Five practice areas. Most engagements use three or four of them, because an AI system that only does one of these is not finished.
We design multi-agent systems that do real work: agents with defined responsibilities, genuine tool access, structured handoffs, and a supervising agent that decides who is needed and assembles one coherent answer at the end.
The engineering that matters here is unglamorous — retries, timeouts, partial failure, context budgets, and knowing when to stop. We build for the case where a step fails at three in the morning, not for the demo.
Human review gates go wherever the consequence of being wrong is expensive. The system asks rather than assumes.
A persona is not a system prompt. It is a stable identity: a name, a role, a tone, a set of things the character will and will not do, and a voice you would recognise with your eyes shut.
We build persona systems where the identity is permanent and the underlying model is an implementation detail. You can change the model behind a persona without the person talking to it noticing anything except that it got better.
On the voice side we build real-time bidirectional speech — recognition, synthesis, natural turn-taking, and latency low enough that the conversation does not feel like a walkie-talkie.
Desktop, web and mobile front-ends for AI systems. We work in React and Electron, and we build generative interfaces — where the layout is assembled around what the user is trying to do rather than picked from a fixed menu of screens.
We also do the part most AI products skip: the empty state, the error state, the state where the model is slow, and the state where the model is confidently wrong. Those screens decide whether people trust the product.
Connecting AI to the systems a business already runs — messaging, documents, code, email, calendars, databases, storage and project tracking — through standard tool-server protocols rather than a pile of bespoke glue.
Work runs on a schedule or in response to an event. Every action is logged, so there is always an answer to the question "why did it do that?"
Positioning, naming, the site, the launch. AI products fail on explanation more often than they fail on capability, and a product nobody can describe does not get bought.