What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the practice of structuring a brand's content and off-site signals so that AI systems like ChatGPT, Perplexity, Gemini and Google AI Overviews cite and recommend it when generating answers to buyer questions.

How it works

Traditional search returns a ranked list of links and lets the user choose. Generative engines return a single synthesised answer that names a handful of sources. GEO is the work of becoming one of those named sources: making content retrievable, quotable, and trusted by the retrieval systems that feed the model.

In practice it spans three layers. The first is making pages machine-legible, through clean structure, schema markup, and answer-lead formatting. The second is making claims extractable, through self-contained definitions, statistics, and proof-pairing. The third is making the brand trusted off-site, through unlinked mentions and authority signals. It is measured in citations, not rankings.

GEO vs SEO vs AEO

SEO optimises to rank a link in a results page. GEO optimises to be named inside a generated answer. AEO (Answer Engine Optimization) is often used interchangeably with GEO; where a distinction is drawn, AEO refers narrowly to winning direct-answer features, while GEO covers the full system of being cited across generative engines.

Why it matters for B2B

B2B buyers increasingly ask an AI assistant which vendor to use, then act on the names it returns. In rawmktg's analysis, 73% of B2B procurement managers already use ChatGPT, Claude, or Perplexity for vendor discovery. The vendors named in the answer get the shortlist. The rest get no second look.