An ML-based demand forecasting system that replaces spreadsheet-driven inventory planning with data-backed reorder recommendations.
Representative engagement — anonymised and illustrative. It describes the kind of problem we solve and the platform we build, not client-confidential performance data.
A fast-growing e-commerce company with 12,000+ SKUs was losing revenue due to stockouts and overstocking. Inventory planning was based on manual spreadsheets, with no consideration for seasonality, promotions, or supplier lead times.
We built an AI-powered forecasting system that replaced manual inventory planning. The solution includes a time-series forecasting model trained on 3 years of historical data, automated reorder recommendations, and a management dashboard showing forecast accuracy and stock health.
Time-series forecasting that accounts for seasonality, promotions and supplier lead times
Automated reorder recommendations replace manual spreadsheet planning
Forecast-accuracy and stock-health metrics visible on a management dashboard
Fewer stockouts and less overstock as planning decisions move onto reliable signals
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