Multi-Tenant AI SaaS Platform: Document Intelligence

The Challenge

Businesses increasingly need AI-powered document intelligence but building a custom RAG system from scratch is expensive and time-consuming. The goal was to create a platform that lets any business deploy AI-powered document search without writing a single line of code, while keeping each tenant's data completely isolated and access-controlled.

The Solution

• Multi-Tenant Architecture: PostgreSQL with Row-Level Security (RLS) ensures complete data isolation between tenants. Each organization's documents, embeddings, and query history are fully siloed at the database level, not just the application level.
• Role-Based Access Control (RBAC): Granular permission system allowing tenant admins to control who can upload documents, run queries, manage knowledgebases, and access specific document collections within their organization.
• Hybrid Retrieval System: Combines vector similarity search (via Qdrant) with keyword-based retrieval for higher accuracy. This hybrid approach catches results that pure semantic search misses, especially for domain-specific terminology and exact phrases.
• PydanticAI Integration: Built the AI orchestration layer using PydanticAI, providing type-safe, structured interactions between backend services and LLM providers (Claude Sonnet as primary model, OpenAI for embeddings).

Key Results

• Multi-tenant architecture with full data isolation between organizations.
• RBAC with granular permission management at the document and knowledgebase level.
• Hybrid retrieval (vector + keyword) delivering more accurate results than either method alone.
• Clean API surface (32 endpoints) enabling frontend integration and third-party access.
• Designed and documented for enterprise-scale deployment.