You want AI in the product. You want it to work in production.
Most AI work dies in the gap between a demo that impresses and a feature that holds up under real users, real data, and real cost. That gap is where we start.
What an AI build covers.
LLM integrations and orchestration. Retrieval-augmented generation over your own data. Agentic workflows that do real work, not party tricks. Evals so you know whether it's getting better or worse. And the infrastructure underneath: caching, rate limits, fallbacks, cost controls. We work with OpenAI, Anthropic, and open-weight models, and we pick per the problem, not per the hype cycle.
Why it runs better in one team.
AI features are product decisions wearing a technical costume. What the model should do is a strategy question. What it feels like to use is a design question. Whether it's affordable at scale is an engineering question. When those three people are the same small team, the feature ships coherent. When they're three vendors, you get a demo.
What you walk away with.
A working AI feature in production, with evals that tell you when it regresses, infrastructure that controls cost, and a clear account of what the model can and can't be trusted to do.
A feature that works on the ten thousandth request, not just the first demo.
What we won't do.
We won't ship AI you can't evaluate.
If we can't measure whether it's working, neither can you.
We won't bolt a chatbot onto a product that doesn't need one.
The question is what the product needs, not what's trending.
We won't hide the cost.
Token economics are part of the design, surfaced from day one.
Have an AI feature you need to take to production?
Start a project →We respond within two business days. The first conversation is honest, about whether the feature is worth building, and whether we're the right team to build it.