Generative Engine Optimization (GEO): being cited when AI answers

A growing share of business research now begins with a question put to an AI assistant rather than a list of links. Generative Engine Optimization (GEO) is the discipline of making your organisation's expertise retrievable, credible and quotable when those systems compose an answer.

This article explains what GEO is, how it relates to SEO, what the published evidence says works, and where popular claims outrun that evidence. It closes with a 90-day plan for B2B firms operating across Europe, MENA and Asia.

Generative Engine Optimization (GEO) defined

Generative Engine Optimization (GEO) is the practice of shaping content, evidence and entity signals so that generative search systems such as ChatGPT, Perplexity and Google AI Overviews select and cite your organisation when they answer relevant questions.

The term was formalised in a research paper by Aggarwal and colleagues, presented at the ACM KDD conference in 2024, which found that targeted optimisation could increase a source's visibility in generative engine responses by up to 40%. Adjacent labels, including answer engine optimization (AEO) and LLM search optimization, describe largely the same objective: to be the source an AI system relies on, rather than one of ten links a user may never open.

The distinction from traditional search lies in the unit of success. In SEO, the unit is a ranked page. In GEO, it is a passage, a fact or a recommendation that appears inside a synthesised answer, often with a citation and often without a click.

Why AI search visibility now matters for B2B

AI search visibility matters because business buyers already use generative tools throughout their purchasing process, and the answers they receive increasingly substitute for the pages they would once have visited.

Forrester reported in November 2024 that 89% of B2B buyers have adopted generative AI. A Gartner survey of 646 B2B buyers in 2025 found that 45% had used AI during a recent purchase. The platforms operate at considerable scale: OpenAI's chief executive said in October 2025 that more than 800 million people use ChatGPT every week, and Google reported in July 2025 that AI Overviews had more than 2 billion monthly users across over 200 countries and territories and 40 languages.

The effect on clicks is measurable. A 2025 Pew Research Center analysis found that users who encountered a Google AI summary clicked a traditional search result in 8% of visits, compared with 15% for those who did not; they clicked a link inside the summary in only 1% of visits. When the answer sits on the results page, being cited within it becomes the visibility that counts.

For firms selling across languages, the dynamics differ by market. Where credible local-language content on a specialist B2B topic is sparse, an AI system has fewer sources to draw on, and a well-structured page in Turkish, Arabic or Dutch may face less competition for citation than its English equivalent.

How GEO differs from SEO, and where they overlap

GEO builds on SEO rather than replacing it: generative systems still retrieve from crawlable, indexed pages, but what they reward is a clear, verifiable passage rather than a page's overall rank.

Google is explicit about the overlap. Its guidance on AI features in Search states that there are no additional requirements to appear in AI Overviews or AI Mode, that a page must be indexed and eligible to be shown with a snippet, and that no special schema.org markup or AI text files are needed. It also notes that these features may use a "query fan-out" technique, issuing multiple related searches across subtopics. In practice, this favours sites that answer the adjacent questions around a topic, not only its head term.

What the evidence says improves citation

The strongest published evidence favours content that is specific and verifiable: cited sources, quotations from credible parties and relevant statistics.

In the GEO study, tested on a benchmark of 10,000 queries, adding statistics, adding quotations and citing sources were among the most effective methods, while keyword stuffing performed below the unoptimised baseline. The study used a controlled benchmark, and generative engines change frequently, so the findings are directional rather than a formula. They are, however, consistent with how these systems are designed: they prefer passages they can attribute and defend.

Write passages that stand alone

Open each section with a one-sentence answer to the question its heading raises, then support it. Define terms where they first appear and avoid pronouns that depend on an earlier paragraph. A passage that makes sense when lifted out of the page is a passage an AI system can quote.

Build entity authority

Generative systems need to know who you are before they recommend you. Describe your organisation, founders, services and locations consistently across your website, LinkedIn, business directories, partner pages and industry listings. Mentions in credible third-party publications matter because they corroborate your own claims.

Treat structured data as hygiene, not as a shortcut

Organization, Person, Article and Service markup in the schema.org vocabulary helps machines distinguish entities and the relationships between them. Because Google states that such markup is not required for its AI features, structured data belongs in a clean technical foundation rather than being treated as a route to citation.

In SEO the unit of success is a ranked page; in GEO it is a passage an AI system is willing to quote and attribute.

llms.txt and other technical signals: what to do and what to discount

llms.txt is a proposed convention rather than an adopted standard; it is inexpensive to implement but should not be treated as a ranking lever.

The llms.txt proposal, published by Jeremy Howard in 2024, suggests a markdown file at the root of a site that gives AI agents concise, expert-level information in one place, on the grounds that web pages built for people are often inefficient for machines to parse. Google's guidance, cited above, says site owners do not need new machine-readable files or AI text files to appear in its AI features. Our view is pragmatic: an llms.txt file costs little and may help some agents, but no firm should expect it to change visibility on its own.

Other technical checks matter more. Confirm that robots.txt does not block the crawlers of AI search services in which you want to appear. Make sure key content is present in server-rendered HTML, keep pages fast and accessible, and ensure that every important claim appears as text rather than only in images or downloadable PDFs.

How the Tugam Growth Engine applies to GEO

We apply the same four stages that govern our outbound and partnership work, because the underlying task is the same: reaching the right buyer with credible, relevant information at the moment it matters.

• Enrich. We build the prompt set from evidence rather than guesswork, drawing on sales call notes, CRM loss reasons, Search Console queries and the roles within your target accounts. We also audit how your organisation is described across the web and where those descriptions conflict.
• Personalize. Answer-first content is drafted with AI assistance, reviewed by subject experts and adapted by persona and language, supported by proprietary data, named authorship and sources an engine can attribute.
• Branch. Definitional, comparative and vendor-selection questions call for different assets, from glossaries to comparison pages to implementation guides. Visitors arriving from AI referrals are identified in analytics and routed to paths suited to a buyer who has already read a summary.
• Deliver. Content is published on your site and distributed through LinkedIn, partner sites and industry media, the third-party sources engines use to corroborate claims. Citation share is measured monthly and the programme adjusted accordingly.

A 90-day GEO plan

1. Days 1–10: baseline. Build the prompt set, run it across the main engines in each target language, and record mention and citation share against competitors.
2. Days 1–20: technical check. Confirm indexing, snippet eligibility, rendering and crawler access, and decide whether to publish an llms.txt file.
3. Days 10–30: entity consistency. Align your organisation's description across the website, LinkedIn, directories and partner pages, and add Organization and Person structured data.
4. Days 20–45: rewrite priority pages. Restructure the ten most commercially important pages so that each section opens with a direct answer and carries verifiable sources.
5. Days 30–70: fill the gaps. Publish answer-first content for prompts where you are absent, including comparison and implementation questions, in each priority language.
6. Days 45–90: earn corroboration. Contribute expertise to partner and industry publications so that independent sources describe what you do.
7. Day 90: review. Re-run the prompt set, compare citation share with the baseline, review AI referral traffic and pipeline, and set the next quarter's priorities.

Generative engine optimization is still a young discipline, and anyone who promises guaranteed placement in AI answers is promising more than the evidence supports. What can be done is to make your expertise easier to find, verify and quote. If you would like to understand how AI systems currently describe your organisation, we would be glad to begin that conversation.