Production search. Relevance, facets, speed at scale.

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

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Cycle

8 weeks · fixed price

Stack

Elasticsearch

Output

Production code + eval suite

Handoff

Full source ownership

[THE SHORT VERSION]

Treat search as a real product feature.

Good search comes down to 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.

Scope and fit

We decide where Elasticsearch & Search earns its place in your system, and where a simpler tool wins. No resume-driven architecture.

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.