AI is only as good as the data feeding it. This is the infrastructure underneath — the part nobody demos and everything depends on.
When this is the right call
What we build
Reliable movement of data between systems, with schema handling and alerting so a broken pipeline surfaces immediately rather than as a wrong number weeks later.
Operational views that reflect what is happening now, built so the people who need them can read them without a analyst translating first.
A warehouse and modelling layer where definitions are agreed once, so "revenue" or "active customer" means the same thing in every report.
Checks on freshness, volume, and distribution that catch the silent problems — the feed that stopped, the field that started arriving null.
Typical stack
Good fit
We take on 4-6 clients at a time and turn down work outside our expertise. If this is not the right fit we will say so on the call rather than after the invoice.
Common questions
Not always. If your data fits comfortably in one operational database and the questions are simple, a warehouse adds cost and moving parts for little gain. We will say so rather than sell one.
Yes. We build on whatever you already run — BigQuery, Redshift, Postgres, or something else — rather than insisting on a migration as step one.
Directly. Retrieval quality, forecasting accuracy, and agent reliability are all downstream of data quality. When an AI project underperforms, the cause is usually here.
Also from us
Agents that take real actions in your systems — not chatbots that summarise a document and stop.
Your domain expertise is trapped in documents, threads, and the heads of three people. This turns it into something the whole team can query.
When the AI system needs a home — or the problem was never an AI problem — we build the software underneath it.