AI Engineering
LLM and agent features built with the same rigor 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
Most AI demos fall over the moment they meet real users, real data, and real edge cases. We build AI features that don't. LLM and agent functionality goes into the product with the same discipline as any other system we ship: typed end to end, observable, evaluated, and safe to put in front of customers.
The hard part of AI work is rarely the model call. It's everything around it: grounding answers in your data, handling the cases where the model is wrong, keeping latency and cost sane, and measuring the right things so you can tell whether it actually works.
How we work
We start from the outcome, not the model. What decision or task is this meant to help with, and how will we know it's any good? Then we reach for the smallest design that gets there. Often plain retrieval with a well-chosen model beats an elaborate agent that nobody can debug.
Everything is built to be trusted in production: evaluations that catch regressions before users do, guardrails on inputs and outputs, structured responses the rest of the app can rely on, and logging so a bad answer is something you can see and fix rather than a mystery. Your data stays yours, with access and privacy handled the way regulated work has taught us to.
We also use AI heavily in delivery, so the work ships 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's reliable enough to ship, not a prototype that impresses once and quietly breaks.
- You have data or workflows an LLM could genuinely help with, and want the judgement to know where AI helps and where it doesn't.
- You need a team that can build the whole feature: the model, the data, the API, and the product around it.