Production vector databases, shipped in 14 days.

pgvector first, dedicated store when earned

If your data already lives in Postgres and your scale is moderate, pgvector usually does the job without a second system to operate. We reach for a dedicated vector database when the scale, latency, or filtering genuinely demands it, and we will show you the threshold rather than assume it.

Retrieval quality is the real work

The database is the easy part. The hard part is chunking, the embedding choice, the relevance threshold, and the refusal behavior when nothing relevant comes back. We build that with an eval set and tune retrieval against real queries, because a fast vector store returning the wrong passage is just a fast way to be wrong.

What a vector database sprint typically covers

• A RAG retrieval layer over your documents, with citations.
• A pgvector implementation for moderate-scale semantic search.
• A migration to a dedicated vector store when scale demands it.
• A retrieval-quality pass: chunking, thresholds, refusal behavior.