What is GEO? Generative engine optimization, explained

By Rolf Kassel · Updated 16 October 2026

Generative engine optimization (GEO) is the practice of making a company more likely to be named and cited in the answers AI assistants such as ChatGPT, Gemini, Perplexity and Google AI Overviews give to buyer questions. The outcome it improves is AI search visibility: how often and how prominently AI assistants name and cite a company when buyers ask for vendors. Visibility is measured by running buyer-style questions written for your category several times and counting the mentions, positions and citations in the real AI answers.

The term comes from Aggarwal et al., 'GEO: Generative Engine Optimization' (2023).

Key facts
  • GEO is the practice. AI search visibility is the outcome it improves.
  • AI answers usually name a handful of vendors; being absent means being absent from the shortlist.
  • Engines build answers from sources they retrieve (review sites, communities, comparison articles, documentation) or from what they learned in training.
  • Answers vary between runs, so visibility is a rate, not a single answer.
  • It is measured with a fixed prompt set, repeated monthly, and summarised as a score, such as Namedra's AI Visibility Score.

Why it matters for B2B software

Buyers use AI assistants to build the first shortlist. A prompt like "best compliance automation software for a 200-person fintech" returns a few names with reasons. Vendors on that list get the demo request; the rest do not learn the deal existed. Unlike a search results page, there is no list of ten links to scroll through. The gaps are large. In our October 2026 compliance automation index, Vanta was named in 22 of 27 scored AI answers (81%), Drata in 22 (81%) and Thoropass in 7 (26%). Of the 15 tracked vendors, 10 were named in under a quarter of answers. Google AI Overviews gave 3 of the scored answers; the rest came from Gemini and GPT-OSS 20B answering without web search. Google's other answers (9) had their vendor lists removed by our data provider, so they contributed sources only.

What determines whether you are named

  1. Retrieval. With web search on, the engine fetches pages for the query. If the pages that rank for "best X tools" do not list you, you are rarely named.
  2. Citations. Engines favour sources they can cite: G2, Capterra, Reddit, comparison blogs, your docs. Presence on those sources matters more than your homepage. In our index, the domains Google AI Overviews retrieved most were g2.com, aikido.dev, complyjet.com, reddit.com and sprinto.com.
  3. Entity confidence. Consistent name, description and structured data across the web let the model connect "Acme" to a category. We expect renamed companies and inconsistent descriptions to be under-counted.
  4. Extractable content. Pages with direct answers, tables and definitions are quoted; pages built from images and vague copy are not. Aggarwal et al. tested this. In their experiments, adding citations, quotations and statistics to a source raised its visibility in generative engine answers by up to about 40%.
  5. Training data. For models answering without web search, what was written about you before the training cutoff. Slow to change, but it is why consistent coverage compounds. In our index, most scored answers came from models answering without web search.

Engine by engine: how ChatGPT chooses which vendors to recommend and how to get cited by Perplexity.

GEO, AEO and SEO

Generative engine optimization (GEO)
Generative engine optimization (GEO) is the practice of making a company more likely to be named and cited in the answers AI assistants such as ChatGPT, Gemini, Perplexity and Google AI Overviews give to buyer questions. Most of the work is earning citations on the sources engines retrieve from.
Answer engine optimization (AEO)
Answer engine optimization (AEO) is the older name for the same idea, first used for featured snippets and voice answers; today it is mostly used interchangeably with GEO. When people separate the two, AEO usually means the on-site work: direct answers first, definitions, structured data and consistent facts.
Search engine optimization (SEO)
Earning rankings on a results page. Some groundwork overlaps with GEO: crawlable pages, structured data and clear positioning. Most AI-specific work is different: it happens on third-party sources and is measured in mentions and citations, not rankings.
AI search visibility
The outcome GEO improves: how often and how prominently AI assistants name and cite a company when buyers ask for vendors.

Side-by-side detail: GEO vs SEO.

How to measure it

Write 20 to 30 prompts that match how your buyers ask. Run each on every engine three times. Record whether you and each competitor are named, in what order, with which sentence, and which URLs are cited. Compute mention rate, share of voice, average position and citation rate, then the score. Repeat monthly with the same prompts. A do-it-yourself version is in our DIY checklist. Our AI Visibility Audit runs 30 buyer prompts, each 3 times on every engine we track (Google AI Overviews, Gemini and OpenAI's open-weight GPT-OSS 20B model), and our free check runs 10 buyer prompts on the same engines and returns a one-page snapshot within one business day.

How to improve it

Publish the comparison and alternatives pages the prompts ask for. Get fresh reviews on the review sites engines cite. Answer in the community threads engines cite. Pitch the authors of cited listicles. Use one name and one description everywhere, with Organization and Product schema. Publish original data worth citing. Then measure again. Our estimate: first changes 4 to 8 weeks after a new source is published and indexed. We have not yet measured this on a client, and answers drawn from training data change only with new model versions. Our FAQ has more on timelines.

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