Machine learning finds patterns in your historical data and uses them to predict what happens next: which customers may leave, how much stock you will need, which applications carry more risk. We build models that feed those predictions into the tools your team already uses.
What's included
- A data audit to check whether your data can support the prediction you want
- Demand forecasting, churn prediction and lead or credit scoring
- Recommendations for products, content and next actions
- Anomaly detection for fraud, quality and equipment data
- Explanations that show which factors drove each prediction
- Deployment as APIs, with monitoring for accuracy and data drift
How we work
We agree a business measure first, such as fewer stock-outs or faster approvals, and compare every model against a simple baseline. Only models that clearly beat the baseline go into production, where they are monitored and retrained as your data changes. You receive the model, its documentation and the code that retrains it.