Predictive Analytics Platform
Time-series forecasting with confidence intervals and residual diagnostics, retrained on a schedule.
Planning ran on a single point forecast in a spreadsheet, with no sense of confidence and no way to catch when the model had drifted. By the time anyone noticed it was wrong, the quarter was already off plan.
Intervals over point estimates
Every forecast ships with a 95% interval. Planning decisions are made against the range, not a single number that was never going to be exactly right.
Production pipeline, not a notebook
The model retrains daily on a scheduled pipeline with validation gates. Residuals are monitored, and drift surfaces as an alert before it compounds into a bad plan.
Diagnostics in the open
RMSE, interval coverage, and residual plots are part of the product, not buried in a notebook. The people who rely on the forecast can see how much to trust it.
One pipeline. One source of truth. Modules over a shared core.
Forecasts now come with a defensible confidence range and a system that flags its own drift. Planning shifted from arguing about a single number to reasoning about a range — and catching problems before they cost a quarter.
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