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GEO · 11 min

Generative Engine Optimization: how to get cited by ChatGPT, Claude, and Perplexity

Search is splitting into two motions — the blue-link index and the answer engine. This is the field guide we use to make a company the source an LLM quotes, not the one it skips.

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For twenty years, being found meant ranking on a results page. That motion is not going away, but a second one has opened beside it: the answer engine. When a buyer asks ChatGPT, Claude, or Perplexity for a recommendation, there is no page of ten blue links to climb. There is one answer, and either you are cited inside it or you are invisible.

Generative Engine Optimization is the discipline of being the source those systems quote. It is not keyword stuffing in a new costume. It is structured, verifiable, machine-legible content that an LLM can lift with confidence — clear claims, named entities, real numbers, and the kind of unambiguous phrasing a model will reach for when it has to be right.

We start every GEO engagement the same way: a citation audit. We run your category's real buying questions through the major assistants and measure where you appear, where a competitor appears instead, and where the answer is simply wrong. That score is the baseline. Everything after it is closing the gaps that are cheapest to move first.

GEO · FAQ

Questions this raises

What is generative engine optimization (GEO)?

Generative engine optimization (GEO) is the practice of structuring your content and entity data so AI answer engines — ChatGPT, Claude, Perplexity, and Google AI Overviews — cite your company when buyers ask for a recommendation. Unlike traditional SEO, which competes for blue-link rankings, GEO competes to be the source quoted inside a single generated answer.

How is GEO different from SEO?

SEO optimizes for ranking on a results page of ten links; GEO optimizes for being the one source an answer engine quotes when there is no list to climb. GEO leans on machine-legible structure — clear claims, named entities, real numbers, schema, and unambiguous phrasing a model will reach for when it has to be correct.

How do you measure GEO results?

By citation rate: you run your category's real buying questions through the major assistants and measure how often you are cited, how often a competitor is cited instead, and where the answer is simply wrong. That baseline score is what every later change is measured against.

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