pgvector

Specialised vector databases are powerful, but they add a whole system to run, secure, back up, and keep in sync with your source data. For the large majority of products, pgvector in the Postgres you already operate is simpler and more than fast enough — and it keeps your embeddings right next to the records they describe.

That co-location is a genuine advantage: you can combine vector similarity with ordinary SQL filters in a single query — 'find the most relevant documents this user is allowed to see, from the last 30 days' — without shuttling data between systems. Doing that across two separate databases is exactly the kind of complexity pgvector removes.

We build the full retrieval pipeline: chunking your documents sensibly, generating embeddings, storing them in Postgres with pgvector, and indexing them for fast approximate nearest-neighbour search. At query time, we embed the user's question, retrieve the most relevant chunks, and feed them to the language model as grounded context.

The result is AI that answers from your actual content rather than the model's memory — accurate, up to date, and able to cite sources. Because retrieval lives in Postgres, we can layer in permissions, freshness filters, and metadata directly, which is what makes RAG safe and precise in real products rather than a generic demo.

pgvector scales comfortably to millions of vectors with proper indexing, which covers the vast majority of applications. If you reach the scale where a purpose-built vector database genuinely wins — hundreds of millions of vectors, extreme query volumes — we'll recognise it and migrate the retrieval layer, keeping the rest of the architecture intact.

That staged approach means you don't pay for complexity you don't need. Start simple with pgvector on the database you already run; add specialised infrastructure only if and when your scale actually demands it. Most products never need to.