AI is only useful at work when it is grounded in your own information, honest about what it does not know, and accountable to a person. We build that kind of AI — and we measure whether it actually saved anyone time.
The pilots are easy. The hard part is an AI feature that holds up on a Monday morning: right answer, cited source, sensible behaviour when the question is unusual, and a cost per answer the finance team can live with.
We start from a decision or a piece of work you want to improve, not from a model. Then we engineer the unglamorous parts — retrieval, evaluation, guardrails, monitoring — that turn a demo into something people rely on.
It was impressive in the meeting and unusable against real data and real edge cases.
Nobody can tell where an answer came from, so nobody trusts it enough to act.
The information the model needs lives in PDFs, tickets, spreadsheets and people’s heads.
Nothing is evaluated, nothing is monitored, and no one can say whether it is getting better or worse.
Take one of these on its own, or let us own the whole practice area end to end.
A generative AI feature is only useful at work if it answers from your content, shows where the answer came from, and behaves sensibly when it does not know.
ExploreAn agent is a loop, not a chat window: it looks at a case, plans a step, uses a real tool, then checks whether it worked.
ExplorePrediction is only worth paying for if it changes a decision.
ExploreAI and analytics inherit whatever your data is.
ExploreA model in production is a product with a lifecycle: retrained, monitored, approved and occasionally rolled back.
ExploreNone of them are clever. All of them are the difference between software that lands and software that limps.
Book a discovery callOne workflow, a named owner, and a baseline: how long it takes and how often it goes wrong today.
Retrieval over your own documents and systems, with permissions respected per user.
A test set of real questions and expected answers, scored on every change.
The AI drafts, proposes or flags. A human approves anything that matters — until the evidence says otherwise.
We choose boring, well-supported technology on purpose — it is easier to hire for and cheaper to live with.
These rarely appear on a wish list, and every successful programme needs them.
If yours is not here, ask it — we would rather answer honestly than sell you something that will not work.
Ask us directlyTell us the outcome you need. We will tell you honestly what it takes to get there — and what we would not bother doing.