Predictive Inventory Solutions
Never overstock or stock-out again. We use AI to forecast demand and automate your supply chain purchasing.
Inventory decisions are usually made on recent sales averages and experience, which handles steady demand and fails at exactly the moments that matter most — seasonal peaks, promotions, and demand shifts. The result is capital tied up in slow stock alongside stockouts on the products customers actually want.
Impact
Stockouts cost the sale and often the customer, particularly where a competitor has the item available. Overstock ties up working capital and eventually becomes markdown. Both are usually happening simultaneously across different SKUs, which is why aggregate inventory value looks reasonable while the mix is wrong.
We build forecasting and replenishment systems that model demand at SKU level using your own sales history, seasonality, and lead times, then translate forecasts into concrete reorder recommendations your team can act on or override.
Technical Approach
Forecasting is built in Python against your historical sales and inventory data, with models chosen for the demand pattern rather than applied uniformly — steady, seasonal, and intermittent demand behave very differently. Integration with your commerce platform and ERP keeps data current, and recommendations surface in a purpose-built interface rather than a raw model output.
Reorder decisions are made from recent sales averages and buyer judgement, with seasonality and lead-time variability handled by intuition and safety stock.
Demand is forecast per SKU with seasonality and lead times accounted for, reorder points are calculated rather than estimated, and buyers spend their time on exceptions and judgement calls.
Strict adherence to global data privacy laws. We never train public AI models on your proprietary data.
Architecture designed to meet rigorous healthcare and enterprise security compliance standards natively.
Scalable cloud-native deployments via AWS and Vercel Edge networks ensuring 99.99% uptime.
Everything you need to know about our Predictive Inventory Solutions process.
Yes, we can set up automated workflows where the system drafts POs and sends them to your suppliers, only requiring a single human click to approve.
Ideally two or more years, because a single year cannot separate seasonality from trend. Useful forecasting is possible with roughly twelve months, with wider confidence intervals. For genuinely new products with no history, forecasting relies on comparable-product analogues, and we are explicit that those carry more uncertainty rather than presenting them with false precision.
If you tell it about them. Promotional periods must be marked in the historical data, otherwise the model treats a promotion-driven spike as normal demand and forecasts accordingly. Capturing promotional calendars, both past and planned, is a standard part of the implementation and materially affects accuracy.
No, and systems built on that premise tend to fail. Buyers hold context the data does not contain — a supplier having difficulties, a competitor exiting a category, a product being discontinued. The system handles routine forecasting across the long tail so buyers concentrate on judgement calls, and every recommendation can be overridden.
It depends heavily on your demand patterns, and any specific figure quoted before seeing your data is guesswork. Steady high-volume products forecast well; intermittent and highly seasonal demand is inherently harder. We backtest against your actual history during the project so you can see real accuracy per category before relying on it.
Let's build a predictive demand engine.
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