Put AI to work on a specific business task.

Retrieval with citations, a narrow scope, and an evaluation set that runs on every change. Confident nonsense is a design failure, not a model quirk.

Tasks you can evaluate: finding information, classifying documents, extracting what people currently retype, drafting inside an existing workflow. We score the candidates on the value they create and on what a failure would cost, and start with the narrowest version that proves the value.

With an evaluation set built from representative examples of the task, run on every change. Quality is measured rather than felt, so you can see what improved, what regressed and whether the scope can widen.

Wherever the decision carries risk. Human review is designed in, not added when something goes wrong, and sensitive actions stay behind a person. The system prepares and proposes; someone accountable decides.

Yes. Retrieval over your documents with citations is the usual foundation, so an answer points to the source it came from. Search over a catalogue or an archive that answers with sources is one of the most common starting points.

Claude, OpenAI and open-weight models, chosen after the compliance constraint rather than before it. Where your data may go is decided first; the model that fits follows from that decision.

No, and usually it should not be. Our engineering team connects the capability to the tools people already use — the application, the CRM, the document flow — so the result arrives where the work happens.

The task, the information available and what a useful result would let your team do. A set of real examples, with sensitive data removed, is worth more than any description of the task.