AI Visibility & GEO: get cited by ChatGPT, Gemini and Perplexity

GEO (Generative Engine Optimization) refers to all the practices aimed at optimising a site's or brand's presence in responses generated by language models (LLMs): ChatGPT, Gemini, Perplexity, Claude, Copilot. AEO (Answer Engine Optimization) is the variant focused on direct-answer engines — a term that emerged with featured snippets and has expanded to cover generative AI.

In practice, both terms describe the same reality: how to make an LLM mention your brand, cite your content, or recommend your service when a user asks a question in your domain. The goal is no longer just 'ranking on page one of Google' but 'being in the answer'.

The difference from classical search is structural. Google ranks URLs. An LLM synthesises sources and produces a natural-language response — sometimes without citing any URL at all. You can rank perfectly on Google and be completely absent from AI responses. The reverse is also true, and increasingly common.

The share of searches that result in a click is shrinking every year. Google AI Overviews, Perplexity, ChatGPT with web browsing: a growing fraction of questions gets answered directly inside the search interface, without the user visiting a third-party site. This is 'zero-click' — a phenomenon that already affected 65% of Google searches on mobile in 2023, before the acceleration of generative AI.

For brands, this means two things. First consequence: organic traffic to generic informational content will continue to contract. Second consequence, a positive one: being cited by an LLM as a reference source confers a form of brand authority that Google ranking no longer offers as sharply — users remember the names the AI explicitly mentions.

The most exposed sectors are those where high-value informational search dominates (health, finance, law, SaaS, consulting). But no sector is spared: even a local query — 'best restaurant in Lyon' — is now handled by AIs that synthesise reviews and mentions before responding.

SEO and GEO share a common foundation: technically sound sites, quality content, authority built over time. This base remains necessary. What changes is the end goal and certain optimisation levers.

In SEO, you optimise for ranking: a URL must appear in the results list for a given query. In GEO, you optimise for citation: a piece of information, a brand or an argument must be deemed reliable and 'citable' enough for an LLM to include it in its synthesis. The signals are not identical.

GEO values factual precision, structure (headings, lists, tables), sourced numerical data, expert quotations and brand-entity consistency across sources. It penalises vague, repetitive content with no point of view — exactly the type of content that rapid-growth SEO sometimes encouraged.

Four levers determine the probability of being cited by an LLM. The first is content quality and citability: precise answers to precise questions, with proprietary data points (studies, internal statistics, benchmarks), structured arguments and a visible update date. An LLM prefers a page that answers the question directly over one that circles around it.

The second lever is entity consistency. LLMs build a representation of your brand from all available sources: your site, press mentions, Google Business profiles, listings in sector directories. The more consistent these sources are with one another (name, activity, geography, specialty), the more 'trustworthy' the entity appears to the model.

The third lever is semantic markup: JSON-LD structured data (Organization, Product, FAQPage, HowTo, Article), Open Graph tags, author markup. These signals help LLMs understand who you are, what you do and why your content is authoritative. The fourth lever is external reputation: reviews, mentions in recognised media, editorial backlinks. An LLM gives more weight to a source cited by other trusted sources.

Measuring AI visibility is still a maturing field, but several methods are already operational. The most direct is prompt probing: systematically asking a representative panel of questions about your domain to ChatGPT, Gemini and Perplexity, and recording whether and how your brand appears in the responses. This probe must be renewed regularly — LLMs are updated and their behaviour evolves.

You can refine the measurement by comparing citation frequency against direct competitors: is your brand cited as often, more or less? For which types of questions? With what positioning (primary recommendation, secondary mention, simple reference)?

The metrics to track are: citation rate across the prompt panel, position in the response (first mention vs list mention), sentiment associated with the citation, and presence of the URL as a source when the engine provides references. This data lets you measure the impact of GEO actions you implement.

AudiScale integrates the GEO dimension into its full audit. It regularly probes ChatGPT, Gemini and Perplexity on queries in your sector, measures your citation rate and compares it to that of your direct competitors. This AI score is part of the global visibility score.

When gaps are detected — incomplete structured data, entity inconsistencies across sources, poorly citable content — AudiScale incorporates them into its prioritised action plan. And as with technical SEO, its operator agent can execute certain fixes directly: JSON-LD enrichment, listing updates, brand description harmonisation.

AI visibility is not a topic separate from SEO: it is an additional dimension of the same challenge. AudiScale is designed to handle them together, with the same rigour and the same traceability.