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?
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Without early warning
Stock-outs and overstock are only visible after they've already cost you
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Despite years of sales history
Demand planning still runs on gut feeling and last month's spreadsheet
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Without a forecast horizon.
Staffing, procurement, and fleet decisions get made a week too late
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By the time the report lands
The trend it describes has already changed
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Reorder cycles behind real demand
Replenishment always catches up, never gets ahead
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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
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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
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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
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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
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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.
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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
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27% fewer
stock-outs on high-turnover SKUs during the pilot -
19% lower
excess inventory on slow-moving items -
6 hrs/week
back per store manager, previously spent on manual reordering
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