Technical Overview

Comprehensive technical architecture overview for TRAST's AI-powered Web3 intelligence platform, focusing on scalable infrastructure, real-time processing, and advanced AI capabilities.

Architecture overview

TRAST is built on a modern, scalable architecture for real-time analysis and pattern recognition. Key components include:

├── Supabase (PostgreSQL) managed database
├── Heroku auto-scaling deployment
├── OpenAI/LLMService for AI features
├── Real-time processing (WebSocket)
├── Entity-centric data model
├── Button-based UI (Telegram)
├── GitHub (code) & GitBook (docs)
└── Security & monitoring

Key features

AI-powered analysis

• OpenAI/LLMService for chat, entity search, and risk analysis

• Contextual memory for AI chat

• Automated trust and risk signals

• Scam detection

Data processing

• Real-time data ingestion and updates

• Inline search and quick add for entities

• Trending and discovery tools

• Historical data analysis

Security & privacy

• End-to-end encryption

• Role-based access control

• Regular security audits

• Privacy-first design

Integration

API Reference

• REST API Documentation
• Websocket events
• Authentication

SDKs & libraries

• Python SDK

• JavaScript SDK

• Mobile SDKs (iOS/Android)

Development

Getting started

• Local development setup
• Contributing guidelines
• Code standards

Deployment

• Deployment guide
• Configuration
• Monitoring

Performance

Scalability

• Heroku auto-scaling

• Supabase egress/MAU quotas

• Load balancing

• Cache optimization

Monitoring

• Real-time metrics

• Performance tracking

• Error reporting

• System health checks

Security

Best practices

• Security protocols

• Data encryption

• Access control

• Audit logging

Compliance

• Data protection

• Privacy standards

• Industry regulations

• Security certifications

---

For implementation details, API documentation, and more, explore the sections above.