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Lily AI

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Transform e-commerce with AI-driven emotional customer insights.

paid· from $200/mo· for mid-market

Lily AI enriches retailer product catalogs with consumer-language attributes — the adjectives, occasions, and style descriptors shoppers actually type into search bars. It ingests product data and images, then outputs hundreds of tags per SKU that feed site search, SEO pages, paid search, and recommendation engines. The buyer is typically a merchandising or digital leader at a mid-to-large apparel, beauty, or home retailer losing revenue to attribute gaps between how shoppers describe products and how brands catalog them.

> pick this if

Pick this if you're a merchandising or digital leader at a mid-to-large apparel, beauty, or home retailer whose site search, SEO, and Shopping feeds are bottlenecked by thin, manufacturer-style product attributes.

> look elsewhere if

Look elsewhere if you sell in categories Lily doesn't specialize in (electronics, industrial, grocery), run a catalog under ~5,000 SKUs where manual tagging is cheaper, or need an out-of-the-box site search engine rather than an enrichment layer feeding one.

> Lily AI is used by

  • LilyAI
  • Bloomingdales

> Lily AI is built for

  • DTC apparel & fashion
  • DTC beauty & cosmetics
  • home & furniture

> what it does for ecommerce

  • Generates consumer-vocabulary product attributes from catalog images and copy
  • Feeds enriched tags into site search, SEO, and paid channels
  • Covers apparel, beauty, home, and softlines vertical taxonomies
  • Reports attribute coverage gaps against actual shopper query logs
  • Integrates with Google Shopping feeds and onsite search platforms

> how you'd use it

  • Mid-market apparel retailer, $80M–$300M GMV, 8–15 person digital merchandising team
    Site search logs show 30%+ zero-result or low-CTR queries on descriptors like 'flowy', 'cottagecore', 'work-appropriate' that the PIM never tagged; Lily ingests the catalog and backfills consumer-vocabulary attributes into Algolia or Constructor
    → Double-digit lift in search conversion and recovery of long-tail queries that previously bounced, without hiring additional copywriters or taxonomists
  • Beauty or home brand, $50M–$500M GMV, 2–4 person paid search team running Google Shopping at scale
    Shopping feed titles and attributes are generic manufacturer copy; Lily rewrites feed attributes with shopper-language descriptors (occasion, finish, mood) before syndication to Google and Meta
    → Improved query match rates and lower CPA on Shopping, with measurable impressions gained on descriptor-heavy searches competitors aren't bidding on
  • Enterprise softlines retailer, $1B+ GMV, 20+ person ecommerce and SEO org
    SEO team needs to spin up hundreds of attribute-based landing pages (e.g. 'midi dresses for petite') but lacks structured tags at SKU level; Lily's enrichment feeds the facet layer and category page generator
    → Landing page coverage expands 3–5x in a quarter, driving incremental non-brand organic traffic without manual catalog work

> Lily AI use cases

> Lily AI key features

  • Emotion Intelligence Technology
  • Customer Insight Dashboard
  • Real-Time Analytics
  • Personalization Engine

> compliance & trust

  • GDPR
  • CCPA

> how lily ai compares

bidirectional editorial

> alternatives to Lily AI in our index

by shared use-case

> Lily AI pairs well with