01
Scope and fit
We decide where pgvector earns its place in your system, and where a simpler tool wins. No resume-driven architecture.
★ pgvectorTechnologies & Infrastructure8-week engagement
PGVECTOR · VECTOR SEARCH IN POSTGRESVectors where your data already lives.
pgvector adds similarity search to PostgreSQL, so embeddings sit next to your relational data. For most RAG and semantic search, you do not need a separate vector database.
pgvectorPostgreSQLVector store
Cycle
8 weeks · fixed price
Stack
Postgres + pgvector
Output
Production code + eval suite
Handoff
Full source ownership
[THE SHORT VERSION]
One database for relational and semantic search.
A dedicated vector database adds a system to operate, sync, and secure. pgvector keeps embeddings in Postgres, joined to the rows they describe, which simplifies most retrieval workloads. We reach for a specialized store only at large scale.
When it fits
- RAG and semantic search at small to mid scale
- Teams that already run Postgres and want fewer systems
- Use cases that join embeddings to relational data
[HOW WE BUILD IT]
How we build with pgvector.
02
Build on a tested foundation
We integrate pgvector against a foundation we trust: typed code, CI, and observability from the first commit. Boring infrastructure, modern surface.
03
Eval before launch
An eval suite proves the build behaves before it reaches a user. We measure, then ship.
04
Handoff with ownership
Your team gets the code, the tests, and a runbook. No lock-in to us or to a vendor framework.
[WHAT YOU GET]
What the engagement leaves behind.
8 wks
Problem to production
100%
Source ownership at handoff
Eval-first
Tested before it ships
0
Framework lock-in
[RELATED]
Worth a look next.
APPLIED K-FRAMEWORKBring the problem.
Bring the problem.
We’ll bring the build.
Eight weeks, fixed price, eval suite at handoff. Senior engineers, full source ownership, no framework lock-in.