What this case proves

RAG is not an AI feature. It is a data engineering problem disguised as an AI feature. The hard part is not calling an LLM. The hard part is building a pipeline that ingests messy enterprise documents, chunks them intelligently, embeds them consistently, retrieves the right context under latency constraints, and does all of that reliably in production.

Why that matters

Every company that adopts AI assistants will eventually need this pipeline. The difference between a demo that impresses and a product that ships is the engineering underneath: chunking strategy, embedding freshness, retrieval precision, and citation traceability.

Tradeoffs worth calling out

Using pgvector instead of a specialized vector database trades some query performance at extreme scale for operational simplicity. For most enterprise knowledge bases under a few million chunks, PostgreSQL handles both relational metadata and vector search without adding another system to the stack.

Practical takeaway

If your team is evaluating RAG, this case gives you a production-ready blueprint that separates concerns cleanly and avoids vendor lock-in on the vector layer.