MetaLearner provides AI‑driven agents that automate the entire supply‑chain planning workflow, turning existing spreadsheets, exports, and databases into accurate demand forecasts, inventory recommendations, and scenario analyses. Its Forecasting Agent generates SKU‑level, explainable forecasts with uncertainty ranges, while the Scenario Agent runs what‑if simulations to evaluate impacts of price changes, supplier delays, tariffs, and demand spikes on working capital, service levels, and revenue. The solution requires no data‑science or IT overhead, enabling teams to make data‑backed decisions quickly.
Funding
Funding not disclosed
Founders
Product
Problem
Supply‑chain planning teams often rely on manual spreadsheets and disparate data exports, making demand forecasting and inventory decisions time‑consuming, error‑prone, and difficult to incorporate external factors such as price changes or supplier delays.
Solution
MetaLearner offers AI‑driven agents that automate the full planning workflow by ingesting existing spreadsheets, exports, and databases without requiring additional data‑science resources. The Forecasting Agent generates SKU‑level demand forecasts that include driver attribution and uncertainty ranges, enabling transparent, explainable planning. An Inventory Agent translates these forecasts into actionable inventory decisions, while the Scenario Agent runs what‑if simulations to assess the impact of price shifts, supplier disruptions, tariffs, and demand spikes. The platform quantifies trade‑offs across working capital, service level, and revenue, providing decision makers with clear, data‑backed options before committing to a plan.
Target Audience
Primary customers are supply‑chain planners, demand‑forecasting analysts, and operations managers in mid‑size to large enterprises that need accurate, automated forecasting and inventory optimization without extensive IT overhead.
Features
- Automated data preparation that connects directly to existing planning files and databases
- Explainable SKU‑level forecasts with driver attribution and calibrated uncertainty intervals
- Inventory decision engine that converts forecasts into optimal stock recommendations
- Scenario modeling that evaluates price changes, supplier delays, tariffs, and demand spikes
- Quantitative trade‑off analysis of working capital, service level, and revenue impacts
- Cloud‑based decision layer requiring no on‑premise data‑science or IT implementation