ABOUT PRIMASTAT
Anyone can spin up a database now. Trusting what comes out of it is the hard part.
Primastat spent its first years building the unglamorous layer of data: pipelines that don't drop rows, databases that answer in under a second, dashboards a whole company can rely on. Founder-led, open-source, everything handed over. That part hasn't changed.
What changed is the world around the work. AI can now provision systems, scaffold structure, and produce fluent output in seconds. Creation stopped being scarce. But when companies pointed AI agents at their real data and asked real questions, the answers came back confidently and plausibly, and, on published evaluations, wrong more often than right. Not a model problem. A meaning problem: your systems don't say what things mean, teams don't agree on definitions, and nothing governs what the agent can see.
That's the problem Primastat narrowed to in 2026. We measure how accurately your AI agents answer, and we build the foundation that makes the answers trustworthy, on open-source tools you keep.
What we believe
Measure before you fix.
No scope until there's a score. A small, fixed measurement beats a big guess.
Boring modeling wins.
Accuracy doesn't come from smarter prompts. It comes from settled definitions, documented grain, and tested models.
Build to own.
Your metric definitions are your business logic. Renting them from a vendor is how lock-in starts. Everything we build lives in your repo.
Prove it with a re-run.
We re-run the same benchmark after the fix, so the improvement is a number you can see, not a promise you have to trust.
Find out what your AI actually scores.
One week. Thirty questions. A number instead of a hope.