PREECURSOR
AI consulting use case

AI consulting for demand forecasting

Forecasts that beat last year's average — probabilistic, explainable, and wired into the planning decisions they're meant to drive.

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The problem

Why demand forecasting is hard to get right

Most demand plans are a spreadsheet anchored to last year plus a gut adjustment, so you carry too much of the wrong stock and run out of the right. Promotions, weather, and new products break the model exactly when accuracy matters most. The challenge is forecasts that are accurate at the granularity decisions are made — SKU, store, week — and trusted enough that planners actually let them drive replenishment.

How we build it
01
Hierarchical, probabilistic forecasts
Models that forecast at item-location-period and reconcile up the hierarchy, with prediction intervals planners can stock against.
02
Drivers, not just history
Price, promotion, weather, and calendar effects modeled explicitly so the forecast reacts to the events that move demand.
03
New-product and cold-start handling
Analog and attribute-based methods to forecast items with little or no history, where naive models simply fail.
04
Decision integration
Forecasts pushed into replenishment, allocation, and S&OP so the accuracy gain shows up as fewer markdowns and stock-outs.
The outcome

Sharper forecasts at the level you actually plan — fewer stock-outs and lower markdowns — backed by intervals and driver explanations that earn planner trust.

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Put AI to work on demand forecasting

Bring us the metric you need to move. We will tell you what we would build, how long it takes, and what it is worth.

See our work