Enrich catalog metadata
Adds detailed, customer-centered attributes at SKU level
Marketing
E-commerce & Selling
Analytics & Data
Lily AI
Lily AI enriches retail product catalogs with customer-centered attributes, helping enterprise brands improve product discovery, recommendations, and marketing feeds.
Runs on
Web
Available on
Web
Built for
Enterprise
Marketing
Overview
What is Lily AI?
Where Lily fits
Lily AI sits between a retailer’s catalog and its search, recommendation, advertising, and merchandising systems. Its models translate product details into granular, customer-oriented attributes such as style, occasion, fit, or aesthetic. The enriched feed gives commerce tools intent signals beyond merchant taxonomy.
Use it or skip it
Great for: retailers and brands with large, changing assortments or inconsistent supplier data. Skip it when: the catalog is small, taxonomy is already clean, or the team cannot support feed mapping and measurement.
Buying and rollout
Lily AI uses custom enterprise pricing rather than public self-service tiers. Scope depends on catalog size, selected use cases, and integrations. Value comes from sending enriched attributes into search, recommendations, SEO, or campaigns and validating lift through tests. It is not a storefront or general-purpose content generator.
Capabilities
Key features
Translate shopper intent
Maps natural shopping language to relevant product characteristics
Improve onsite discovery
Supplies richer signals to search and recommendation systems
Feed optimization
Refines product data for organic and paid shopping channels
Analyze assortment context
Organizes products through a retail-specific intent taxonomy
Keep exploring
Teams also considered
When shoppers cannot put a product into words, Syte uses visual AI to connect images and intent with relevant items across a retailer’s catalog.
Shoppers often know what an item looks like but not the words to search for. ViSenze gives retailers visual search, catalog tagging, and recommendation APIs suited to image-heavy product catalogs.
Poor retail search buries relevant stock and costs conversions; Constructor uses shopper behavior to improve product discovery. It is especially good at ranking large catalogs around purchases rather than simple keyword matches.
Still weighing your options?
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