This is like giving a store a pair of smart eyes: cameras and image-recognition software watch shelves and customer behavior, then an AI predicts what will sell next so buyers know what, when, and how much to reorder.
Retailers and consumer brands struggle to forecast demand accurately and align purchasing with real-time customer behavior; this uses AI image recognition to turn visual signals from stores (shelf stock, shopper interactions, product placement) into better sales forecasts and purchasing decisions.
Access to large volumes of in-store imagery and historical sales data, combined with retailer-specific purchasing workflows and integrations into merchandising and replenishment systems.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Video/image ingestion and storage costs, real-time inference latency from many cameras, and ensuring data privacy/compliance for in-store footage.
Early Majority
Uses AI image recognition as an additional, real-time signal for demand forecasting and purchasing, rather than relying solely on historical sales and ERP data as in traditional forecasting systems.