Building Production RAG Pipelines for Enterprise Search
How Compile Vision designs retrieval-augmented generation systems that stay accurate, secure, and maintainable in production.
Enterprise search fails when chunks are noisy, embeddings are stale, or citations are missing. A durable RAG stack treats ingestion, retrieval, and answer generation as separate services with clear SLAs.
We typically use FastAPI microservices, a vector store such as Pinecone or pgvector, and LangChain/LlamaIndex orchestration - with observability on latency and groundedness scores.
Security matters: redact PII at ingest, scope retrieval by tenant, and never send secrets to third-party model APIs without review.
The result is a document QA agent your support and ops teams can trust - not a demo chatbot that hallucinates policy.
Key takeaways
- Separate ingest, retrieve, and generate services.
- Citations and tenant filters are non-negotiable.
- Monitor groundedness, not only latency.
Practical flow
- 1
Ingest
Parse, clean, chunk, and tag documents
- 2
Embed + index
Store vectors with ACL metadata
- 3
Retrieve
Hybrid search and rerank top passages
- 4
Generate
Answer only from retrieved context
- 5
Observe
Track quality, freshness, and cost
Need this built?
We turn these playbooks into production systems for your team.
Talk to Compile Vision