PostgreSQL engineering
Schema design, query tuning and the extensions that let one database cover far more ground than expected.
PostgreSQL handles far more than most teams assume — full-text search, geospatial through PostGIS, time-series through TimescaleDB, vector similarity through pgvector, and queues through SKIP LOCKED. A great many architectures add a second datastore they did not need.
We design schemas that survive scale, tune the queries that actually hurt, and reach for a specialised datastore only when Postgres genuinely runs out — which happens later than the industry implies.
PostgreSQL, specifically.
Our queries have gotten slow. Where do you start?
Query statistics to find the real cost centres, then execution plans on those specifically. The cause is usually a missing index, an N+1 pattern in the application, or a query that stopped using an index after the data distribution changed.
Can Postgres handle our time-series or vector workload?
Very often yes, with TimescaleDB or pgvector. One database with two extensions is dramatically less operational burden than two systems, and we will tell you the point at which that stops being true.
Tell us what you are building.
A short conversation is usually enough to tell whether we are the right firm for the problem. If we are not, we will say so and point you somewhere better.