GEO Visibility Audit for Generative AI Engines: Understanding How AI Cites You

The Essential:

Generative engines no longer simply rank pages—they compose complete responses and choose which brands deserve to be cited. A GEO audit doesn't measure a ranking position but rather the place your domain occupies in the mind of these models. It reveals for which topics, user profiles, and contexts AI systems recognize you as a trusted source.

The stakes go beyond traditional SEO. It's about observing how ChatGPT, Perplexity, or Gemini connect your site to semantic fields, entities, personas, and concrete user intentions. A solid GEO audit doesn't just look at what you think you're communicating—it examines what AI has actually understood and chosen to reference. On that basis, it proposes a plan to gradually move your brand toward the responses that truly matter.

When an AI Answers Your Customer's Question Instead of You

Imagine a marketing director preparing next year's budget. She opens Perplexity, asks a question about her content strategy, receives a structured answer with actionable insights, and sees three or four brands cited as references. She probably doesn't know exactly why those brands appear, but a reflex takes hold: if the AI cites them, they must carry weight on the subject.

Behind this increasingly ordinary scene lies an implicit audit. The model has decided, after conducting web searches or drawing on internal knowledge, which entities and domains merit appearing in the response and within the context of the user's specific question. This automatic choice shapes how users perceive authority far faster than any positioning report.

A GEO visibility audit makes this mechanism observable and measurable. We no longer ask only whether a site ranks well for a keyword, but whether the AI deems it credible enough to support a recommendation for a given user profile.

Why a GEO Audit Isn't the Same as an SEO Audit

Read a ChatGPT or Perplexity response on a professional topic carefully. Observe it objectively. No page 1, no blue snippet, no CTR. Instead, you see a synthesized argument that assembles multiple sources—sometimes without citing all of them—and attempts to account for the context of the question.

A GEO audit seeks to measure the cognitive preference a model develops for certain brands. It doesn't just ask whether your domain is known, but for which kinds of problems it's spontaneously brought to mind. It examines whether AI associates you with strategic guidance, step-by-step tutorials, product comparisons, or practical user experiences.

Within this logic, the audit isn't limited to an abstract authority score. It observes how the model moves from a domain to a theme, from a theme to a persona, from a persona to an intention, and finally to an explicit or implicit citation. This chain reveals why you appear for some audiences and disappear for others.

Step 1: Let the Domain Speak Before the Brief

According to my methodology, the starting point is deliberately raw: you take the domain—usually via its homepage—and submit it to the model without any context. No keyword lists, no pre-written targeting, no marketing slides. The implicit question is simple: from this signal alone, what do you understand about this site?

This initial reading should surface real business activity, products, services, discourse sophistication, and visible expertise. We identify which structuring entities emerge naturally. A company type, a client category, a molecule, a technology, a territory, a methodological concept: each contributes to building the future AI visibility graph.

A serious GEO audit accepts holding up this mirror to the brand. It compares the official narrative from internal presentations against what the site actually reveals when read by a statistical model trained on billions of texts.

Example prompt for Step 1

You are an AI model tasked with preparing a pre-audit of visibility in generative engines (GEO).

Task:
1. Analyze only the following domain: [URL].
2. From this page, describe:
   - the brand's actual business activity
   - main products / services
   - discourse sophistication level
   - visible expertise signals (client cases, numbers, certifications, etc.)
3. Then list the key named entities you identify:
   - entity type (organization, product, method, concept, location, molecule…)
   - entity name
   - phrase or site section that justifies this entity

Constraints:
- List all URLs you used to build your response
- Don't invent features, offers or evidence that aren't visible.

Step 2: Uncover Invisible Personas

On Google, the search results page already reveals much about user profile, since it's relatively consistent based on who enters the query. In a generative engine, only the person typing the prompt sees the model's response. Each person's use of conversational engines is personal, and no one except the manufacturers has access to this data. To this difficulty we add the fact that the experience is meant to be highly personalized. The model responds based on user memory (built progressively through previous conversations and preferences), prompt formulation, context, requested detail level, and stated constraints. A GEO audit therefore necessarily includes identifying the personas your site attracts or repels—even if the brand has never formally defined these profiles (which is frankly a major oversight; it's mandatory work for 2026).

The goal is to reconstruct as closely as possible the questions each persona type would ask in a generative engine related to your business, so you can understand if your brand appears naturally credible to the AI.

Example prompt for Step 2

You play the role of a GEO analyst.

