Putting language models into real products where they have to work every time, not just in the demo.
Agents, tooling, and the boring plumbing around them. The gap between an impressive AI demo and a product people rely on every day is almost entirely engineering discipline — latency budgets, evaluation, and knowing when a model shouldn't be trusted with the answer.
Language models wired into real product surfaces, not just a chat widget bolted onto the side.
Multi-step agents that branch, loop and call real tools, for work that has actual state.
Retrieval grounded in your own data, at a scale where rolling it yourself stops being worth it.
Running and tuning open models when sending data to a third-party API isn't an option.
Every AI feature gets a time budget before it gets built — the same discipline game engines force on every frame.
Open models and fine-tuning when sending data to a third-party API isn't an option.
Model output gets measured against real cases before it ships, not just eyeballed in a demo.
We start with the problem, not the tech. A short scoping conversation tells us whether this is a fit before either of us commits real time.
A small senior team, not a rotating cast. You talk to the people actually writing the code.
Load testing, security review and edge cases — the unglamorous work that decides whether it holds up in production.
We stay involved after launch. Software that works on day one and still works on day two hundred.
Tell us what you're trying to build. We'll tell you honestly whether it's a fit.
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