgreSQL 14+ with SQLAlchemy
- Cache: Redis 7+ (multi-layer caching)
- Queue: Celery with Redis broker
ML & Infrastructure
- ML Framework: PyTorch + Transformers
- File Storage: S3-compatible object storage
- WebSockets: FastAPI WebSocket support
- Monitoring: Prometheus + Grafana
- Logging: ELK Stack
Infrastructure Stack
Core Components
1. GRPO Training Engine
The heart of the platform, implementing Group Relative Policy Optimization:- Architecture
- Key Features
2. Synthetic Data Generation Pipeline
Pipeline Components:Document Processor
Document Processor
- Handles multiple formats (PDF, DOCX, TXT, HTML)
- Intelligent content extraction
- Metadata preservation
- Chunking strategies for large documents
Prompt Generator
Prompt Generator
- Template-based prompt construction
- Dynamic variable injection
- Context-aware prompting
- Multi-language support
Generation Engine
Generation Engine
- Async LLM API calls with retry logic
- Load balancing across providers
- Token optimization
- Response caching
Quality Filter
Quality Filter
- Rule-based validation
- ML-powered quality scoring
- Duplicate detection
- Consistency checking
3. Agent Deployment Service
4. Real-time Communication Layer
Features:- Connection pooling and management
- Heartbeat monitoring (30s intervals)
- Message queuing with delivery guarantees
- Horizontal scaling with Redis clustering
- Graceful reconnection handling
Data Flow Architecture
Training Data Flow
1
Document Upload
Raw documents uploaded to S3-compatible storage
2
Processing Pipeline
Documents processed through extraction pipeline
3
Synthetic Generation
LLM generates variations based on templates
4
Quality Curation
ML models filter and score generated data
5
Training Preparation
Data formatted for GRPO training
6
Model Training
GRPO engine trains on prepared data
Request Processing Flow
API Gateway Features
Security Features
- Rate Limiting: Token bucket algorithm
- Authentication: JWT with refresh tokens
- Authorization: RBAC + ABAC
- Input Validation: Pydantic models
- CORS: Configurable origins
Performance Features
- Response Caching: ETag support
- Compression: Gzip/Brotli
- Connection Pooling: Keep-alive
- Load Balancing: Round-robin/least-conn
- Circuit Breaker: Fault tolerance
Security Architecture
Multi-Layer Security Model
Security Components
Authentication Service
Authentication Service
Authorization Service
Authorization Service
Data Security
Data Security
Audit & Compliance
Audit & Compliance
Performance & Scalability
Performance Optimizations
- Frontend Performance
- Backend Performance
- Training Performance
Caching Strategy
Scalability Architecture
Horizontal Scaling
- Stateless services
- Load balancing with health checks
- Auto-scaling based on metrics
- Session affinity when needed
Vertical Scaling
- Resource limits and requests
- Memory-optimized instances for ML
- GPU instances for training
- Burst capacity handling
Data Scaling
- Database sharding strategies
- Time-series data partitioning
- Object storage for large files
- CDN for static assets
Performance Metrics
Development Guidelines
Coding Standards
- Python Backend
- TypeScript Frontend
- API Design
Testing Architecture
Unit Testing
Unit Testing
Integration Testing
Integration Testing
E2E Testing
E2E Testing
Performance Testing
Performance Testing
Future Architecture Roadmap
Phase 1: Foundation Enhancement (Q1 2025)
1
GraphQL API Implementation
2
Service Mesh Integration
- Istio deployment for traffic management
- mTLS for service-to-service communication
- Advanced traffic routing and canary deployments
3
Advanced Monitoring
- Distributed tracing with OpenTelemetry
- Custom metrics and SLI/SLO tracking
- AI-powered anomaly detection
4
Multi-tenancy Support
- Namespace isolation in Kubernetes
- Resource quotas per tenant
- Tenant-specific data segregation
Phase 2: Advanced Features (Q2 2025)
