Enterprise RAG & Private Vector Knowledge Engines
Give your team an AI assistant that answers questions directly from your internal contracts, technical SOPs, and customer wikis — with exact source citations and zero hallucinations.
The 4-Stage Enterprise RAG Pipeline
How we ingest private corporate documentation, index it in PostgreSQL with pgvector, and generate verifiably grounded answers.
PostgreSQL Native Vector Storage
Avoid expensive standalone vector databases like Pinecone. We store vectors natively in PostgreSQL alongside your users, permissions, and audit logs.
- Transactional ACID guarantees (delete account = purge vectors)
- HNSW indexing for sub-20ms similarity lookups
- Row-level security ensures users only retrieve permitted files
- Substantial infrastructure cost reduction
Strict Source Citations
Every answer generated by the assistant includes direct clickable links to the exact paragraph and page number of the original document.
- Strict context prompt guardrails against speculative answers
- Threshold confidence scores (flags when info is missing)
- Audit trail logging for enterprise compliance
- Human review escalation for low-confidence queries
Automated Ingestion Pipelines
As your team edits Google Docs, Notion pages, or Zendesk macros, background sync workers automatically re-chunk and update embeddings in real time.
- Real-time webhook sync from Google Drive & Notion
- Automated PDF layout analysis and table parsing
- Deduplication across versioned documents
- FastAPI microservice backend for high-concurrency requests
Build a Private AI Brain for Your Business
Stop wasting hours re-answering repetitive policy questions. Book an architecture session to plan your private enterprise RAG engine.
Book Architecture Session →