SuperCommerce customers manage catalogs with hundreds of thousands of SKUs and fulfillment networks spanning dozens of distribution nodes. These are the outcomes they measure.
The customers referenced here are composites drawn from our pilot cohort. Specific results vary by catalog size, existing system configuration, and operational starting point.
An apparel retailer bringing 4,000 new SKUs to market each season spent 11 days per drop on manual tagging, attribute enrichment, and search-indexing prep. After connecting their ERP feed to SuperCommerce Catalog AI, the same process completes in under 18 hours. New styles go live 9 days earlier per season, and manual attribute entry dropped 87% per SKU.
A furniture and home goods retailer operating 14 distribution centers was manually triaging 800 to 1,200 fulfillment exceptions per week. SuperCommerce Fulfillment AI now handles 78% of those exceptions automatically, routing to the optimal node based on real-time stock and SLA windows. Late shipments from routing failures dropped 34%.
A sporting goods retailer with 520,000 active SKUs discovered that 14% of their catalog had missing or outdated attribute data, suppressing those items in filtered search results. SuperCommerce analytics surfaced the problem; a targeted enrichment pass fixed 71,000 SKUs in four days. Category impressions lifted within two weeks of correction.
Measured across our pilot retailer cohort through Q2 2026.
Our pilot program runs against your actual inventory feed. You see results before you commit to a full deployment.