Machine Learning Development

Not every problem needs a large language model. A well-specified machine learning model, trained on your own data, is often cheaper to run, easier to explain and considerably more accurate for the narrow job you actually need doing. AI Applied builds and deploys custom machine learning models for UK organisations, and we are equally happy to tell you when a simple rules engine would do the job better.

Where custom machine learning earns its place

Custom models make sense when you have historical data that encodes something valuable, when the decision repeats often enough to justify automation, and when you need consistent, measurable behaviour rather than open-ended generation. Typical examples include demand and capacity forecasting, churn and propensity scoring, anomaly and fraud detection, quality classification from images or sensor data, document and case routing, and pricing or risk models.

They make less sense when you have a few hundred rows of data, when the underlying process changes every quarter, or when the real problem is that nobody agrees on the definition of the outcome. We check for those conditions before proposing a build.

How we build models

Problem framing

We turn a business question into a prediction task with a target variable, a unit of analysis, an evaluation metric and a baseline. The baseline matters: if a three-line heuristic gets you 85 percent of the value, that is the bar a model has to beat, and sometimes it does not.

Data assessment

We audit what you have for coverage, leakage, label quality, class balance and drift. Most model failures are data failures discovered late. We surface them in week one with a written assessment you can act on regardless of whether you proceed with the build.

Modelling and evaluation

We start simple and only add complexity when it pays for itself in held-out performance. Gradient boosted trees, regularised linear models and classical time series methods solve a large share of real business problems. Deep learning gets used where the data justifies it, typically for images, audio, or genuinely large sequence problems. Every model ships with a documented evaluation on data it has never seen, plus calibration and error analysis broken down by segment.

Fairness, explainability and documentation

If a model influences decisions about people, we test performance across relevant subgroups, produce feature attribution so decisions can be explained, and write a model card recording intended use, limitations and known failure modes. This is increasingly a regulatory expectation as well as good practice.

Deployment and monitoring

A model that lives in a notebook is not a product. We package models behind versioned APIs or batch pipelines, wire them into your systems, and add monitoring for input drift, prediction drift and performance decay. Retraining is scheduled and automated where the data supports it, with a manual approval gate where it does not.

Combining classical ML with language models

The strongest systems we build usually combine both. A language model handles the messy unstructured input, extracting structure from an email, a PDF or a call transcript. A classical model then makes the actual prediction on that structured data, where it is faster, cheaper, auditable and testable. The language model never gets to make the decision on its own.

The stack we work in

• Python with scikit-learn, XGBoost, LightGBM, PyTorch and standard time series libraries.
• Data platforms including SQL warehouses, Databricks, Snowflake, BigQuery and plain Postgres.
• Cloud deployment on Azure, AWS or Google Cloud, or on-premise where data residency demands it.
• Experiment tracking, model registries and reproducible pipelines so results can be recreated months later.
• Containerised serving with CI/CD, so a model update follows the same release process as any other code change.

What you get

Working code in your repository, a trained model with a documented evaluation, a deployment you can run, monitoring dashboards, and a handover session that leaves your team able to maintain and retrain it. We do not keep the interesting parts in a black box.

Engagement options

A data assessment is a fixed-fee, fixed-duration piece of work that ends in a written recommendation. A model build is quoted per milestone against an agreed evaluation target. Longer programmes run as monthly increments you can stop at the end of any month. Where a model informs regulated or high-impact decisions, we bundle in the controls described in our AI governance and compliance service.

Talk to us about a model

If you have a repeated decision, a pile of historical data and a suspicion that it could be done better, we can usually tell you in one conversation whether the data supports a model and roughly what it would take. See also our AI implementation services for the wider delivery picture, or contact us directly.

Explore our services

A full index of what we do is on the AI services page. The individual services are:

• AI readiness assessment
• AI implementation services
• Machine learning development
• Data engineering
• AI automation
• AI governance and compliance

Where we work

We deliver across the United Kingdom from our Glasgow studio, with on-site time included: London, Manchester, Birmingham, Edinburgh, Glasgow.