When Off-the-Shelf
Will Not Do
Some problems are too specific for a general model — your terminology, your edge cases, your formats. We tune on your examples and hold back a test set, so accuracy is measured rather than claimed.
When Off-the-Shelf Will Not Do
Some problems are too specific for a general model — your terminology, your edge cases, your formats. We tune on your examples and hold back a test set, so accuracy is measured rather than claimed.
- Tuned on your own labelled examples and domain language
- Measured against a held-out set you can inspect
- Deployed where it belongs — your cloud or ours
- Retrained on a schedule as your data shifts
How It Runs In Your Business
How a model is trained and proven, what the first month with us looks like, and where a bespoke model earns its keep in your sector.
How it works
- The problem is framedWhat counts as right, what counts as wrong, and what level of accuracy is worth paying for.
- Your examples are labelledA training set is built from your data, with a test set held back and never trained on.
- The model is tuned and measuredReported against that held-out set, class by class, so weak spots are visible rather than averaged away.
- Deployed and watchedIn your cloud or ours, with drift monitoring and a retraining schedule.
How we deploy it in your business
- Feasibility Week 1An honest read on whether your data can support the accuracy you need.
- Labelling Weeks 2–3Done with your subject experts, because only they know the edge cases.
- Train and evaluate Weeks 4–6Iterated until it clears the bar you set — or we tell you it cannot.
- Deploy with monitoring OngoingVersioned, logged and retrained on a schedule as your data shifts.
What it looks like in your industry
The Rest Of What We Build
Six more capabilities, each one scoped before you commit, running on your own data, and yours to keep.
A Model That Speaks Your Language
Bring us the problem a general model keeps getting wrong. We will tell you whether fine-tuning is the answer, how many examples it would take, and how we would prove it works.