solutions/demand forecasting

Plan around demand, not around guesses.

Per-SKU demand with an honest confidence range and the drivers behind it, so you can size inventory, staffing, and spend with the uncertainty in view.

demand_30d.forecastlive
next 30 days
+0%
± 0% band
now
driverspromoseasonality
Why a score isn't enoughA / the problem >>>

A single forecast number hides the risk. Teams either over-stock to be safe or stock out and lose sales, because the forecast came with no range and no explanation of what's driving it.

From data to decisionB / how it fits >>>
// you feed it

Historical demand per SKU, plus the signals that move it: promotions, seasonality, price, events.

// it decides

It forecasts demand with a calibrated confidence band and attributes the movement to its drivers.

// you get

A per-SKU forecast with an upper and lower range and the reasons behind each inflection.

How it worksC / the engine >>>
01

forecast with a band

Every SKU gets a central forecast and a calibrated confidence range, not a single fragile number.

02

attribute the drivers

Promotions, seasonality, and price effects are separated out so you can see what's moving demand.

03

plan against risk

Size inventory and spend against the range, with the cost of over- and under-stocking in view.

What it runs onD / inputs >>>
[✓]Historical demand per SKU
[✓]Promotions & price calendar
[✓]Seasonality & events
[✓]Lead times
What you walk away withE / outcomes >>>
±4%honest confidence band, not false precision
per-SKUgranular, not a single top-line
driver-awaresee why demand moves
Common questionsF / faq >>>
Other leversG / more >>>

Stop guessing. Start deciding.

engine: online