Inventra AI replaces static reorder rules with continuously recalibrated, ML-driven demand forecasts and reorder logic — across multi-echelon supply chains. Free working capital, cut stockouts, keep service levels intact.
Where Inventra AI fits — inventory operations where forecast accuracy directly drives working capital and service level.
Most enterprises run inventory on rules someone set in 2018 — static economic order quantities, fixed reorder points, gut-feel safety stock. Those rules ignore everything that's true today: shifting demand patterns, promotional cycles, supplier lead time variance, and the simple fact that what worked at 200 SKUs breaks at 2,000.
Inventra AI replaces the static rules with continuously calibrated ML models, per SKU, per channel, per location. Demand is forecast with Prophet + ARIMA + LSTM ensembles. Reorder points adapt to actual lead time variance and service-level targets. Slow-mover detection flags SKUs eating working capital. Multi-echelon optimization treats the warehouse network as one system, not silos.
The result: less cash in inventory, fewer stockouts, and a planning team that finally trusts the numbers.
Layered design, production tooling, native Azure integration. Every component is one we use in shipping client systems — not a theoretical reference stack.
Outcome ranges are illustrative — based on structural economics of the problem and what comparable production systems achieve. Actual results depend on baseline maturity, data quality, and integration depth.
Our products are designed to compose. Inventra AI works standalone, but most enterprise engagements combine three or four — built on a shared data foundation and a single Azure tenant.
Tell us about your current setup and the outcome you'd want from Inventra AI. We'll come back within one business day with a path forward.