Models that earn their place
We start with a simple baseline and a success metric that matters to the business — revenue protected, hours saved, error rate reduced. Every model we build has to beat that baseline on held-out data before it goes anywhere near production.
Our approach
- Frame the problem and agree on a measurable target
- Explore the data and engineer robust features
- Train candidate models, from gradient boosting to deep learning
- Evaluate with proper validation, fairness checks and explainability
- Deliver the model with code, documentation and a deployment path
Common projects
- Weekly demand forecasts by SKU, store or region
- Lead scoring that prioritises the right prospects
- Churn prediction with the reasons behind each score
- Anomaly detection for transactions, sensors or pricing