AI Engineering
LLM and agent features built with the same rigour as the rest of the stack, typed, observable, evaluated, and safe to put in front of real users.
- LLM and agent features wired into real products (retrieval, tool-use, structured output)
- Evaluations, guardrails, and observability so you can trust what ships
- AI on top of your own data, with privacy and access handled properly
- Pragmatic model choice, the smallest thing that works over the most expensive
What this is
Every AI demo works. That is what a demo is for. The real question is what happens on the second Tuesday, in front of a real user, with data the model has never seen. We build the AI features that are still standing on that Tuesday.
The model call is the easy part. The work is everything around it: grounding answers in your data, catching the moment the model is confidently wrong, keeping latency and cost honest, and measuring the right thing so you actually know whether it works, instead of hoping it does.
How we work
We start from the outcome, not the model. What decision is this meant to help with, and how will we know it is any good? Then we reach for the smallest thing that gets there. Plain retrieval with a well-chosen model beats a clever agent nobody can debug, most days of the week.
Everything is built to be trusted, not just launched: evaluations that catch regressions before your users do, guardrails on what goes in and what comes out, structured responses the rest of the app can rely on, and logging that turns a bad answer into something you can see and fix rather than a ghost story. Your data stays yours, held the way regulated work taught us to hold it.
We use the models hard in our own work too, so the build goes faster, with an experienced eye on what to trust and what to check by hand.
Good fit if
- You want AI in your product that is reliable enough to ship, not a prototype that dazzles once and quietly falls over.
- You have data or a workflow an LLM could genuinely help with, and want someone who knows where it helps and where it only adds risk.
- You need a team that can build the whole feature: the model, the data, the API, and the product wrapped around it.
Sound like the shape of your problem?