Capability 03
Predictive ML
Forecasting, anomaly detection, and recommendations trained and validated on your historical data.
- Stack
- PyTorch · XGBoost
- MAE reduction
- 42%
- ROI lift
- 2.3×
- Scale
- 1M/day
We build predictive models — demand and revenue forecasting, anomaly and fraud detection, and recommendation engines — on your own historical data, validated against held‑out periods so the numbers hold up out of sample, not just in a notebook.
Models ship with the plumbing that keeps them honest: feature pipelines, backtesting, and drift monitoring that flags when the world has moved and a retrain is due.
What a build includes
- Feature engineering and pipelines from your existing data sources
- Backtested forecasting, anomaly detection, or recommendations
- Drift monitoring with automatic retrain triggers
- Batch or real‑time scoring at up to 1M predictions/day