Hosted language models used as a component — for extraction, classification, drafting, and agents allowed to act inside defined limits.
What we reach for first
Work that is currently manual, tolerant of review, and expensive in people’s time. Extraction and classification pay back fastest. The shape that pays back is a task done by hand today, at volume, with a person still checking the output. An agent given real tools needs the same discipline: scoped credentials, a boundary it cannot write outside of, and a record of what it did.
Decisions that must be deterministic, auditable, or right every time without a human in the loop. In regulated workflows that is most of them, and we will say so. A prompt asking a model to be careful is not a control, and we will not present one as though it were.
An evaluation set in the pipeline, prompts versioned like code, cost and latency budgets per call, and a defined behaviour for when the model is wrong. Cost and latency are instrumented per call from the first week, because both scale with adoption and neither is visible until the invoice arrives.
Where this appears on the site
We work in LLM APIs and agents, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Also in ai and machine learning
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.
