AI Development

Machine learning solutions

Not all AI is a chatbot. If you have a history of structured data, classical machine learning can forecast demand, spot problems and predict outcomes, often cheaply and very reliably.

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Language models get all the attention, but much valuable AI is still ordinary machine learning on tabular data. Predicting which customers may leave, forecasting stock, spotting odd transactions and scoring leads all use techniques such as gradient-boosted trees. Models like these are cheap to run, fast, and easier to explain than an LLM.

The first question is whether you have the data to support it. You’ll need enough historical examples, recorded consistently, including the outcome you’re trying to predict. Many projects stop here, and it’s better to find out in a short assessment than midway through a build. Sometimes a simple rule or a spreadsheet model gets you most of the way, and that’s a perfectly good outcome.

A model isn’t a product

Much of the effort comes after the notebook. The model needs reliable data feeds, a way of being deployed, monitoring for drift as the world changes, and a way of being retrained. If its predictions affect people, as in hiring, lending or insurance, you’ll also need to consider fairness and explainability. The ICO’s guidance on AI and data protection covers this, and such decisions may fall under UK GDPR’s rules on automated decision-making.

Problems ML is good at

Demand and stock planning

Forecasting what you’ll need from past sales, seasonality and other signals.

Spotting the unusual

Finding suspicious transactions, faulty equipment readings or data errors among normal activity.

Prioritising effort

Ranking leads, cases or customers by how likely they are to need attention.

What the work involves

Data assessment

A short study of whether your data can support the prediction you want.

Baseline first

Comparing every model against a simple baseline, so that the added complexity is justified.

Model development

Feature engineering, training and validation on properly separated data.

Deployment

APIs or batch jobs that fit into your existing systems.

Monitoring and retraining

Tracking accuracy and drift, with a process for updating the model.

Where it fits

  • Stock and demand forecasting
  • Customer churn prediction
  • Fraud and anomaly detection
  • Product recommendations

Technology

  • Python
  • scikit-learn
  • XGBoost
  • PyTorch
  • pandas
  • MLflow
  • FastAPI
  • AWS SageMaker

Machine Learning questions

How much data do we need?
There’s no single number, as it depends on how hard the problem is and how consistent the data is. A short assessment on your data will answer this better than any rule of thumb.
Is this different from generative AI?
Yes. Classical machine learning generally predicts or classifies from structured data. Generative AI works with language and images. Plenty of projects only need one, and some use both.
Can you explain why a model made a decision?
For many models, yes. Tools such as SHAP show which factors influenced a prediction. Where decisions affect people, we’d favour models that can be explained.

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