Predictive analytics turns historical data into forward-looking signals for demand, churn, risk and supply. We start with the decisions you need to make and work backwards to the features, models and dashboards that support them. Clear framing avoids over-fitting and ensures adoption by the business.
Our teams mix tree ensembles, gradient boosting and time-series models with deep learning where patterns are non-linear or sparse. We engineer features, manage leakage, and build robust cross-validation so metrics reflect reality. Explainability and sensitivity analysis help stakeholders trust and act on the output.
We productionise models with MLOps best practices, ensuring fresh data, retraining schedules and drift monitoring. Compact dashboards show accuracy, drivers and confidence intervals so teams understand when to rely on the forecast. This creates a feedback loop that improves both the system and the process it supports.
