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Technical Architecture

Deep dive into Vector AI's technical infrastructure, analysis engine, and data processing systems.

Backend Infrastructure

Python-Based Core

Framework Architecture:

  • FastAPI - High-performance API framework
  • AsyncIO - Asynchronous processing for scalability
  • Celery - Distributed task queue for heavy computations
  • Redis - In-memory caching and session management
  • PostgreSQL - Primary database for structured data
  • MongoDB - Document storage for unstructured data

Microservices Design:

  • Analysis Engine - Core token analysis logic
  • Data Ingestion Service - Multi-source data collection
  • API Gateway - Request routing and rate limiting
  • Notification Service - Alert and webhook management
  • Authentication Service - User and API key management

Modular Feature System

Component Architecture:

  • Security Module - Contract analysis and vulnerability detection
  • Market Module - Price, volume, and liquidity analysis
  • Social Module - Community and sentiment analysis
  • Risk Module - Comprehensive risk assessment
  • Scoring Module - VectorScore calculation engine

Plugin System:

  • Extensible architecture for new analysis types
  • Hot-swappable modules for updates
  • A/B testing framework for improvements
  • Custom analysis pipelines

Data Processing Pipeline

Stage 1: Data Collection

Contract Address Input
    ↓
Multi-Source Data Gathering
    ↓
Blockchain Data (Etherscan, Alchemy)
Social Data (Twitter, Telegram)
Market Data (DexScreener, DEXs)
Security Data (GoPlus, EVA AI)

Stage 2: Data Validation

Raw Data Input
    ↓
Format Validation
    ↓
Cross-Reference Verification
    ↓
Quality Scoring
    ↓
Anomaly Detection

Stage 3: Analysis Processing

Validated Data
    ↓
Parallel Analysis Modules
    ↓
Security | Market | Social | Risk
    ↓
Results Aggregation

Stage 4: Intelligence Generation

Module Results
    ↓
VectorScore Calculation
    ↓
Risk Assessment
    ↓
Report Generation
    ↓
User Delivery

Performance Optimizations

Smart Caching System

Multi-Layer Caching:

  • L1 Cache (Redis) - Frequently accessed data (30 second TTL)
  • L2 Cache (Memory) - Analysis results (5 minute TTL)
  • L3 Cache (Database) - Historical data (24 hour TTL)

Cache Strategies:

  • Write-Through - Immediate cache updates
  • Write-Behind - Asynchronous cache updates
  • Cache-Aside - On-demand cache population
  • TTL Management - Automatic expiration handling

Benefits:

  • 90% reduction in API response times
  • 75% decrease in external API calls
  • Improved system reliability
  • Better cost efficiency

Efficient Data Fetching

Parallel Processing:

  • Concurrent API calls to multiple data sources
  • Asynchronous data processing pipelines
  • Load balancing across service instances
  • Intelligent retry mechanisms

Data Source Optimization:

  • Primary/backup source configuration
  • Response time monitoring
  • Automatic failover systems
  • Rate limit management

Response Time Optimization

Target Performance:

  • Token Analysis - < 5 seconds
  • Quick Scans - < 2 seconds
  • API Responses - < 1 second
  • Webhook Delivery - < 500ms

Optimization Techniques:

  • Database query optimization
  • Index management
  • Connection pooling
  • Result streaming

Data Architecture

Multi-Source Integration

Blockchain Data Sources:

  • Etherscan Family - Ethereum, BSC, Polygon transaction data
  • Alchemy RPC - Reliable blockchain access
  • Moralis - Multi-chain API services
  • QuickNode - High-performance RPC endpoints

Security Data Providers:

  • GoPlus - Comprehensive security analysis
  • Token Sniffer - Scam and honeypot detection
  • EVA AI - Advanced contract analysis
  • Certik - Professional security audits

Market Data Sources:

  • DexScreener - DEX trading data and analytics
  • CoinGecko - Price and market cap data
  • DeFiPulse - DeFi protocol metrics
  • 1inch - DEX aggregation data

Social Data Sources:

  • Twitter API - Social media sentiment
  • Telegram API - Community analysis
  • Discord API - Community engagement
  • Reddit API - Discussion sentiment

Data Flow Architecture

Real-Time Processing:

External APIs → Data Ingestion → Validation → Processing → Storage → Delivery
     ↓              ↓             ↓            ↓          ↓         ↓
Rate Limiting → Format Check → Quality Score → Analysis → Cache → Response

Batch Processing:

Scheduled Jobs → Bulk Data Collection → Historical Analysis → Trend Detection → Report Generation

Stream Processing:

WebSocket Feeds → Real-Time Events → Immediate Processing → Alert Generation → User Notification

Error Resilience

Fault Tolerance

Circuit Breaker Pattern:

  • Automatic failure detection
  • Service degradation handling
  • Recovery monitoring
  • Fallback mechanisms

Retry Strategies:

  • Exponential backoff
  • Jitter introduction
  • Maximum retry limits
  • Dead letter queues

Graceful Degradation:

