TRAST Documentation
TRAST.spaceConnect
  • Introduction
    • What is TRAST?
    • TRAST Litepaper
    • Trust System
    • Mechanics
    • Core Concepts
  • Getting Started
    • Quick Start Guide
    • Beta Information
    • Understanding Trust Scores
  • Platform Features
    • Platform Overview
    • Core Infrastructure
      • Vote Staking (Planned)
      • Trust & Reviews
      • TRAST Premium (Planned)
      • Verification
      • Feature Specifications
      • Tokenomics Analysis (Planned)
    • PayAI & Marketplace
      • PayAI System (Planned)
      • TRAST Marketplace (Planned)
      • TRAST.fun (Planned)
      • Advanced Platform Features
      • Bridge to XRPL
    • User Interaction
      • Inline Search
      • Live Feed
      • Commenting System
      • Project Marketing
      • Identity Integration (Planned)
      • Smart Onboarding (Planned)
      • Monetization Options (Planned)
    • Chat System
      • Chat Rooms (Planned)
      • Specialized Rooms (Planned)
      • Two-Tier Rooms (Planned)
    • Platform Analytics (Planned)
  • Technical Documentation
    • Technical Overview
    • Core Architecture
      • Design & Implementation
      • Security & Trust
      • Infrastructure Costs
    • AI Systems
      • Bot System
      • AI Chat Guide
      • AI Scam Radar
      • AI Review Copilot & TrustDigest
      • Content Agent (Ongoing)
    • Advanced Features
      • Bot Outputs
      • Customization & Integrations
  • Community
    • Guidelines
      • Communication Style
      • Communication Playbook
      • Community Features
    • Engagement & Rewards
      • Community Incentives (Planned)
      • Premium Features (Gem Hunters, planned)
  • Resources
    • Use Cases
      • Overview
      • Detailed Examples
      • Real World Scenarios
    • Support & Contact
      • Contact Information & Company Details
      • Support Center
  • Token
    • Economics
      • Financial Model (Planned)
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On this page
  • System Overview
  • Core Technology Stack
  • AI Implementation
  • Architecture Components
  • Request Processing
  • Real-time Features
  • Security Infrastructure
  • Authentication and Authorization
  • Data Protection
  • Performance Optimization
  • Frontend Optimization
  • Backend Performance
  • Model Selection and Processing
  • Selection Criteria
  • Monitoring and Maintenance
  • System Health
  • Maintenance Procedures
  • Emergency Response
  • Incident Management
  • Communication
  • Future Development
  1. Technical Documentation
  2. Core Architecture

Design & Implementation

When we are building TRAST, our goal is to create a robust, scalable system that could handle complex data workflows efficiently. This technical architecture document breaks down our core infrastructu

System Overview

TRAST's architecture combines modern web technologies with advanced AI capabilities to create a robust, scalable platform for Web3 intelligence. Our system is built on a foundation of reliable, proven technologies while incorporating cutting-edge AI and blockchain capabilities.

Core Technology Stack

The platform utilizes React 18 with TypeScript for the frontend, providing a responsive and type-safe user interface. This is complemented by FastAPI on the backend, offering high-performance API endpoints with automatic OpenAPI documentation. For real-time capabilities, we implement WebSocket servers that enable live updates and interactive features.

Our primary database is a managed PostgreSQL instance on Supabase, providing scalable, reliable, and secure data storage. Supabase egress (0/250GB per month) and Monthly Active Users (0/100,000 MAU) quotas are monitored for cost and performance optimization. All data is available to the application layer in real time, with no complex sharding or multiple database layers required.

The bot is deployed on Heroku with auto-scaling enabled, ensuring high availability and seamless scaling based on demand. Both the application and database layers are designed for horizontal scaling.

Source code is managed on GitHub. Documentation is maintained on GitBook for transparency and ease of contribution.

AI Implementation

Our AI system is powered by OpenAI (via LLMService), providing advanced language understanding and generation for chat, entity search, and memory features. The system is designed to easily integrate new AI providers as needed. All AI interactions are managed through a unified service layer, ensuring flexibility and future extensibility.

Architecture Components

Request Processing

Every user interaction follows a carefully optimized flow through our system. When a request arrives, it first passes through our validation layer, which ensures data integrity and security. The request is then routed to appropriate handlers based on its type and requirements.

Context management plays a crucial role in our system. Each request is enriched with relevant contextual information, pulled from our caching layer or database as needed. This context helps ensure accurate and relevant responses while maintaining high performance.

Real-time Features

The platform's real-time capabilities are built on a robust WebSocket infrastructure, enabling immediate updates and interactive features. This system handles:

  • Live market data updates

  • Instant user notifications

  • Real-time collaboration features

  • Interactive analysis tools

Our real-time system is designed for scalability, using message queues and load balancing to handle high volumes of concurrent connections while maintaining low latency.

Security Infrastructure

Security is fundamental to our platform's design and implementation. We employ a comprehensive, multi-layer security approach that protects both user data and system integrity.

Authentication and Authorization

Access control begins with robust authentication mechanisms, supporting multiple authentication methods while maintaining strict security standards. Our authorization system implements role-based access control, ensuring users can only access appropriate resources and functionality.

Rate limiting and request validation protect our APIs from abuse while ensuring service availability for legitimate users. All system access is logged and monitored for security purposes.

Data Protection

We implement end-to-end encryption for all sensitive data, both in transit and at rest. Key management follows industry best practices, with regular key rotation and secure storage. Access to data is strictly controlled and audited.

Regular security audits and penetration testing help identify and address potential vulnerabilities before they can be exploited. Our security team maintains constant vigilance over system security and responds quickly to any potential threats.

Performance Optimization

Performance optimization is a continuous focus across all system components. This includes:

Frontend Optimization

  • Efficient component rendering and state management

  • Strategic code splitting and lazy loading

  • Optimized asset delivery and caching

  • Progressive enhancement for different device capabilities

Backend Performance

  • Query optimization and database indexing

  • Caching strategies at multiple levels

  • Efficient resource allocation and scaling

  • Load balancing and traffic management

Model Selection and Processing

Our AI system implements sophisticated model selection based on multiple factors:

Selection Criteria

  • Task complexity and requirements

  • Response time needs

  • Resource availability

  • Cost efficiency

  • Privacy considerations

The system continuously monitors performance metrics and adjusts model selection strategies to optimize both performance and cost efficiency.

Monitoring and Maintenance

Comprehensive monitoring ensures system health and performance:

System Health

  • Real-time performance monitoring

  • Error tracking and alerting

  • Resource usage analysis

  • Security event monitoring

Maintenance Procedures

Our maintenance processes ensure system reliability through:

  • Regular updates and patches

  • Performance optimization

  • Security improvements

  • Feature enhancements

Emergency Response

We maintain comprehensive emergency response procedures for handling any system issues:

Incident Management

  • Immediate threat detection and assessment

  • Rapid response procedures

  • System recovery protocols

  • Post-incident analysis

Communication

  • Clear notification procedures

  • Stakeholder communication plans

  • Status update protocols

  • Resolution confirmation

Future Development

Our platform continues to evolve with planned improvements including:

  • Enhanced AI capabilities

  • Expanded blockchain support

  • Advanced analytics features

  • Improved performance optimization

  • Additional security features

All development follows our core principles of security, scalability, and user value, ensuring that new features enhance rather than complicate the platform.

Remember: This architecture represents our current implementation and continues to evolve based on user needs, technological advances, and security requirements. Regular updates ensure the platform remains current with best practices and emerging technologies.

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Last updated 1 month ago