SEO · AEO · Agentic
Be the product AI recommends.
Optimize the feeds, pages, and structured data AI systems read across three tiers, SEO, AEO, and ACO, so answer engines and shopping agents can understand, cite, recommend, and select your products. We improve readiness and input quality, not guaranteed placement.
#1
AI recommended product
A leading global beauty house, surfaced as the top recommendation across major answer engines.
The offering
Three tiers of AI discovery: SEO, AEO, and ACO.
Discovery is moving from a list of links to AI answers, to agents that choose and buy. Each tier runs on the same product intelligence layer, enriched once, distributed everywhere. The deeper the tier, the more readable a machine-readable catalog becomes, and the fewer brands are ready for it.
Tier 1 · SEO
Search Engine Optimization
Traditional organic search. Still the baseline, and still the largest share of product discovery today.
Tier 2 · AEO
Answer Engine Optimization
Visibility in answer engines, where being cited matters as much as ranking: ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews and AI Mode.
Tier 3 · ACO
Agentic Commerce Optimization
Visibility where an AI agent researches, compares, and buys on the shopper's behalf. The newest and deepest tier of discovery.
One enrichment layer feeds all three tiers. The same structured product data that strengthens organic SEO also makes you readable to answer engines and selectable by agents. On AEO and ACO we improve readiness and input quality. The platforms and agents control final placement, citation, and selection.
What Lily optimizes
Two layers AI reads: the feed and the page.
Getting cited and recommended takes more than a clean feed. AI systems read two layers: the feed you send out, and the live page they crawl. Lily Max optimizes both, and keeps them in sync.
Feed layer · What you send out
Feed attributes
Titles, descriptions, and structured product attributes, enriched in the consumer language engines parse, then synced to the OpenAI commerce feed, Perplexity merchant data, and Google product inputs where available.
Category & product type
Precise classification, several levels deep, so engines place each product in the right part of the commerce graph.
Conversational & Q&A attributes
Question & answer product data, including Google's conversational attributes, so engines can answer shopper questions with your products.
Page layer · What crawlers read on your site
Metadata
Meta title, meta description, and image alt text, written for machine legibility and for the snippet an engine shows.
Schema & structured data
Product, FAQ, and Breadcrumb schema, the structured data crawlers parse first.
Crawlable PDP copy
The on-page description and attributes a bot reads, kept consistent with the feed so the page and the feed never disagree.
What it impacts
Visibility where AI does the choosing.
Across answer engines and shopping agents, the same enriched product intelligence improves how you show up.
AI answer visibility
Eligibility to appear in AI Overviews, AI Mode, and conversational results.
Product citations
Being referenced by name when an engine answers a shopping question.
Recommendations
Surfacing in ChatGPT, Gemini, and Perplexity product recommendations.
Product comparison
Showing up accurately when an engine compares options side by side.
Agentic shopping readiness
Being legible to shopping agents that select and check out on a shopper's behalf.
AI-assisted selection
Making the consideration set when an agent narrows to a final pick.
One enrichment layer feeds all three tiers. The same structured product data that strengthens organic SEO also makes you readable to answer engines and selectable by agents. On AEO and ACO we improve readiness and input quality. The platforms and agents control final placement, citation, and selection.
Agentic commerce
Commerce is moving from people browsing to agents selecting and buying, through ChatGPT Shopping and emerging agentic checkout protocols. The product data that is legible to those agents decides whether you are in the consideration set when an agent makes the pick.
Operational pace
Google ships. Lily shipped.
When Google introduced Conversational Attributes in Merchant Center, a new product-data type designed to make catalogs legible to AI Mode, Gemini, and Business Agent, Lily Max shipped support for all six attributes within days.
The window between Google announcing a new AI-surface capability and your team actually shipping it determines whether you show up in the next wave of AI commerce, or you don't. Lily Max is built so that window is days, not quarters. A catalog that's always scored, content that's always testable, a pipeline that's always ready to ship.
The six conversational attributes
The loop on onsite
Your catalog, continuously tested.
The optimization loop, applied to the product data behind your PDPs, search, and facets.
01
Gap detected
Agents find thin PDP copy, missing attributes, and the gaps that make onsite search and filters miss relevant products.
02
Enrichment generated
Richer product descriptions, attributes, and facet data, written for your shoppers and your search engine.
03
Experiment launched
Enriched records go live as a controlled test on onsite search and PDPs, against a control.
04
Lift measured · Next step recommended
Revenue and search relevance are measured against the control, and the system recommends the next enrichment.
05
Scaled to full catalog
Winning enrichments roll across the catalog, and the learning compounds across customers.
Proof
Measured against a control. On your own site.
Partners use the support to bring Lily Max to clients confidently.
AI recommendation
#1
A leading global beauty house, surfaced as the top recommended product across major answer engines.
LLM recommendation analysis across ChatGPT, Gemini, and Perplexity; external coverage cited in launch materials.
How we state it
Readiness, not promises
On AI surfaces we state impact as readiness and input quality. The platform controls placement; we control how complete and understandable your product data is.
See the full standard of evidence on the Methodology page.
Get started
See how AI-ready your catalog is.
Bring a set of products and the engines that matter to you. We will scope where your data is eligible to AI, and how we would measure readiness.
Frequently asked questions
What is agentic commerce?
Agentic commerce is shopping where AI agents research, compare, and buy on a shopper's behalf. Instead of browsing your site, the shopper asks an assistant, and the agent reads product data to decide what to surface.
How does Lily Max help my products show up in ChatGPT, Gemini, and Perplexity?
Lily Max enriches your feeds, schema, and bot-facing content so answer engines can understand and recommend your products. It improves readiness and input quality; it does not guarantee placement, ranking, or citation on any AI surface.
What is AI discovery?
AI discovery is when shoppers find products through AI experiences like AI Overviews, AI Mode, and answer engines rather than a traditional search results page. Your products show up only if AI systems can read and understand your catalog.
Why can't AI recommend my products today?
Most catalogs are written for keywords and human shoppers, not machines, so key attributes sit in prose or images an AI can't parse. Lily Max structures that data into machine-readable signals agents can actually use.
What is agent-ready product data?
Agent-ready product data is structured, machine-readable information, schema, attributes, and clear descriptions, that AI agents and answer engines can interpret. Lily Max generates and maintains that layer so your products are legible wherever AI sells.
Does Lily Max guarantee my products appear in AI search results?
No. No tool can guarantee placement, ranking, or citation on an AI surface. Lily Max improves the readiness and quality of the product data those systems read, which is the part you can actually control.
How is this different from traditional SEO?
SEO optimizes pages to rank in a list of links. AI discovery optimizes structured product data so answer engines and agents can understand, compare, and recommend your products directly, often without a results page at all.
Can I measure revenue from AI-driven discovery?
Yes. Lily Max tracks how AI-driven discovery and agentic surfaces contribute to revenue, so you can see the impact of product intelligence beyond traditional paid and organic channels.
Which AI surfaces and agents does Lily Max support?
Lily Max works toward answer engines and shopping agents such as AI Overviews, AI Mode, ChatGPT, Gemini, and Perplexity, by enriching the feeds, schema, and bot-facing content those systems read.
How do I get my catalog ready for AI shopping agents?
Start by scoring your catalog to find where product data is missing or unreadable to machines, then enrich those attributes and structured signals. Lily Max runs that diagnosis and shows the highest-impact gaps first.