What is GEO (Generative Engine Optimization)?
Led growth at cnvrg.io before its acquisition by Intel.
What is GEO?
GEO (Generative Engine Optimization) is work on content, access, identity and measurement for discovery in AI-search answers. A brand mention names a company; a citation links to a supporting source. Neither is guaranteed by the implementation.
How GEO differs from SEO
SEO and AI-search work can share readable pages, useful content, internal links and accurate facts. AI-answer observation adds the recorded question, engine, mode, answer and cited sources. Different systems can show different results; a universal separate set of citation weights is not established here.
SparkToro clickstream research found that roughly 60% of US Google searches ended without a click in 2024, in that study, without establishing an early-2026 rate (SparkToro, 2024). Gartner separately projects traditional search engine volume will drop 25% by 2026 as buyers shift to AI assistants (Gartner, 2024). This is why GEO matters now.
The 5 pillars of GEO
- Crawlable content — Key pages served as readable HTML to the intended search retrieval systems. Separate OAI-SearchBot and other search access from GPTBot or ClaudeBot training permissions and user-requested fetches; allow each only under the intended policy.
- Third-party citations – Mentions of your brand on the sites AI engines already cite in your category: trade press, review sites, communities, podcasts.
- Entity description Accurate visible identity and applicable schema, with appropriate sameAs references; correct AI identification remains a separately observed result.
- Answer-first content – TL;DR blocks, FAQ structure, direct quotes that LLMs can extract.
- Citation magnets – Original data, methodology, leaderboards that LLMs preferentially cite.
Where llms.txt fits: low priority, a nice-to-have. Ahrefs checked 137,210 domains and found 97% of llms.txt files got zero requests in May 2026. In the sampled May 2026 logs, named AI bots made zero observed requests for missing llms.txt paths; that does not establish a universal future behavior (Ahrefs, 2026). Ship it after the pillars above, not before.
robots.txt: the file is a request to crawlers, and it cannot lock them out. OpenAI notes that its robots.txt rules may not cover ChatGPT-User fetches a user initiates (OpenAI); Perplexity says its user-triggered fetcher generally ignores them (Perplexity); and after Perplexity's declared bot was blocked, Cloudflare caught it crawling under an undeclared browser user agent (Cloudflare, 2025). Name matching is narrow too: a crawler follows just the most specific group whose name matches it (Google Search Central). A bare User-agent: * block therefore catches every bot you left out, including those you intended to allow. Give each bot family its own rules: training (GPTBot), search (OAI-SearchBot, PerplexityBot) and user fetchers (ChatGPT-User, Perplexity-User).
How to measure GEO results
A practical starting sample is a defined set of buyer questions and relevant engines. Record mode, date, market, language, answer and linked sources, with repeated runs and failed runs reported separately. Agree the review cadence from the decision; 10–30 questions and day 90 are examples, not an engine standard.
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Why GEO changed how I think about search
Let me answer the question directly. GEO stands for Generative Engine Optimization. It is the practice of structuring your content so AI answer engines like ChatGPT, Perplexity, Gemini, and Google AI Overviews cite you inside their answers. Old search sent people a list of ten blue links. New search reads the sources, writes one answer, and names a few brands. So what is GEO, in plain terms? It is the work of becoming one of the brands that gets named.
The shift is structural, not cosmetic. When someone asks an AI “who is the best fractional growth lead for a B2B SaaS,” the model does not show a ranked page. It writes a paragraph and pulls two or three sources into it. If you are not in that paragraph, you do not exist for that buyer. That is the core reason what is GEO is the question I get most from founders right now. They watched their organic clicks flatten while AI answers ate the top of the funnel.
Here is how GEO differs from SEO, because the two get blurred constantly. SEO optimizes for a crawler that ranks pages. GEO optimizes for a model that synthesizes answers. SEO rewards keywords, backlinks, and page speed. GEO rewards clear claims, cited statistics, direct definitions, and content a model can lift cleanly without misreading it. The research backs this. The original GEO study from Princeton and Georgia Tech, published on the arXiv preprint server, found that adding statistics, quotations, and cited sources lifted visibility in generative answers by up to 40 percent. Keyword stuffing did almost nothing.
So what is GEO doing differently at the page level? It front-loads the answer. AI engines reward content that states the conclusion in the first two sentences, then supports it. They reward a clean question-and-answer structure, because that maps to how people prompt. They reward specificity: a real number beats a vague adjective every time. And they reward consistency across the web, since a model trusts a claim it sees confirmed in three places more than one it sees once.
