AI inventory replenishment, demand forecasting and budget-driven buy planning for retailers.
Enter your store count and SKU catalog size for an instant monthly and yearly estimate — no forms, no sales call required.
buffers.ai is a real-time AI platform that automates demand forecasting, purchase planning, and inventory replenishment for retail and manufacturing enterprises, built from three focused modules: Forecasting for AI demand prediction, Replenishment for AI inventory replenishment, and Buy Plan for budget-driven, periodic purchase planning. It is used by global retail and manufacturing enterprises including H&M, Bath & Body Works, Victoria's Secret, P&G, and Toshiba.
Inaccurate demand forecasts and slow replenishment cause two costly outcomes at once: stockouts that lose sales, and excess inventory that ties up working capital. buffers.aiaddresses both by improving forecast accuracy per SKU and automating the resulting purchase and replenishment decisions, so safety stock reflects real, current demand rather than a static rule.
Forecasting backtests five algorithms (TimesFM, LightGBM, Prophet, Exponential Smoothing, and Moving Average) over a rolling six-month window, selects the closest-fitting champion model per SKU, and converts that forecast into a supply plan accounting for lead time, safety stock, minimum order quantities, budget, and open commitments. Replenishment then manages day-to-day store and channel inventory: restocking, assortment, new product introduction, promotional push, omnichannel balancing, and KPI reporting. For products with no sales history — or for franchisees and multi-banner retailers working to a periodic buying budget — Buy Plan generates category-and-month purchase quantities directly from that budget, independent of the forecasting pipeline above.
Three products, each licensed on its own: Forecasting for what to buy from suppliers, Buy Plan for spending a set buying budget, and Replenishment for what to send to each store
The right forecasting model for every SKU
Five forecasting models compete on each product's own history, the most accurate one forecasts it, and the result becomes a purchase plan for your central warehouse.
Turn a buying budget into a 12-month plan
For franchisees and multi-banner retailers held to a periodic budget: how much to buy in each category, each month, in units and dollars.
The right stock in every store, every day
Decides what each store needs from the central warehouse and when to send it, recalculating every product's target stock level daily.
buffers.ai was established in 2015, growing out of a consulting practice that spent years inside the inventory and demand-planning operations of retail and manufacturing companies — from luxury and fast fashion to footwear, grocery, consumer goods, and manufacturing operations like Toshiba's.
The goal from day one was a holistic supply chain solution, not another siloed reporting tool: one system carrying an operational action all the way from demand planning to supply, first allocation for new products, and day-to-day store replenishment. We built it as a call-to-action system — one that outputs the next order, not a BI system that hands you a report and leaves the decision to someone else.
That work kept surfacing the same problem: stockouts and excess inventory weren't a data problem, they were a process problem — replenishment decisions run on static rules and spreadsheets that couldn't keep pace with real, day-to-day demand. Rather than solve it one client engagement at a time, we built the automation platform that consulting work kept trying to become.
buffers.ai is that platform: years of hands-on inventory and forecasting expertise, encoded into software that makes the call every day instead of a consultant revisiting it every quarter.
Metrics reported by buffers.ai customers, sourced from published case studies
Delta Israel Brands Ltd. — deployed across European stores and online channels
“Among the systems currently in use, buffers.ai has proven to be the strongest performer in supporting our operational needs.”Read the Bath & Body Works case study
Grocery retailer managing 6,000–7,000 SKUs