We build AI into products that already have a job to do. Not a chatbot bolted onto a landing page, but features that use a model where a model is genuinely the right tool: understanding messy input, generating a first draft, summarising what a person would otherwise read line by line, or deciding what to surface next.
What we build
- Model integrations — ChatGPT, Claude and other providers wired into your product through their APIs, with prompts, tools and fallbacks that hold up under real traffic.
- Retrieval over your own content — answers grounded in your documents, catalogue or database instead of whatever the model happens to remember.
- Agents and automations — multi-step flows that call your existing systems, with the checkpoints and logging you need to trust the output.
- Evaluation and cost control — the part most projects skip: measuring whether the feature is actually right, and keeping the token bill predictable.
How it works
We start with the narrowest version of the feature that would still be useful, ship it behind a flag, and look at real outputs before widening the scope. AI features fail in ways ordinary code does not, so we treat the first release as something to measure rather than something to announce.
Good fit if
You have a product and a concrete task inside it that a model could do well, and you would rather find out in a few weeks than commit a year to it. If you are still deciding what to build at all, start with an MVP — the AI part fits better once the product has a shape.