I run this for my own brand and for clients, and the method is the same. First, I map the questions buyers actually ask an AI, not the keywords they type into Google. Those are different lists. Second, I rewrite the pages to answer those questions in the first paragraph, with a cited fact attached. Third, I add structured data and clean schema so machines parse the page correctly. Fourth, I seed the same factual claims across the sources models read: my site, my profiles, third-party mentions, and structured directories. This is the same operating discipline I used when I drove Riverside +337% Net MRR: find where the demand is forming, then put the brand in front of it before competitors notice the channel exists.
One honest caveat. GEO is early, and the engines change their citation behavior month to month. So I treat what is GEO as an ongoing measurement problem, not a one-time fix. I track which prompts surface my pages, which competitors get cited instead, and which content edits move the needle. Then I repeat. The brands that win the next two years of search are the ones treating AI citation as a measurable growth channel today, with the same rigor I applied managing $100M+ in total ad spend across roles: test, measure, cut what fails, double the rest. That is the work, and it is why what is GEO is no longer an optional question for anyone serious about pipeline.
Related
Frequently asked questions
What is GEO in simple terms?
GEO is work on how a company is discovered and represented in AI-search answers, including useful content, access, accurate facts and recorded observations. A mention names the brand; a source citation links to supporting material. Keep both separate from a qualified inquiry or sale.
How is GEO different from SEO?
SEO and AI-search work can use the same useful, accessible pages and accurate facts. Their observations differ: a search listing is not the same as a linked source in a generated answer. Inspect the relevant system and mode, and record the answer rather than assuming universal citation weights or duplicate parallel work.
Does GEO replace SEO, or do I need both?
You need both right now. Classic search still drives meaningful traffic, and many of the sources AI models read are the same pages that rank well. I treat SEO as the foundation and GEO as the layer on top: clean technical pages, then front-loaded answers, cited facts, and schema that helps a model parse and quote you correctly. Drop either one and you leave pipeline on the table.
How do I know if GEO is actually working?
Measure observed citation presence separately from rankings and qualified outcomes. Repeat a recorded buyer-query set across the same engines and settings, and log sources and dates. Compare observations before and after edits, including no change; this shows association, not that the edit caused the movement.
What content changes have the biggest GEO impact?
Choose a change from a documented buyer or page problem: clarify an answer, add relevant evidence, repair access or correct a fact. A useful example or appropriate schema does not promise a citation uplift. Record the approved change and repeat comparable answer observations to see what happened.
What are the five pillars of GEO?
I review five practical areas: readable crawl access, relevant third-party sources, a clear person or company identity, answers the buyer can understand, and original evidence with a visible method. These are checks and editorial choices, not established weights in an AI engine. Access, schema, a FAQ or original data does not guarantee inclusion or citation.
What does your GEO process look like step by step?
I use the same method for my own brand and for clients, in four steps. First, I map the questions buyers actually ask an AI, not the keywords they type into Google. Second, I rewrite pages to answer those questions in the first paragraph, with a cited fact attached. Third, I add structured data and clean schema so machines parse the page correctly. Fourth, I seed the same factual claims across the sources models read: my site, my profiles, third-party mentions, and structured directories.
Does robots.txt control whether AI engines use my content?
Not fully. robots.txt is a request, not a lock. OpenAI says its rules may not apply to user-initiated ChatGPT-User fetches, and Perplexity says its user-triggered fetcher generally ignores them. A crawler also follows only the most specific group that matches its name, so a bare User-agent: * block catches every bot you did not name. Write separate rules per bot family: training (GPTBot), search (OAI-SearchBot, PerplexityBot) and user fetchers (ChatGPT-User, Perplexity-User).
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About the author
Yaniv GoldenbergFractional CMO & Head of GrowthFractional CMO & Head of Growth — I lead strategy and personally execute across the revenue funnel. He scales post-product-market-fit companies to the revenue milestone that unlocks their next funding round. He scaled Elementor from $200K to $20M in ARR (a 100x increase), grew Riverside.fm’s Net MRR by 337%, and led demand generation at cnvrg.io, which Intel acquired in 2020. With 10+ years operating across every channel, motion, and stage, he hands the growth engine back once a team can run it without him.
- Elementor $200K→$20M ARR
- Riverside +337% Net MRR
- cnvrg.io → Intel
- 10+ yrs operating