AI turns your historical data into what's coming next
We build predictive models on your own sales, inventory, and operations data
By the numbers
Up to 42% lower
forecast error vs. traditional statistical methods (ARIMA, exponential smoothing)
20–30% inventory reduction
while maintaining or improving service levels
65% fewer
lost sales from stock-outs
Up to 50% less
unplanned downtime with predictive maintenance
Does this sound familiar?
Without early warning
Stock-outs and overstock are only visible after they've already cost you
Despite years of sales history
Demand planning still runs on gut feeling and last month's spreadsheet
Without a forecast horizon.
Staffing, procurement, and fleet decisions get made a week too late
By the time the report lands
The trend it describes has already changed
Reorder cycles behind real demand
Replenishment always catches up, never gets ahead
Years of ERP and BI history
Sitting in dashboards that describe the past and nothing else
See what your data can already predict
Send us a data sample. We'll tell you what's forecastable, how accurate it can get, and whether a pilot is worth running
Review my use case
When your data goes in, a decision comes out
Connect
existing ERP, POS, WMS, CRM, or sensor data, no new systems required
Predict
Model trained on your own history forecasts demand, risk, or failure
Act
Alert, a reorder trigger, or an updated plan, before the issue reaches your team
Your data never leaves your infrastructure
No third-party data sharing.
Sales, inventory, and operational data are never sent to an external provider for training or inference
Self-hosted or private cloud deployment.
Models run on your servers or a private cloud
Built for regulated data.
Financial, logistics, and operational data stay under your access controls from day one
Case
Client — regional grocery retail chain, 35+ stores
Challenge:
Store-level demand planning ran on manager intuition and last month's sales spreadsheet. High-turnover SKUs were frequently out of stock for 2–3 days before anyone noticed, while slow movers piled up in the back room, tying up working capital
What we did:
Connected the client's existing POS and inventory data to a forecasting model trained on their own SKU-level sales history, seasonality, and store-level patterns. Ran a pilot across 4 stores, validating forecasts against real demand before rolling out chain-wide.
Quote:
"We stopped ordering by feel. Now the system tells us what's about to run out before it happens." Supply Chain Manager of the Company
27% fewer
stock-outs on high-turnover SKUs during the pilot19% lower
excess inventory on slow-moving items6 hrs/week
back per store manager, previously spent on manual reordering
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