operations

Forecasts from the data you already trust.

Orders in the ERP, sell-through in the warehouse, seasonality in three years of history. Your agent fits the forecast in the runtime and returns the plan — reorder points, not a research paper.

Why it works here

Demand forecasting, the Contextually way.

Every input, one frame

Order history, inventory positions, promotions calendar, and lead times combine inside the runtime — the join your planning spreadsheet approximates.

Method fit to the series

Stable SKUs get seasonal decomposition; volatile ones get something sturdier. The agent chooses per SKU family, case by case.

An answer, not an export

What returns is the decision layer: reorder quantities, risk flags, confidence ranges — sized to be read, not re-analyzed.

The flow of data
snowflake.sales_history_3y 8,204,551 rows erp.inventory 1,142 SKUs contextually runtime forecast + reorder_points read-only · audited 12-week plan · 9 flags 1,048 tokens → context
The program, case by case
goal → 12-week demand forecast + reorder points, top SKU families
hist = sql("snowflake", "SELECT sku, week, units FROM sales_history_3y") inv = sql("erp", "SELECT sku, on_hand, lead_time FROM inventory") fc = forecast(hist, horizon="12w", method="auto_per_family") plan = reorder_points(fc, inv, service_level=0.95) return summarize(plan, flag="stockout_risk")
fit on 8,204,551 sales rows · 1,142 SKUs returned reorder plan + 9 risk flags · 1,048 tokens → context
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See it on your data.

Book a demo and we'll run demand forecasting against systems like yours — your stores, your policies, live — and map the path to a deployment in your VPC.