Problem

Retailers and brands often describe products with sparse, legacy, or internally oriented attributes rather than the language shoppers use. That makes products difficult for search engines, recommendation systems, paid-media platforms, and emerging AI shopping agents to understand and match. The economic pain is lower discoverability, weaker onsite search and conversion, smaller orders, less full-margin revenue, and less accurate merchandising and demand forecasting. The clearest use case is enriching a retailer’s catalog or Google Merchant Center feed so products become more discoverable in Google Shopping and onsite search: Lily reports a 28% Google Shopping revenue lift, a 28.3% onsite-revenue lift, and a 21.4% ROAS lift on Meta in controlled tests.

Product / Service

Lily Max is Lily AI’s agentic product-intelligence engine. It ingests a retailer’s catalog, ad-account, storefront, and performance data; uses goal-based AI agents to identify gaps and enrich attributes, phrases, descriptions, metadata, and schema; and republishes improved product intelligence across Google Ads, Meta Ads, onsite search, recommendations, and AI discovery or agentic-commerce surfaces. It is designed to work inside the customer’s existing catalog, feed-management, commerce, and advertising stack rather than requiring a replatform, and pricing is tailored to catalog size, channels, and ad spend.

The product’s differentiator is measurement rather than simply generating content or a visibility score. Lily treats each enrichment as a hypothesis, runs matched-spend A/B tests, holdout tests, or difference-in-differences analyses, and deploys the changes that produce measurable improvements in visibility, conversion, revenue, or ROAS. Customers can begin with a 30-day trial on 500 products, while the platform continuously feeds performance results back into the product model.

Market

Lily AI competes in the overlapping markets of retail AI, product-data enrichment and attribution, ecommerce search and personalization, retail-media optimization, and emerging agentic commerce. Its alternatives include product-enrichment vendors such as Outfindo and experience, search, personalization, and experimentation platforms such as Bloomreach, Dynamic Yield, Monetate, Kameleoon, AB Tasty, Wyng, Voyado, Vue.ai, Coveo Qubit, and Syte. Lily’s positioning is narrower and more cross-channel than a conventional feed manager: it improves the product intelligence carried by feeds and connects that improvement to measured commercial outcomes across paid, onsite, organic, and AI-mediated commerce.

The company is clearly beyond pre-revenue: it has served enterprise customers including Bloomingdale’s, Gap Inc., Macy’s, and thredUP, and its current materials cite M&S, Shiseido, Bombas, A.L.C., Tapestry, and other retail deployments with reported revenue, sell-through, search, and ROAS improvements. Lily closed a $25 million Series B in 2022, and a 2024 report said it raised another $20 million and had reached $62 million in total funding. Third-party revenue estimates are inconsistent, so the evidence supports an operating, funded enterprise software business but not a reliable current revenue figure.