Building Lily Max: the engine behind every Lily product
Lily Max started with a simple conviction: product intelligence should be a living system, not a one-time enrichment project. Here's how we built it.
One model of products and shoppers
Everything begins with a unified model of your catalog and the demand around it. That model is what every downstream channel, agentic, Google, Meta, onsite, draws from, so improvements compound instead of fragmenting.
Test, deploy, learn
We treat every enrichment as a hypothesis. Lily Max tests which signals lift performance, deploys what wins, and feeds the outcome back into the model. The system gets smarter with every cycle.

See Lily in action
Book a personalized demo and see how Lily can grow your retail revenue.
Related Blogs
The most expensive thing in retail right now
Brands are pouring attention into a future that isn't generating revenue yet, while the surfaces actually producing revenue today get treated like settled infrastructure. A preview of my CommerceNext session with Ken Pilot and Noam Paransky.
By Purva Gupta
Google says conversational attributes are optional. History says otherwise.
Mobile-friendly, page speed, structured data: Google introduced each as optional, and each became the price of entry. Conversational attributes, its new AI-shopping feed fields, look like the next one.
By Purva Gupta
You don't have an agency problem. You have an input problem.
A CMO fired three paid agencies in two years and decided you can't find a good one anymore. The real problem was upstream: the old agency edge has been commoditized, and the leverage has moved to the inputs only the brand controls.
By Purva Gupta