Generative engine optimisation (GEO)
Generative Engine Optimisation (GEO) is the practice of optimising content and brand signals for generative AI platforms such as ChatGPT, Gemini and Perplexity, so the brand appears accurately in AI-generated answers.
Generative engine optimisation focuses on the platforms that generate answers from a language model, such as ChatGPT, Gemini and Perplexity. The aim is for the brand to appear, and appear accurately, in the text those platforms generate.
GEO and AEO overlap heavily, and the industry uses the terms with some variation. The useful distinction is scope: GEO is usually applied to generative AI platforms specifically, while answer engine optimisation covers the broader set of answer surfaces, including AI Overviews and answer features inside traditional search.
In practice the techniques are largely shared: accurate content, a clear entity definition, corroborating sources and machine-readable signals. GEO simply emphasises the generative-platform end of that spectrum.
Because generative platforms compose answers probabilistically, GEO is measured over a set of prompts and across time rather than from a single response.
What it looks like in practice.
A brand that wants to appear in ChatGPT and Perplexity answers for its category publishes accurate, well-sourced content and a clear entity definition, then tracks whether those platforms name and describe it correctly across a set of category prompts.
How Zicy measures it.
Zicy tracks how generative platforms describe a brand across ChatGPT, Gemini, Perplexity, Google AI Overviews and Google AI Mode, and points to the fixes that improve it. See the platform.
Keep reading.
Questions about Generative engine optimisation (GEO).
What is generative engine optimisation?
Generative Engine Optimisation (GEO) is the practice of optimising content and brand signals for generative AI platforms such as ChatGPT, Gemini and Perplexity, so the brand appears accurately in AI-generated answers.
What is the difference between GEO and AEO?
GEO is usually applied to generative AI platforms specifically, such as ChatGPT, Gemini and Perplexity. AEO covers the broader set of answer engines, including AI Overviews and answer features inside traditional search. The techniques overlap heavily.
Do GEO and AEO use different techniques?
Largely the same: accurate content, a clear entity definition, corroborating sources and machine-readable signals. GEO emphasises the generative-platform end of that spectrum.
Why is GEO measured over time?
Generative platforms compose answers probabilistically, so a single response is not representative. GEO is measured across a set of prompts and repeated over time to see a reliable pattern.
Measure it with Zicy.
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