GEO 101: How AI Engines See Your Store
If you have spent the last few years tuning title tags, chasing backlinks, and watching your Google rankings, you already know how SEO works. A search engine crawls your pages, indexes them, and decides where to rank you. Your job is to make that ranking go up.
Generative Engine Optimization — GEO — asks a different question entirely.
When a buyer types "best running shoes for flat feet" into ChatGPT or asks Perplexity to recommend a standing desk, no ranked list of blue links appears. Instead, the AI names a handful of brands, explains why it likes them, and sometimes links directly to a product page. The buyer picks from that short list. Your store is either on it or it is not.
SEO tells you where you rank on Google. GEO tells you whether AI engines understand your catalog well enough to recommend it.
The gap most merchants do not know exists
Traditional SEO tools are built around keywords and links. They answer: "How visible am I to Google's crawlers?" That is a solved problem with mature tooling.
AI engines read your store differently. They do not rank pages against a query — they synthesize an answer from everything they know about your product category, then decide which stores to name. The inputs that drive that decision are structural: how clearly your product schema describes variants, how precisely your headings state product facts, whether your structured data gives an AI the confidence to attribute a claim to your store by name.
A store can rank on page one of Google and be invisible to every AI engine. The reverse is also true. They are measuring different things.
The four engines that matter
Surfacely probes four AI engines because each surfaces products differently and draws on different data.
ChatGPT (GPT-4 family, OpenAI) is the highest-volume AI assistant for product questions. When ChatGPT answers a product query, it tends to name stores it can cite with confidence — stores whose structured data is clean enough that the model can attribute specific claims (price range, material, use-case) back to a source.
Claude (Anthropic) weights factual precision heavily. A product page that states clear, verifiable facts — dimensions, materials, compatibility — performs better in Claude's answers than one that uses marketing superlatives without specifics.
Perplexity is built around citations. Every answer Perplexity generates shows the user which sources it drew on. If your store appears in a Perplexity answer, the user can see your domain name next to the claim. If your schema is ambiguous or your product facts are buried in unstructured prose, Perplexity is less likely to surface you.
Gemini (Google) is relevant both as a standalone assistant and as the engine behind Google AI Overviews. Gemini has access to Google's index, which means your existing SEO signals carry some weight — but structured data and AI-readable markup still determine how confidently the model can extract and restate your product facts.
No single engine dominates. A store optimized for one may be invisible on another. Auditing across all four gives you a realistic picture of your AI share-of-voice.
What "AI sees your store" actually looks like
Here is a concrete example of the difference between a store that AI engines cite and one they skip.
Imagine two stores selling cast-iron cookware. Store A has product pages with complete JSON-LD schema: product name, brand, description, price, material, weight, and a review aggregate. The heading hierarchy is clean. The copy states specific facts: "4.5 kg pre-seasoned skillet, compatible with induction, oven-safe to 260°C."
Store B sells the same skillet. The product page has a compelling lifestyle photo and marketing copy that reads "our most popular pan, loved by home cooks everywhere." No schema. No stated weight or temperature rating. The heading is the brand name, not the product.
When a user asks ChatGPT "what is a good induction-compatible cast-iron skillet," ChatGPT is much more likely to name Store A. Not because Store A outranks Store B on Google — but because the model can extract a specific, attributable fact (induction-compatible, 260°C oven-safe) and cite it with confidence. Store B's page gives the model nothing concrete to work with.
The same pattern plays out on Perplexity, where citations are visible to the user. Store A's domain appears as a source. Store B does not. The buyer never sees Store B.
Share-of-voice is the metric that captures this at scale: across a set of category prompts ("best cast-iron skillets," "induction cookware for beginners," "durable pans under $100"), what percentage of AI answers mention your store? A store with a share-of-voice of 0% on those prompts is invisible to that buyer segment, regardless of its Google rankings.
How a GEO audit works
A Surfacely audit covers three layers.
Structural analysis crawls your public-facing pages — product pages, category pages, the homepage — and checks schema coverage, JSON-LD validity, heading hierarchy, meta completeness, and whether your store has an llms.txt file signaling AI-crawler intent. This layer tells you what the machines can and cannot extract from your store right now.
Live LLM probing goes further. Surfacely queries ChatGPT, Claude, Perplexity, and Gemini with category-level prompts relevant to your store and records the verbatim responses: which stores they name, what claims they attribute, and the sentiment of those attributions. This is your actual AI share-of-voice — not an estimate, but a live read of what each engine says about your category today.
Prescriptive fixes translate the findings into a ranked list of actions. Each fix specifies the affected pages, the exact change to make (a schema field to add, a heading to rewrite, a product fact to surface), and the expected score lift. The fixes are copy-paste ready — no interpretation required.
The structural analysis runs in in minutes and does not require a login.
Why structure beats tricks
GEO optimization is not a new form of keyword stuffing. It is closer to quality control on the data layer of your store.
AI engines reward stores that state facts clearly, structure data consistently, and give crawlers enough signal to attribute claims with confidence. Stores that try to game AI answers with keyword-dense prose tend to underperform against stores that simply describe their products accurately in machine-readable markup.
That is good news for merchants who invest in their catalog. Clean schema, accurate variant data, and clear product facts are not just GEO signals — they are the same things that make a product page useful to a human buyer.
The shift in search behavior is already underway. Buyers who use AI assistants for product research are not going back to ten blue links. Your AI visibility is a distribution channel, and right now most stores have no idea what their share of it looks like.
See how AI sees your store. The audit is free and typically under 5 minutes.
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