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Lily AI uses AI models trained on millions of consumer search queries and product images to generate rich, consumer-centric product attributes. These attributes align product listings with the language shoppers actually use, improving relevance in onsite search, organic SEO, Google Shopping feeds, and recommendation engines. By bridging the gap between internal merchandising terminology and consumer vocabulary, Lily AI ensures products surface for the queries most likely to convert.
Retailers typically see incremental organic traffic increases of 20-40%, conversion rate improvements of 5-9%, and measurable lifts in Google Shopping and Performance Max campaign performance within 60-90 days of full deployment. Results vary based on catalog size, existing content quality, and the breadth of channels receiving enriched attributes. Lily AI provides performance reporting tied to traffic, conversion, and revenue metrics so teams can quantify ROI.
Yes, Lily AI's Application Layer architecture is designed to integrate with existing ecommerce technology stacks rather than replace them. The platform connects with major search providers, PIM systems, content management platforms, and advertising channels through APIs and pre-built connectors. Retailers can layer Lily AI's attribute enrichment onto their current infrastructure without migrating away from existing tools.
Lily AI specializes in fashion, apparel, home goods, furniture, and beauty retail categories. The platform's taxonomy and computer vision models are purpose-built for these verticals, with thousands of category-specific attribute values covering silhouette, pattern, texture, material, color, style, occasion, and other dimensions relevant to how consumers search for these products. The platform does not currently support non-retail verticals such as electronics, grocery, or automotive.
Lily AI continuously monitors millions of consumer search queries, social signals, and emerging style terminology to keep its attribute models current. The platform identifies trending language patterns—such as new style descriptors, seasonal terms, or viral product characteristics—and incorporates them into product attribute enrichment. This ensures that product listings remain aligned with how consumers are actively searching, rather than relying on static taxonomies that become outdated as language evolves.
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