Lily AI

Lily AI bridges the language gap between how customers describe products and how retailers tag them, powering search, recommendations, and conversion lifts.

Overview

Lily AI attacks a quiet conversion killer: product catalogs described in industry language while shoppers search in human language. Its platform analyzes shopper behavior and search patterns to build a customer-centric attribute taxonomy, then enriches product data with the attributes and synonyms shoppers actually use. The result is search that understands intent, recommendations that match preferences, and category pages that surface the right products for each visitor. Because the enrichment happens at the data layer, improvements propagate across every discovery surface — site search, merchandising, paid media, and marketplaces. Retailers in fashion, home, and beauty — categories dense with subjective attributes like fit, style, and mood — see the largest gains, since text matching fails hardest exactly where shoppers struggle most to describe what they want. Its models continually learn from search behavior — including searches that return nothing — refreshing attribute coverage as trends and vocabulary shift seasonally across categories and customer segments. Its measurement framework ties enrichment directly to search conversion and product discovery metrics, giving merchandising teams clear evidence of value that survives scrutiny from finance and executive leadership.

Key Features

• Customer-centric attribute taxonomy built from shopper language
• Catalog enrichment with shopper-vocabulary synonyms and attributes
• Improved site search, recommendations, and category merchandising
• Data-layer enrichment propagating across all discovery surfaces
• Continuous learning from search behavior and zero-result queries
• Seasonal vocabulary adaptation that tracks shifting shopper language

Pricing

Lily AI works through enterprise agreements with retailers, priced by catalog size and modules. Engagements include taxonomy integration, validation measurement, and ongoing optimization against conversion metrics.

Who Is It For

Retailers in attribute-rich categories whose search and discovery underperform, and merchandising teams that suspect their catalog language does not match how customers actually shop. It fits businesses that measure success in search conversion and zero-result reduction rather than vanity engagement metrics.