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System Architecture

AuthentiVoice follows a modern microservices architecture with clear separation of concerns between frontend, backend, and database layers.

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Component Overview

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Frontend (React/TypeScript)

The frontend is a single-page application built with:
  • React 18 with TypeScript
  • Vite for fast development and building
  • shadcn-ui for consistent UI components
  • TanStack Query for data fetching and caching
  • Supabase JS Client for authentication and real-time updates

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Backend (Python/FastAPI)

The backend API service handles:
  • Audio file processing (trimming, format conversion)
  • AI-powered analysis using Google Gemini
  • Multiple transcription engines (Whisper, Parakeet, NeMo)
  • Integration management (S3, OneDrive, webhooks)
  • JWT authentication validation

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Database (Supabase/PostgreSQL)

The database layer provides:
  • User authentication with Supabase Auth
  • Row Level Security (RLS) for data isolation
  • Real-time subscriptions for live updates
  • Database functions for complex operations

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Data Flow

1

Audio Upload

User uploads audio file through frontend → Backend stores in S3/MinIO → Metadata saved to database
2

Processing

Backend retrieves audio → Applies trimming/processing → Generates transcription → Performs AI analysis
3

Analysis

AI model analyzes transcript → Fraud score calculated → Results stored in database → Frontend updates via polling/subscription
4

Review

Reviewer accesses analysis → Makes decision → Metrics tracked → Audit log updated

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Security Architecture

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Authentication Flow

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Security Layers

  1. Frontend Security
    • HTTPS enforcement
    • Content Security Policy
    • Secure token storage
  2. API Security
    • JWT validation
    • API key authentication
    • Rate limiting
    • CORS configuration
  3. Database Security
    • Row Level Security policies
    • Role-based access control
    • Encrypted connections

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Deployment Architecture

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Kubernetes Deployment

┌─────────────────┐     ┌─────────────────┐
│    Ingress      │     │  Load Balancer  │
│   (HTTPS/TLS)   │────▶│                 │
└────────┬────────┘     └────────┬────────┘
         │                       │
    ┌────▼────┐             ┌───▼────┐
    │Frontend │             │Backend │
    │  Pods   │             │  Pods  │
    │ (3x)    │             │ (3x)   │
    └─────────┘             └────────┘
         │                       │
         └───────────┬───────────┘
                     │
              ┌──────▼──────┐
              │  Supabase   │
              │ PostgreSQL  │
              └─────────────┘

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Scaling Considerations

  • Frontend: Stateless, horizontally scalable
  • Backend: Stateless API, auto-scaling based on CPU/memory
  • Database: Managed by Supabase, automatic backups
  • Storage: S3-compatible object storage for audio files

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Technology Stack

Frontend Technologies

  • React 18
  • TypeScript
  • Vite
  • TailwindCSS
  • shadcn-ui
  • TanStack Query
  • React Router

Backend Technologies

  • Python 3.10+
  • FastAPI
  • Pydantic
  • pydub/ffmpeg
  • Google Gemini API
  • Boto3 (S3)
  • httpx

Infrastructure

  • Docker
  • Kubernetes
  • Nginx
  • MinIO/S3
  • Supabase
  • PostgreSQL

AI/ML Services

  • Google Gemini
  • OpenAI Whisper
  • NVIDIA NeMo
  • Parakeet ASR

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Performance Optimizations

  1. Caching Strategy
    • React Query for frontend caching
    • Redis for backend caching (optional)
    • CDN for static assets
  2. Async Processing
    • Background jobs for audio processing
    • Webhook notifications for long operations
    • Real-time updates via Supabase subscriptions
  3. Database Optimization
    • Indexed columns for common queries
    • Materialized views for analytics
    • Connection pooling

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Monitoring and Observability

  • Application Monitoring: Error tracking and performance metrics
  • Infrastructure Monitoring: Kubernetes metrics and logs
  • Database Monitoring: Query performance and connection pools
  • User Analytics: Usage patterns and feature adoption