  • Partial analysis results
  • Reduced feature sets
  • Cached data fallbacks
  • User notification systems

Data Quality Assurance

Validation Layers:

  • Input Validation - Format and range checking
  • Business Logic Validation - Domain-specific rules
  • Cross-Reference Validation - Multi-source verification
  • Output Validation - Result consistency checking

Quality Metrics:

  • Data completeness scores
  • Source reliability ratings
  • Freshness indicators
  • Confidence levels

Analysis Engine

Feature Weight System

Primary Factors (High Weight):

  • Contract security vulnerabilities (40%)
  • Liquidity analysis (25%)
  • Holder distribution (20%)
  • Team credibility (15%)

Secondary Factors (Medium Weight):

  • Social media presence (30%)
  • Market metrics (25%)
  • Community engagement (25%)
  • Development activity (20%)

Tertiary Factors (Low Weight):

  • Website quality (35%)
  • Documentation completeness (30%)
  • Partnership claims (20%)
  • Marketing presence (15%)

Scoring Algorithm

VectorScore Calculation:

def calculate_vector_score(analysis_results):
    base_score = 100
    
    # Apply security deductions
    security_score = analyze_security(contract_data)
    base_score -= security_penalties(security_score)
    
    # Apply market adjustments
    market_score = analyze_market(market_data)
    base_score = adjust_for_market(base_score, market_score)
    
    # Apply social bonuses/penalties
    social_score = analyze_social(social_data)
    base_score = adjust_for_social(base_score, social_score)
    
    # Normalize to grade scale
    return normalize_to_grade(base_score)

Grade Assignment:

  • Mathematical mapping to A-F scale
  • Confidence interval calculation
  • Risk level categorization
  • Recommendation generation

Pattern Detection Algorithms

Anomaly Detection:

  • Statistical outlier identification
  • Behavioral pattern analysis
  • Time series anomaly detection
  • Multi-dimensional clustering

Risk Pattern Recognition:

  • Honeypot indicators
  • Rug pull warning signs
  • Wash trading detection
  • Bot activity identification

Success Pattern Identification:

  • Legitimate project characteristics
  • Sustainable tokenomics patterns
  • Strong community indicators
  • Reliable team markers

Visual Generation System

Scoring Card Engine

Dynamic Card Generation:

  • Real-time data visualization
  • Responsive design adaptation
  • Brand consistency maintenance
  • Performance optimization

Card Components:

  • Header with token information
  • VectorScore display
  • Risk level indicators
  • Key metrics summary
  • QR codes for sharing

Image Processing Pipeline

Generation Process:

  1. Data Preparation - Format analysis results
  2. Template Selection - Choose appropriate design
  3. Content Rendering - Generate visual elements
  4. Quality Assurance - Validate output
  5. Optimization - Compress for delivery
  6. Caching - Store for future use

Optimization Techniques:

  • SVG-based graphics for scalability
  • WebP format for web delivery
  • PNG fallbacks for compatibility
  • Lazy loading for performance

Reliability & Monitoring

Error Handling Strategy

Error Classification:

  • Transient Errors - Temporary network issues
  • Permanent Errors - Invalid input data
  • System Errors - Internal service failures
  • External Errors - Third-party service issues

Handling Mechanisms:

  • Automatic retry with backoff
  • Circuit breaker activation
  • Fallback data sources
  • User error notification

Data Quality Assurance

Quality Metrics:

  • Completeness - Percentage of required data obtained
  • Accuracy - Validation against known sources
  • Freshness - Age of data used in analysis
  • Consistency - Agreement between sources

Monitoring Systems:

  • Real-time quality dashboards
  • Automated alert systems
  • Trend analysis reports
  • Performance benchmarking

Scalability Design

Horizontal Scaling:

  • Load balancer distribution
  • Auto-scaling groups
  • Database sharding
  • Microservice replication

Vertical Scaling:

  • Resource monitoring
  • Automatic scaling triggers
  • Performance optimization
  • Capacity planning

Security & Privacy

API Security

Authentication & Authorization:

  • JWT token-based authentication
  • Role-based access control
  • API key management
  • Rate limiting per user/key

Data Protection:

  • Encryption at rest and in transit
  • Secure API endpoints (HTTPS)
  • Input sanitization
  • SQL injection prevention

User Privacy

Data Handling:

  • Minimal data collection
  • Anonymized analytics
  • GDPR compliance
  • User consent management

Privacy Features:

  • No personal data storage
  • Anonymous usage tracking
  • Opt-out mechanisms
  • Data deletion requests

Monitoring & Analytics

System Monitoring

Key Metrics:

  • Response times
  • Error rates
  • Throughput
  • Resource utilization

Alerting Systems:

  • Real-time monitoring
  • Threshold-based alerts
  • Escalation procedures
  • Incident response

Performance Analytics

User Metrics:

  • API usage patterns
  • Feature adoption rates
  • User retention
  • Satisfaction scores

System Metrics:

  • Database performance
  • Cache hit rates
  • External API latency
  • Error distribution