Custom AI model training, measured before it ships
A general purpose model is a good starting point and a poor finishing point. When accuracy on your own data is what decides whether a system is usable, the answer is usually a model trained for your task, and the harder part is keeping it good once it is live. This is model training, not a course: for teams inside businesses that have their own data and a task where a general model is not accurate enough.
What this covers
Data assessment and labelling strategy
Working out what data you actually have, what is usable, and the cheapest labelling approach that reaches the accuracy you need.
Fine-tuning and custom training
Adapting an existing model to your domain, or training a task-specific one where that is genuinely the better answer.
Evaluation harness
A held-out set and a scoring method agreed up front, so model changes can be judged rather than argued about.
Productionization and serving
Getting the model deployed, versioned and served at the latency and cost the product can live with.
Monitoring, drift and retraining
Watching live performance, catching drift as your data shifts, and retraining on a trigger rather than a hunch.
How we approach it
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01
Try the cheap option first
Prompting, retrieval and a better data pipeline often close the gap. We only recommend training when the evidence says the cheap options will not get there.
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02
Agree the quality bar before training
We define what accurate enough means for your use case, in your terms, before any model work starts.
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03
Build the evaluation before the model
Without a held-out set and a score, model work becomes guesswork and every change is a matter of opinion.
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04
Plan for the model getting stale
Data shifts. The retraining path and the monitoring that triggers it are part of the build, not a later project.
What you end up with
Every engagement is scoped to an agreed outcome and quoted as a fixed fee, so you know the cost before work starts. This work suits firms and teams that hold a labelled dataset and have a number that accuracy moves; size matters less than the data and a task worth the effort.
- A model measurably better than the off-the-shelf baseline on your data
- An evaluation harness you can rerun on any future change
- The model deployed and serving at a workable cost and latency
- Monitoring that tells you when quality slips
- A documented retraining path your team can run
Get a fixed-fee scope
Describe the task, the data you hold and how accuracy is measured today. You will get a considered reply from the engineer who would do the work, not a brochure.
This form is for businesses and teams. For anything else (roles, study, press, suppliers) email [email protected] with that word in the subject.
