What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the practice of optimizing content to be surfaced and cited by generative AI engines — ChatGPT, Gemini, Perplexity and AI Overviews — when they generate answers.
Also known as: GEO
GEO and AEO are used almost interchangeably: both describe making your brand the one generative AI names. GEO leans slightly more on the generative side — the model composing an answer from retrieved and trained knowledge — while AEO frames it around the answer the user receives. In day-to-day work they are the same discipline.
GEO matters because a growing share of category research now happens inside an AI chat rather than a search results page. If the model does not surface you when it generates its answer, you are invisible to that buyer — and unlike a low Google rank, you cannot see it happening without measuring it directly.
A GEO programme typically audits current citation rates across engines, identifies the prompts and topics where you are absent, and builds the entity authority and source coverage that make generative engines include you.
Is GEO different from AEO?
Barely. GEO emphasizes the generative model composing an answer; AEO emphasizes the answer the user gets. Most teams treat them as the same practice of getting named by AI.
Which engines does GEO target?
The generative answer surfaces: ChatGPT, Google Gemini and AI Overviews, Perplexity, and Microsoft Copilot, among others.
AI Brand Visibility & Citations — ChatGPT, Perplexity, Gemini measures your visibility, share of voice and citation rate across ChatGPT, Perplexity and Google AI Overviews — no code, pay per run.
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