Custom models & fine-tuning
Purpose-built models, or fine-tuned foundation models, matched to the problem and the data you actually have.
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Machine Learning
From experiment to production.
A model that scores well in a notebook has proven a hypothesis; it has not yet done anything useful. Most machine-learning value is won or lost in the distance between that notebook and a service the business can depend on, and that distance is an engineering problem.
We build ML systems that are reproducible, evaluated and monitored, so a model keeps earning its keep long after the launch demo.
What we do
The work, in detail.
Feature engineering & stores
Reusable, versioned features so training and serving see exactly the same thing.
Reproducible training
Versioned data, code and runs, if you cannot rebuild a model exactly, you cannot trust it.
MLOps & serving
CI/CD for models, scalable serving, and safe, gradual rollout of new versions.
Evaluation & monitoring
Automated evaluation gates plus live monitoring for drift and performance decay.
Forecasting, recsys & prediction
Production systems for demand forecasting, recommendation, ranking and risk.
How we deliver
A disciplined path to production.
- 01
Frame the problem
Define the decision the model serves and the metric that actually reflects success.
- 02
Build the data & features
Assemble reproducible datasets and a feature pipeline shared by training and serving.
- 03
Model & evaluate
Iterate against a representative evaluation set, not a single flattering number.
- 04
Productionise
Wrap the model in serving, CI/CD, versioning and rollback.
- 05
Monitor & retrain
Watch for drift and decay, and make retraining routine rather than a research project.
What you get
Outcomes, not artefacts.
- Models running as reliable production services
- Fully reproducible training and versioned artefacts
- Evaluation gates that stop bad models reaching users
- Monitoring for drift, decay and data issues
- A retraining path your team can run themselves
Questions
Good questions, answered.
Whichever serves the outcome. Often the right answer is a fine-tuned or off-the-shelf model wrapped in excellent engineering, not a bespoke model built for its own sake.
Monitoring for data and prediction drift, evaluation gates on every change, and a routine retraining path, so decay is caught and corrected, not discovered in an incident.
Yes, we frequently partner with in-house teams, bringing the MLOps and production discipline that turns their research into dependable systems.
Related insights
All insights Further reading.
From notebook to production: what MLOps really means
A model in a notebook is a hypothesis. A model in production is a system. MLOps is the discipline that gets you reliably from one to the other.
11 March 2026Read
Why your data foundations decide whether AI succeeds
Most failed AI initiatives are not model failures, they are data failures. Here is how to build the foundation that makes everything downstream possible.
14 August 2026Read
Have something worth building well?
Whether you are starting from a blank page or rescuing something that has outgrown its foundations, let's talk about what good looks like.