Context:
Here's an analysis summary of the domain and extracted entities (Step 1):
[PASTE STEP 1 RESULT HERE]

Task:
1. Infer the main personas this site attracts or genuinely targets.
2. For each persona, provide:
   - a fictional first name
   - their precise role (job title + company context)
   - their expertise level on the subject (beginner, intermediate, expert)
   - their typical situation when seeking information like what the site offers
   - the type of result they expect from a generative engine (strategic advice, step-by-step tutorial, benchmark, field insights…)

3. Conclude by noting:
   - which personas seem well-served by the site
   - which potentially important personas don't really find what they're looking for

Constraints:
- Don't produce "generic" personas.
- Base your assessment on tone, vocabulary, and visible expertise signals.

Step 3: Map the Actually-Occupied Semantic Field

Once these profiles are clarified, you must trace the exact semantic perimeter that the brand occupies within content the AI has ingested. You don't limit yourself to obvious keywords. You catalog recurring concepts, named entities, systems, places, ingredients, technologies, and practices.

This work draws a graph. Some nodes are central, connected to many others; others remain peripheral, used rarely but carrying nuance. The clearer this topology is, the better your GEO audit can explain why the model chooses or ignores you.

Sites are often richer than they think. Yet a language model infers nothing from unwritten intentions. It extrapolates only from what is actually expressed and connected. Hence the necessity, during the audit, of identifying entities that serve no SEO purpose but become decisive when the AI seeks a credible explanatory angle.

The final objective of this step is to identify the semantic gap—that is, to compare the most important entities and concepts in your sector against those actually discussed on your site. The more your brand covers the core concepts of your sector, the higher your probability of being recognized as a reference, and thus cited in AI responses.

Example GEO prompt for this step

You are an AI model specialized in semantic analysis oriented toward GEO.

Context:
- Analyzed domain: [DOMAIN URL]
- Summary of extracted entities: [PASTE ENTITY RECAP]
- Main personas: [PASTE PERSONA LIST]

Task:
1. Create a map of the semantic field actually occupied by this domain.
2. Organize the answer in three blocks:
   A. Central concepts (those that recur and structure discourse)
   B. Peripheral but differentiating concepts (rare, more expert, more technical)
   C. Expected in the sector but little or absent from the site

3. For each concept or entity:
   - state its name
   - specify its role (problem, solution, method, context, metric…)
   - give an example of a sentence or section where it appears

Constraints:
- Base yourself only on actual site content.
- Clearly flag semantic "gaps" that seem surprising given the sector.

Step 4: Build a Question Fan-Out by Persona

From this semantic graph and the personas emerges the most structuring phase: constructing the fan-out of questions asked by identified personas. This isn't about generating mechanical keyword variations, but reconstructing the realistic search situations that matter for your business.

You assemble basic discovery questions, purchase scenarios, comparison requests, expert inquiries, diagnostic questions—and also queries your site never addresses. Often these overlooked angles best reveal your blind spots in AI responses.

The question fan-out becomes the audit's reading grid. Each question represents a scene where AI must choose brands to help it respond reliably and contextually. The richer and more coherent your panel of scenes, the more actionable your results.

Example GEO prompt for Step 4

You are tasked with building a query fan-out for a GEO audit.

Context:
- Domain: [DOMAIN URL]
- Personas: [BRIEF PERSONA RECAP]
- Semantic field: [SUMMARY OF CORE AND PERIPHERAL CONCEPTS]

Task:
Generate a table of realistic queries that users would type into a generative engine (ChatGPT, Perplexity, Gemini…) related to this domain.

For each row, provide:
- targeted persona
- expertise level (beginner / intermediate / expert)
- intention type (discovery, comparison, purchase decision, diagnosis, implementation, monitoring…)
- expected response format (step-by-step guide, strategic summary, checklist, benchmark, FAQ…)
- exact query formulation as each persona might write it

Deliberately include:
- queries the site is well-equipped to answer today
- queries the site seems absent or weak on (missing angles)

Constraints:
- Stay credible to your actual sector.
- Vary formulations and intentions.

Step 5: Query Models Like a Reading Panel

Once the priority question list is stabilized (though it's meant to evolve continuously), you move to the testing phase. Queries are sent to multiple models (ideally directly on conversational platforms rather than via API to stay closest to real user context), with an identical protocol. The goal isn't to have a conversation, but to prompt a series of comparable responses.

