Retailers and consumer businesses collect large amounts of behavioral and transactional data across brands, regions, channels, and supply-chain partners. These datasets are rarely unified due to internal boundaries, privacy considerations, and competitive sensitivities. Pricing and recommendation models contain proprietary logic that organizations prefer not to disclose. Many AI opportunities—forecasting, personalization, assortment optimization—require insights across data sources that cannot be easily combined.
Super enables retailers, suppliers, and business units to use shared or sensitive data for AI without exposing raw customer information or internal algorithms. Each participant keeps data within its domain while contributing it securely to joint analytics or model training. Workloads can run across clouds, regional systems, or in-store deployments while maintaining strict separation from underlying platforms and partners.
Retailers can use broader signals for forecasting, personalization, and planning without centralizing customer data or exposing competitive strategy.