Multi-modal Support
- Text + Vision model training
- Audio processing capabilities
- Cross-modal synthetic data
Federated Learning
- Privacy-preserving training
- Edge device support
- Differential privacy integration
Edge Deployment
- Model optimization for edge
- ONNX runtime support
- Mobile SDK development
AutoML Features
- Automated hyperparameter tuning
- Neural architecture search
- Automatic feature engineering
Phase 3: Enterprise Scale (Q3 2025)
- Global CDN Integration: CloudFlare/Fastly integration
- Disaster Recovery: Multi-region failover, automated backups
- Compliance Certifications: SOC2, HIPAA, ISO 27001
- White-label Support: Customizable branding and domains
Phase 4: Innovation (Q4 2025)
- Quantum-ready Algorithms: Hybrid classical-quantum training
- Neuromorphic Computing: Support for brain-inspired chips
- Explainability Dashboard: SHAP/LIME integration
- Self-optimizing Infrastructure: AI-driven resource management
Architecture Decision Records (ADRs)
ADR-001: Microservices Architecture
ADR-001: Microservices Architecture
Status: Accepted
Date: 2024-10-15Context: Need for scalable, maintainable system that can evolve independentlyDecision: Adopt microservices architecture with clear service boundariesConsequences:
Date: 2024-10-15Context: Need for scalable, maintainable system that can evolve independentlyDecision: Adopt microservices architecture with clear service boundariesConsequences:
- ✅ Better scalability and team autonomy
- ✅ Technology flexibility per service
- ❌ Increased operational complexity
- ❌ Network latency between services
ADR-002: GRPO Algorithm Implementation
ADR-002: GRPO Algorithm Implementation
Status: Accepted
Date: 2024-11-01Context: Need for stable, efficient RL training without critic model overheadDecision: Implement custom GRPO with group-relative advantagesConsequences:
Date: 2024-11-01Context: Need for stable, efficient RL training without critic model overheadDecision: Implement custom GRPO with group-relative advantagesConsequences:
- ✅ 50% memory savings vs PPO
- ✅ Faster convergence
- ❌ Custom implementation maintenance
- ❌ Less community support
ADR-003: Multi-Layer Caching
ADR-003: Multi-Layer Caching
Status: Accepted
Date: 2024-11-20Context: Need for high performance at scale with <200ms response timesDecision: Implement L1 (memory) + L2 (Redis) + L3 (DB) cachingConsequences:
Date: 2024-11-20Context: Need for high performance at scale with <200ms response timesDecision: Implement L1 (memory) + L2 (Redis) + L3 (DB) cachingConsequences:
- ✅ Sub-millisecond response times
- ✅ Reduced database load
- ❌ Cache invalidation complexity
- ❌ Memory overhead
ADR-004: Event-Driven Architecture
ADR-004: Event-Driven Architecture
Status: Accepted
Date: 2024-12-05Context: Need for real-time updates and loose service couplingDecision: Use Redis Pub/Sub for event propagation with WebSocketsConsequences:
Date: 2024-12-05Context: Need for real-time updates and loose service couplingDecision: Use Redis Pub/Sub for event propagation with WebSocketsConsequences:
- ✅ Real-time user experience
- ✅ Decoupled services
- ❌ Event ordering challenges
- ❌ Potential message loss
Conclusion
The Synthetic Data Studio architecture represents a world-class platform that combines cutting-edge AI research with enterprise-grade engineering. The architecture delivers:Technical Excellence
- Performance: Sub-200ms API responses
- Scalability: 10,000+ concurrent users
- Reliability: 99.9% uptime SLA
- Security: Multi-layer protection
Business Value
- Time to Market: Rapid deployment
- Cost Efficiency: Optimized resource usage
- Flexibility: Adapt to changing needs
- Innovation: Future-ready platform
Architecture Team Contact: For questions or contributions to this architecture guide, please contact the Platform Architecture Team at architecture@stateset.com