Note:

There are many ways to conduct this step, and everyone can adapt it to their preferences (like all other steps). A method I particularly favor involves creating a dedicated project per persona when the platform permits (as with ChatGPT or Claude AI, though not currently with Gemini). The idea is to input a system prompt describing all persona characteristics (who they are, their job, constraints, expectations, doubts, etc.) to constrain the machine to respond to our persona, not to us.

For each question, you ask the AIs to detail the sources they mobilize, brands cited, entities highlighted, and sometimes the confidence level associated with these citations. Each response becomes a small window into how the model has mapped the topic and distributed credit among actors.

Differences between models are particularly instructive. Some quickly recognize your domain, others systematically avoid it. These divergences reveal something about the signal you send and how it blends into the broader web.

Example GEO prompt for Step 5

You are a generative engine transparent about the sources you mobilize.

Task:
For the following query:
[INSERT QUERY FROM QUESTION FAN-OUT]

1. Answer the user's question normally.
2. Then, in a second section, explain:
   - which sources or source types you mobilize (sites, content types, author profiles)
   - which brands or domains you might explicitly cite as references
   - for each of these brands, why you consider them relevant here

3. Finally, indicate for the following domain: [AUDITED DOMAIN URL]
   - whether it seems relevant to cite in your response
   - on what precise angle you'd use it (example: case study, practical guide, expert position, hands-on experience…)

Constraints:
- Don't limit yourself to only links you might display.
- Draw also on your internal representation of content (entities, concepts, patterns).

Note:

This prompt is just one way among many others. You could obviously simply type the questions one by one into ChatGPT or another generative engine to stay in a context closest to reality.

Step 6: Read Visibility by Persona, Not Just by Topic

Once all responses are collected, re-segment results through the lens of personas defined earlier. You're no longer looking only at the theme, but the implicit profile of the person the model believes it's answering.

A marketing manager might see your brand appear regularly when structuring overall strategy, while a beginner never encounters it in foundational guides. A technical expert might judge your content credible for architecture questions, but the AI might prefer other sources as soon as operational use cases come up.

AI visibility isn't homogeneous. It follows lines of force linked to how your content speaks, to whom it speaks, and in which situations it proves truly useful. Your GEO audit should identify which persona type the AI recommends you to most and for which question types (organizing all questions from your fan-out by theme gives you several granularity levels when finalizing the audit, allowing you to segment your visibility both by persona and by sub-themes).

Step 7: Build an AI Visibility Score That Actually Matters

From this data, you can develop an AI visibility score that's neither a gimmick nor a simple comfort rating. This score aggregates multiple dimensions: citation frequency, position in lists, diversity of questions won, persona coverage, thematic coverage, convergence or divergence across models.

The point isn't to achieve a flattering score, but to build a useful tool. A domain can be very strong in one ultra-specific segment and nonexistent everywhere else. Better to know it than not know it behind a global index that means nothing.

Example:

Personally, I'm abundantly cited by all AIs when identifying an expert in visibility for generative engines in France, while I'm completely ignored for the same search on SEO consultants—despite practicing that profession for over 10 years.

This score primarily serves to prioritize initiatives. It highlights angles where you can quickly consolidate presence, those requiring repositioning, and those still in play. It transforms a diffuse sense of your AI visibility into concrete landmarks.

Step 8: Transform Your GEO Audit into Strategic Editorial and Product Decisions

A GEO audit matters only if it drives change. Results must translate into decisions about content architecture, editorial prioritization, sometimes even your offer itself.

Three questions typically structure what comes next:

Which topics genuinely strengthen brand authority in the minds of models?
Which user segments are still underserved by your current content?
Which semantic connections are missing so AIs naturally connect your brand to certain critical business intentions?

The answers rarely lead to spectacular revolutions. They drive targeted moves: adding key entities, writing the guide everyone expects, clarifying a fuzzy promise, making a methodology explicit, or articulating differently evidence you already possess but models don't know how to use.

What a Generative Engine Visibility Audit Really Reveals

Accepting a GEO audit means accepting to be seen through the eyes of a system that can't see your history, past efforts, or stated intentions. It observes only what's written, connected, repeated, and clear enough to be reused in a response.

Visibility in generative engines doesn't fall from the sky. It results from well-defined entities, coherent semantic fields, readable positioning, and the ability to address real situations (actual user needs) rather than abstract keywords. Brands investing time to measure this reality and act on it build an advantage ordinary SEO optimization doesn't easily catch up with.

Others keep fighting for clicks while their competitors begin winning citations directly in responses. The GEO visibility audit is designed to help you decide which side of this line you want to occupy over the coming years.