AI / ML

We treat ML the way we treat any other backend system — with tests, evals, and a clear path to production.

Most useful ML in production isn't novel — it's competent. We'll build the unglamorous pipeline that beats the demo every time.

We set up offline eval, online eval, and a way to track regressions before tuning models. No guessing whether v2 is actually better.

Are you building 'AI agents'?

When the use case justifies it. Most of the time, a well-prompted single call beats an unreliable agent — we'll tell you which is which.

Do we need a GPU cluster?

Almost certainly not. Hosted inference + smart caching is the right answer for nearly every startup we work with.

Can you train custom models?

Yes, but only when prompting and retrieval don't get there. Most engagements never need it.

Have a hard problem?

Tell us what's broken or what you're trying to ship. We'll tell you whether we can help and what the engagement looks like.