GEO and E-Commerce: Your Catalog as an AI Visibility Asset
E-tailers already have the material LLMs love — but in the wrong format. Your catalog as an AI visibility asset.
E-tailers have a GEO advantage nobody tells them about.
They already have the content. Thousands of product pages with descriptions, specs, use cases. Informational material that LLMs love to draw from when answering their users' purchase questions.
The problem: this material is in the wrong format. It was built for Google and for human conversion, not for LLMs. And the gap between the two is wider than most people think.
What LLMs Do with Purchase Queries
"What vacuum cleaner for a 1,000 sq ft home with a dog?" "Which earbuds for working out under $80?" "Best mattress for side sleepers?"
These queries land in ChatGPT, Perplexity, and Gemini in massive volume. Users want recommendations, not lists of results. And LLMs respond with recommendations — often very specific, often with justifications based on product characteristics.
Who gets cited in these recommendations? Rarely the merchants' product pages. Usually comparison sites, buying guides, review articles. Because that content is structured to answer a question, not to sell a product.
The e-tailer loses the direct recommendation and sees their traffic arrive from a third-party comparison site that talks about their product — when they're lucky.
Why Classic Product Pages Miss LLMs
A typical product page is built like this: image carousel, price, prominent buy button, spec bullet points, short description, dynamically loaded customer reviews, similar product section.
For a human on the site, it works. For an LLM scanning the page for information, it's a nearly empty page. The carousel doesn't load. The reviews are in JavaScript. The description is short and generic. The spec bullet points exist but explain nothing about the use case.
Result: the LLM can't build a rich recommendation from this page. It looks elsewhere.
The Markdown Conversion: What It Changes
Converting a product catalog to markdown transforms a format designed for visual rendering into one designed for text processing.
In markdown, the product page changes in nature. The title becomes a precisely named entity with its key attributes. The description becomes a prose paragraph that explains who the product suits and why. Technical specs sit in a cleanly structured table. Use cases are described in natural language. Comparisons with alternatives are stated clearly.
This version isn't visible on the site. It's accessible to LLM crawlers via a dedicated URL or through the llms.txt. It doesn't replace the classic product page — it complements it for a different audience.
The impact on AI visibility can be significant. An LLM that finds a well-structured markdown version of a product page has exactly the material it needs to formulate a precise, sourced recommendation.
The Ten-Criteria Audit
Before rewriting everything, you need to know where the gaps are. An audit against ten LLM readability criteria gives a precise picture of the catalog's state and the right priorities.
These criteria cover: title precision, quality of prose description, presence of explicit use cases, mobilizable numerical data, named entities, review accessibility, technical spec structure, comparative positioning, alignment with llms.txt, and content readability without JavaScript.
Vurto offers this audit across an entire catalog, with per-page scoring and prioritization of which SKUs to tackle first based on commercial importance and LLM visibility potential. The tool then rewrites the pages in real time in markdown according to the identified standards, with validation of the result against each of the ten criteria.
The Product Categories Where GEO Changes the Most
Not all categories face LLMs equally. Some are massively touched by purchase queries directed at generative engines.
Consumer electronics. Home appliances. Tools. Beauty and skincare. Sports equipment. Books and board games. Any category where users need to compare before buying and where technical criteria matter — that's where LLMs get queried.
In these categories, not being cited in AI recommendations means not existing in a growing share of the purchase journey.
Conversely, in impulse-buy or commodity categories, the impact is less direct. The person buying socks or printer paper isn't asking Perplexity for advice.
The Right Way to Start
Not by rewriting the entire catalog at once. Start with the 50 to 100 most commercially important SKUs and check their LLM readability score. Often 20% of SKUs drive 80% of traffic. Those are the ones to tackle first.
Then audit the site's technical structure to verify that these pages — even well rewritten in markdown — are actually accessible to LLM crawlers. Perfect content blocked by robots.txt is perfectly useless content.
GEO for e-commerce isn't a revolution. It's a logical extension of existing SEO and content work. But it requires shifting the reference frame: thinking "LLM readability" alongside "human conversion," and producing formats that serve both.
Vurto analyzes product pages against ten criteria and converts them to structured markdown to maximize their presence in LLM recommendations.