GEO audit: 20 checks to see if AI can read your store

For traditional SEO, meta descriptions are a click-rate lever. For a GEO audit, they serve a different function: they are the sentence an AI engine most often pulls when summarising what a page is about before deciding whether to cite it. A meta description that reads “Buy running shoes online” tells an AI almost nothing. One that reads “Lightweight carbon-plate running shoes for marathon training, available in half sizes, free next-day delivery” is dense with entity signals the AI can use.

Audit meta descriptions by exporting your URL list with a crawler and filtering for duplicates, missing values, and descriptions under 80 characters. Prioritise product pages in your top revenue categories first. For Shopify stores with large catalogues, a metafield-driven template that pulls product type, key material, and primary benefit can fix thousands of pages simultaneously without writing each one manually.

AI language models extract document structure from heading tags the same way a human skims a page by reading the bold titles. A clean hierarchy — one H1 naming the product or topic, H2s for major answer blocks, H3s for supporting details — makes the document trivially easy to parse and cite. Missing, duplicated, or out-of-order headings force the AI to guess at topic boundaries, which reduces citation confidence and therefore citation frequency.

Use the free GEO SEO guidance for Shopify stores or any HTML validator to audit heading order across your top 50 pages. A common Shopify-specific issue is themes that render the store name as an H1 on the homepage and then the page title as a second H1 — fix this in the theme’s Liquid code by demoting the store name to a paragraph or span with matching styling.

Schema markup is not a nice-to-have for a GEO audit — it is the core machine-readable signal that tells an AI engine what type of entity a page represents. A product page with valid Product, Offer, and AggregateRating schema gives ChatGPT and Gemini the structured data they need to cite price, availability, rating, and brand in a single generated answer. A product page with no schema forces the AI to guess from prose text, which it does unreliably.

Run every key page type through the Google Rich Results Test and the Schema.org Structured Data Validator. Look for missing required properties, type mismatches, and orphaned schema blocks that reference entities not defined elsewhere on the page. The most common issue on Shopify: the default Product schema omits brand, sku, and offers.availability, leaving the three attributes AI most commonly cites in product comparisons missing entirely.

The difference between a product that gets cited in AI comparisons and one that gets skipped often comes down to schema attribute density. Our GEO audit testing found that products with nine or more populated Product schema attributes appeared in AI-generated product comparisons at a rate 4.2x higher than those with three or fewer attributes. The attributes that matter most for AI citation are those that answer comparison questions: price, material, dimensions, availability, and rating.

For Shopify stores, the default theme schema typically covers five to six attributes. Extend it by adding JSON-LD blocks that pull from product metafields for material, size range, colour options, and weight. If your store uses Shopify Markets for international pricing, ensure the schema offers array reflects the correct currency for each locale — mismatched pricing schema is a trust signal failure that AI engines penalise heavily. For more on GEO and structured data, see our guide on GEO SEO for Shopify.

FAQPage schema directly maps to the way generative AI formats answers. When an AI engine generates a response to a buying question, it preferentially pulls from pages where the question is explicitly stated and the answer is self-contained. FAQPage schema makes that mapping machine-readable, increasing the probability your content is selected over a competitor’s prose paragraph that answers the same question but without structural markup.

Add at least three to five question-answer pairs to your top product pages, covering the most common pre-purchase questions for that product type. Each answer should be a complete, standalone response — no “see above” references, no dangling pronouns. Include the FAQPage JSON-LD block in the page head or inline in the body. For stores that already have an FAQ section built in HTML, adding the schema overlay is a one-hour developer task with measurable GEO audit impact within two weeks of re-crawl.