Predictive Analytics & Business Intelligence

We build predictive models for demand forecasting, churn prediction, and inventory/sales projections using time-series methods (Prophet, ARIMA) and standard classification/regression approaches, matched to the pattern in your actual historical data rather than a one-size model applied by default. These models are only as useful as the decisions built around them, so every engagement includes the BI layer — dashboards in Power BI or Tableau that put the model's output in front of the people making planning decisions, not just a data science notebook.

This is a meaningfully different service from a standard BI dashboard project: we're building the forecasting or prediction logic itself, then wiring it into a dashboard, rather than just visualizing historical numbers. That means a data-readiness check on your historical data volume and quality is a required first step before we commit to a model approach.

• Demand forecasting, churn prediction, and sales/inventory projection models
• Time-series forecasting (Prophet, ARIMA) and standard ML classification/regression
• BI dashboards in Power BI or Tableau built around model output
• Historical data quality and volume assessment before model selection
• Ongoing model retraining as new data accumulates