01
HNSW & IVFFlat index tuning
Tuning m, ef_construction and ef_search for the recall and latency your product needs.
Technologies — Databases & storage
PostgreSQL pgvector architecture & optimization
We implement vector similarity search inside your existing PostgreSQL database, with no separate vector store to keep in sync.
Core capabilities
Why we build with pgvector
02
Relational and vector queries together
Single ACID transactions joining user permissions, relational tables and vector similarity in one SQL query.
03
No sync pipeline
Embeddings live in the same rows as your data and are written in the same transaction, with no separate sync workers or ETL.
Use cases
Where pgvector fits
Multi-tenant SaaS AI search
Filtering vector search results strictly by tenant_id with database Row Level Security.
CRM note search
Semantic search across sales interaction logs joined with CRM pipeline status.
How we staff it
pgvector engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
Yes. With an HNSW index and enough RAM to keep the index in memory, multi-million-row tables return nearest-neighbour results quickly. We benchmark recall and latency on your data before committing.
When your vectors number in the low tens of millions or fewer, and you benefit from joining them with relational data and RLS policies. Beyond that, or at very high query rates, a dedicated vector database is worth evaluating.
Ecosystem
Related technologies
Planning a pgvector project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.