What is generative engine optimization (GEO)?
GEO, generative search optimization, AI SEO
Generative engine optimization (GEO) is the practice of getting your brand mentioned, recommended and cited in answers from AI engines such as ChatGPT, Perplexity, Gemini and Google AI Overviews. GEO combines content built for retrieval, technical access for AI crawlers, and presence on the third-party sources that AI engines trust when they recommend brands.
GEO aims to get your brand named and cited inside AI answers, not just ranked.
GEO rests on three pillars: retrievable content, technical access and trusted third-party coverage.
Measure GEO with visibility, share of voice, placement, sentiment and citations across engines.
How generative engine optimization works
Generative engine optimization works by making your brand easy for AI engines to find, understand and trust when they build an answer. Many engines use retrieval-augmented generation: they search the web, pick passages, and write a response that cites some of them. GEO targets each step:
Retrieval: AI crawlers can access your pages, and those pages match the questions buyers ask.
Selection: your passages are clear, specific and self-contained, so the engine prefers them.
Trust: reviews, press and comparison articles on other sites back up what you say.
The term comes from a November 2023 research paper by Aggarwal et al. (Princeton and others), which tested how content changes such as adding statistics, quotes and citations affected visibility in generative answers.
GEO vs SEO
GEO and SEO share most foundations, but they optimize for different outcomes. SEO aims to rank a page in a list of links. GEO aims to have your brand named, described accurately and cited inside a single written answer.
Unit of success: SEO tracks rankings and clicks. GEO tracks mentions, position in the answer, sentiment and citations.
Content: SEO rewards a comprehensive page. GEO rewards passages that answer one question on their own.
Off-site signals: SEO relies heavily on backlinks. GEO relies on earned media and mentions, even without a link.
Volatility: AI answers vary by engine, wording, country and run, so GEO needs repeated measurement.
Good SEO helps GEO, since many engines retrieve from search indexes. GEO adds the work of shaping what the answer says. A closely related label is answer engine optimization, and some teams say AI SEO.
How to start with generative engine optimization
Generative engine optimization starts with measurement, because you cannot improve answers you have not seen. A practical first cycle:
Baseline: track 50 to 100 buyer prompts across the engines your market uses and record your AI visibility score.
Diagnose: find prompts where competitors appear and you do not, and note the cited sources.
Fix access: check that AI crawlers can reach and render your key pages.
Improve content: rewrite key pages answer-first, add specific facts, and cover comparison and "best for" questions.
Earn coverage: pitch the publications, review sites and communities that engines cite most.
Re-measure: compare results after a few weeks and repeat.
GEO is ongoing work. Models update, sources change and competitors respond, so the cycle runs continuously.
How KIME supports generative engine optimization
KIME covers the full GEO loop. It measures how ten AI engines mention, rank, describe and cite your brand by country and language, then turns gaps into ranked content, technical and outreach tasks in Actions. Through AWX, agents can execute those tasks, such as drafting content or technical fixes, and the results show up in your tracking as you re-measure.
Frequently asked questions
Is GEO replacing SEO?
GEO is not replacing SEO. Many AI engines retrieve pages from search indexes, so strong SEO still helps you get found. GEO adds new goals on top: being named inside AI answers, being described accurately, and being cited as a source. Most teams run both together, with shared technical and content foundations and separate measurement for AI visibility.
How long does generative engine optimization take to show results?
Generative engine optimization timelines vary. Fixes to engines that search the web in real time, such as Perplexity or ChatGPT search, can show up within weeks once pages are recrawled. Changes to what a model knows from its training data take longer, because they depend on future model updates. Tracking the same prompts over time shows which changes are working.
Who coined the term generative engine optimization?
The term generative engine optimization was introduced in a research paper by Aggarwal and colleagues from Princeton and other institutions, published in November 2023. The paper defined generative engines as systems that write answers from multiple sources and tested methods for improving how often and how prominently content appears in those answers.