A model that lives in a notebook has not helped anyone. We take AI work from the decision it is meant to improve through to production, behind access controls, with monitoring and an explanation a person can inspect.
Every bullet below is work we have shipped. Where a line looks like filler on another company's site, it is here because a client needed it and we built it.
Custom models trained on your own data, from dataset construction and labelling through training, evaluation and deployment — including fine-tuning open models where a general one will not do.
Classification, ranking, forecasting and risk scoring.
Detection, segmentation and automated quality inspection, including on-device inference for places with no reliable connection.
Retrieval-augmented assistants, document understanding and task agents grounded in your own content, so an answer can be checked against a source rather than trusted on tone.
The pipelines underneath.
Where a model touches a decision about a person, the explanation and the contest route are part of the deliverable, not a later phase that never gets funded.
The same artefacts every time, regardless of project size.
Deliberately narrow. A team our size claiming twenty frameworks is claiming one thing badly, twenty times over.
Open the work page for the full case note on any of these, including what did not go to plan.
50% less manual monitoring and 35% more consistent identification.
Pixel-level instance segmentation separating scratch from dent on the CarDD benchmark.
Speech and text fused at decision level, beating both single-modality branches.
Answered as we would answer them on a call, including the ones with awkward answers.
Usually less than you fear and more than you think, because operational systems hold more than people remember. The data audit in week two answers this properly, and it is the cheapest way to find out.
No. Client data trains client models. If a project benefits from a shared base model, that base is a public or licensed one, never another client's data.
It will. Performance decays as the world moves away from the training data. We build drift monitoring and a retraining schedule into the original scope so this is routine rather than a crisis.
Yes. We work with local models through Ollama and self-hosted inference where data cannot leave your estate.
The reason a product is not working is rarely where the client first thought it was, so these three sit alongside this one more often than not.
Describe the problem rather than the specification. If a smaller piece of work would answer it, we will propose that instead.