Machine Learning & Predictive Intelligence
We build models that learn from your years of past data. They forecast demand, score risk and flag what is coming next.
Predict, Don't React
Spot demand, churn and breakdowns while there is time to act.
Accuracy That Holds
We test models against real outcomes, not only a test set.
Continuously Retrained
We retrain as your market shifts, so the model stays current.
Production-Grade
Watched, logged and under control from day one.
Our Machine Learning Capabilities
Forecasting Models
Demand, revenue, capacity and stock, planned week by week.
Classification & Scoring
Score risk, churn, intent and each new lead.
Anomaly Detection
Spot the odd one out in payments, sensors and logs.
Recommendation Systems
Rank products and content for each customer.
MLOps Pipelines
Training, release and drift checks that run on their own.
Model Explainability
Show an auditor — or the RBI — why the model made each call.
Where This Creates Impact
- Improvement in Forecast Accuracy
- 25–40%Improvement in Forecast Accuracy
- Reduction in Inventory Holding
- 30%+Reduction in Inventory Holding
- Precision on Risk Scoring
- 90%+Precision on Risk Scoring
- Faster Model Deployment
- 50%+Faster Model Deployment
Figures are typical ranges observed across comparable engagements, not guaranteed outcomes.
Popular Use Cases
Demand Forecasting
Plan production and stock against real demand signal.
Churn Prediction
Find the accounts likely to leave, while you can still save them.
Fraud & Risk Scoring
Score each payment and loan file as it arrives.
Predictive Maintenance
Fix the machine in quiet hours, before it stops the line.
How This Actually Works
Most of this work is not modelling. First we settle what you are really predicting. Then we build a clean history for the model to learn from. Then we set an honest baseline, which is usually your current process. The model must beat that baseline to be worth running.
After that, picking the model is quick. For tabular business data, gradient boosting usually beats the fashionable options, and it costs far less to run. We would rather ship that than something clever your team cannot maintain.
What You Get
- A feature pipeline drawn from your source systems
- A trained model, measured against your current way of working
- A reason attached to each prediction
- Retraining and drift checks that run to a schedule
- A link into the system where the decision is made
When This Is Not the Right Fit
We would rather tell you now than three weeks into a project.
- You have a few hundred records — statistics, not machine learning, is the right tool at that scale
- The thing you want to predict is not recorded anywhere in your history
- The process changes so much each year that past data no longer describes the future
How We Deliver
- 01
Discover
Understand your business, data, challenges and goals.
- 02
Design
Prioritise use cases and design the solution architecture.
- 03
Build
Develop, train and validate models against real outcomes.
- 04
Deploy
Integrate into your systems with monitoring and governance.
- 05
Optimise
Measure, refine and scale what demonstrably works.
Machine Learning — Common Questions
For demand forecasting, two to three years of history covers the seasons. For sorting or scoring, a few thousand labelled examples is often enough to start. Less than that still works. The model is simply less sure, and we tell you how sure it is.
That is drift, and it is normal rather than a fault. We compare what the model predicts against what actually happens. We retrain on a schedule you agree, say once a month, or sooner if drift crosses your threshold. A model shipped with no watch on it will quietly decay.
Yes. Where the decision affects a person — credit, hiring, risk — we use models you can read. Each prediction comes with the reasons that drove it, and their weight.
We will not quote a number before we have seen your data. Anyone who does is guessing. The pilot sets a baseline from your current process. What matters is the gap between the two, not an accuracy figure quoted in advance.
Ready to put your data to work?
Book a 30–45 minute discovery call. No commitment — just a clear view of what is realistic for your business.
Book a Free Consultation