Consistently in a narrow set of places: extracting structured data from documents and email, triaging and drafting responses to routine support requests, search and retrieval across internal knowledge, and summarising long records for people who need the gist. It pays off far less often in anything where a wrong answer is expensive and nobody checks the output.

We use enterprise API tiers where your data is contractually excluded from training, or we run open-weight models in your own infrastructure when the data cannot leave your environment at all. Which route applies is decided before any data moves, and it is written into the engagement.

A scoped pilot on one workflow, typically four to eight weeks, with agreed success metrics defined at the start. If it hits them, we extend. If it does not, you have spent a bounded amount to learn something concrete rather than committing a year to a platform.

For the large majority of business problems, integrating an existing model with your own data through retrieval is faster, cheaper and more accurate than training a model from scratch. We recommend custom training only where you have a genuinely specific task with quality labelled data, and we will tell you when that is not you.

By constraining the problem. We ground responses in your own documents rather than model memory, cite sources so answers are checkable, add validation on structured output, and route low-confidence cases to a human. We also measure accuracy on a held-out set before launch, so you get a number rather than a demo.