Four pillars. One discipline. Three providers.
We do AI engineering, not "AI strategy." Every engagement ends with software running in production on Claude, OpenAI, or Gemini — evals you can re-run, and a team that owns it.
Discipline over novelty
The boring tool, when it works. Measurement before shipping. Nothing’s "done" until an eval catches the regression you just fixed.
- Eval-first — every project starts by labeling a real evaluation set. No production traffic until offline numbers move.
- Provider chosen per problem — Claude, OpenAI, or Gemini, sometimes routed across all three. The eval set picks the winner, not the brochure.
- Small models where they fit — frontier models cost real money. Many problems don’t need them.
- Citations are required — a customer-facing answer without a citation is, by policy, a bug.
- Handoff is the deliverable — the team that runs it after we leave is the customer, too.
Customized to your stack, not productized
Every system we ship is purpose-built around your data, your latency budget, your compliance posture, and the model provider that fits the job. We don’t resell a SaaS. We don’t hand you the same RAG pipeline we handed the last client. Each engagement starts from your eval set and ends with a system designed for it.
Have a problem in mind?
Tell us in a couple of sentences what you’re trying to ship, the constraints, and what done looks like. We’ll respond with whether we’re a fit.