01
Scope and fit
We decide where Elasticsearch & Search earns its place in your system, and where a simpler tool wins. No resume-driven architecture.
★ Elasticsearch & SearchTechnologies & Infrastructure8-week engagement
SEARCH · FULL-TEXT + RELEVANCESearch people actually find things with.
Relevance, facets, typo tolerance, and speed. We build search on Elasticsearch when you need it, and on Postgres full-text when you do not.
ElasticsearchPostgreSQLVector store
Cycle
8 weeks · fixed price
Stack
Elasticsearch
Output
Production code + eval suite
Handoff
Full source ownership
[THE SHORT VERSION]
Search is a product feature, not a checkbox.
Good search is about relevance, not just matching strings: ranking, synonyms, facets, typo tolerance, and speed at scale. Elasticsearch (or OpenSearch) is the heavy hitter. For many products, Postgres full-text or pgvector is enough and far simpler, so we right-size the engine.
When it fits
- Large catalogs needing relevance, facets, and speed
- Search-heavy products where quality drives engagement
- Hybrid keyword plus semantic search
When it does not
- Small datasets where Postgres full-text suffices
[HOW WE BUILD IT]
How we build with Elasticsearch & Search.
02
Build on a tested foundation
We integrate Elasticsearch & Search 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.