Solutions / AI prediction

Forecasts your planners can trust

Demand and revenue forecasts that track reality closely, so you plan inventory, staffing and capacity with confidence

Multi-horizonPer date-shop-SKURevenue-aware
9:41
Demand forecastper SKU · per store daily → quarterly
Retail / Supply chain · in production
95%
On track
MAPE 15–20% · date-shop-SKU
SKU-4471 · cluster MSK-N · h+14d
Forecast vs actual
SKU-4471 · MSK-N842 u±4%
SKU-2093 · SPB-11,210 u±6%
multi-horizon 84k series
iPhone running Demand forecast
+13%revenue impact · in production
−24%write-offs, fresh categories
250%ROI over 3 years
What we build

From noise to a forecast you can order against

Gradient-boosted models per SKU learn cycles, promo elasticity and live shocks, and serve a forecast resolved at every planning horizon

Raw demand stream · noise streaming
skushopdate7dqty
∑
Demand model
gradient-boosted · per SKU
Drivers & scenarios accounted for
Sales history & trend
24 months
Seasonality
weekly + yearly cycles
Promotions & price
elasticity + events
Weather & local events
external feed
Stockouts & cannibalization
censored demand
Fit on the historical period52%
Forecast · SKU-4471 · Store cluster MSK-N
min error max profit
actuals fit forecast confidence

Streaming raw demand. Thousands of date × shop × SKU events arrive as noise, no shape yet.

MAPE · date-shop-SKU
0%
forecast error band
Out-of-stock
−0%
shelf availability up
Revenue · $
+0%
profit-aware targets
ROI · 3 yrs
0%
over baseline ops

Two ways to miss: both expensive

Every demand number has a two-sided cost. Forecast too low and you stock out: the sale walks to a competitor. Too high and cash freezes on the shelf, then gets marked down. Precision is what collapses both costs.

Our forecast hugs real demand: the confidence band stays tight per date, shop and SKU.
Multi-horizon planning

One model, every planning cadence

The same demand signal, resolved at four horizons and synced to your order cycles, so each team plans on a number built for their own decisions

Day
Store replenishment

Per-store, per-SKU orders for tomorrow: the tightest band, refreshed daily.

Week
Staffing & promo prep

Labour and promotion allocation ahead of the week's demand shape.

Month
Category & supplier

Category plans and supplier orders aligned to real lead times.

Quarter
Procurement & capacity

Volume commitments, warehousing and capacity for the season ahead.

Per item, category and store, synced to your ordering cycles.

The challenge

Forecasts you can't order against

Manual forecasting

Manual forecasts miss seasonality, promos and shocks.
Over-stock freezes cash; out-of-stock loses the sale.
One global model ignores per-store, per-SKU behaviour.
Accuracy metrics never connect to revenue.

With Zentavor

Gradient-boosted models per SKU learn cycles, promo elasticity and live shocks, hitting 15–20% MAPE.
Profit-aware targets price both errors: −15–20% out-of-stock, −22–28% write-offs.
A forecast resolved per date × shop × SKU: every store and item gets its own demand shape.
Targets optimise revenue and margin, delivering +8–12% revenue recovery.
FAQ

Your frequently asked questions

How accurate is the forecast?
We report error per horizon, not one global number: typically 15–20% MAPE at date × shop × SKU. Fresh and high-velocity categories are tracked separately because their cost of error is different.
What data do you need to start?
About 24 months of sales history is enough to begin. We then add drivers incrementally: seasonality, promotions and price, weather and local events, and stockouts (censored demand). The fit sharpens with each.
Does our data leave our environment?
No. The models run inside your perimeter, on-prem or private cloud, so demand and sales data never leave your environment.
How long until we see the effect?
We pilot on your data and measure it with an A/B test on real revenue, read over weeks or months. This is the same way the +13% result was proven, not with a backtest.
Does it work per-SKU at our scale?
Yes. Forecasts are produced per item, category and store, proven in production at 1,500 stores and 400,000 orders a day.
Accuracy or revenue: what is optimized?
Revenue and margin, not just the error metric. Targets are revenue-aware, so the model spends its accuracy where it moves the business most.
Let's talk

Plan with forecasts you can trust

Share your demand data and planning cycle. We'll scope a forecasting model and rollout

Request a demo