Data Architecture

Overview

Frontier AI uses a modern data architecture built on Neon PostgreSQL as the primary database, with specialized Cloudflare services for real-time processing, vector search, and object storage.

Database Schema

The system uses a normalized PostgreSQL schema with clear relationships between users, calls, and analysis data:

Data Layer Architecture

Primary Database (Neon PostgreSQL)

Purpose: Persistent storage for all application data

• User accounts and authentication data
• Call records and metadata
• Transcript storage and indexing
• Analysis results and summaries
• User settings and preferences

Key Features:

• Serverless Scaling: Automatic scaling based on demand
• Branching: Database branches for development/staging
• Connection Pooling: Efficient connection management
• Backup & Recovery: Automated daily backups

Vector Database (Cloudflare Vectorize)

Purpose: AI embeddings and semantic search

• Knowledge base document embeddings
• Transcript semantic search
• RAG (Retrieval Augmented Generation) queries
• Similar call detection

Integration Flow:

Object Storage (Cloudflare R2)

Purpose: Large file storage and static assets

• Call recordings (when available)
• Knowledge base documents
• User-uploaded files
• System assets and backups

Cache Layer (Cloudflare KV)

Purpose: Fast key-value storage

• Session data and temporary state
• API response caching
• Bot mapping for Recall.ai integration
• User preference caching

Ephemeral Storage (Durable Objects)

Purpose: Real-time state management

• Active WebSocket connections
• Transcript buffering during calls
• AI processing state
• Temporary coaching data

Data Flow Patterns

Real-Time Data Flow

Post-Call Analysis Flow

Data Consistency & Integrity

ACID Compliance

Neon PostgreSQL provides full ACID compliance for critical data:

• Atomicity: All transactions complete or rollback entirely
• Consistency: Data integrity constraints enforced
• Isolation: Concurrent operations don't interfere
• Durability: Committed data survives system failures

Eventual Consistency

Distributed Systems use eventual consistency:

• Durable Objects: State eventually propagates to PostgreSQL
• Vectorize: Embeddings updated asynchronously
• KV Store: Cache invalidation handles consistency
• R2 Storage: Object consistency across edge locations

Data Validation

Performance Optimization

Read Optimization

• Database Indexing: Strategic indexes on frequently queried columns
• Connection Pooling: Efficient database connection management
• Query Optimization: Optimized SQL queries with proper joins
• Caching Strategy: KV cache for frequently accessed data

Write Optimization

• Batch Processing: Bulk operations for transcript data
• Async Processing: Background jobs for heavy operations
• Write-Behind Caching: Immediate response with delayed persistence
• Partitioning: Table partitioning for time-series data

Storage Optimization

Data Security & Compliance

Encryption

• At Rest: All data encrypted in Neon PostgreSQL
• In Transit: TLS 1.3 for all API communications
• Application Level: Sensitive data encrypted before storage
• Key Management: Doppler for secure secret management

Access Control

• Role-Based Access: Granular permissions per user role
• API Authentication: Clerk-based authentication for all endpoints
• Database Security: Row-level security for multi-tenant data
• Audit Logging: Comprehensive access logging for compliance

This data architecture provides the foundation for reliable, scalable, and secure data processing for real-time AI applications.