How to match sizes and colors across stores so AI agents can buy the right one

Channel3 explains how it builds a product catalog for AI agents by storing products rather than web pages, using per-category rules to decide which variations count as the same product, reading option values instead of labels, aligning each store's size and color buttons to the brand's naming, and mapping values onto fixed per-category lists so search can filter exactly. The article walks through adidas Gazelle Indoor and Nike Air Force 1 examples, noting that over 90% of apparel values, over 95% of shoe values and over 75% of furniture values map onto these lists. It argues structured listings let an agent query every listing at once and get consistent answers, unlike web search built for people.