• GEO builds on SEO. Crawlable, indexable, well-ranked content is still the foundation.
• The goal shifts from ranking a page to being mentioned and cited within an answer.
• Models learn about brands from the whole web, so third-party mentions matter as much as your own site.
• Answers vary from one run to the next, so measure trends across many prompts, not single responses.
• GEO, AEO, LLMO and AI SEO are largely different names for the same work.

For twenty-five years, search meant a list of links. Increasingly it means an answer, written by a language model, with a handful of sources attached. GEO is the discipline of earning a place in that answer.

You will also see it called answer engine optimisation (AEO), LLM optimisation (LLMO), AI SEO or simply AI search. The labels differ and the work overlaps almost completely.

How GEO differs from SEO

• The unit of success changes. In SEO you rank a page for a query. In GEO you want your brand mentioned, your content cited, or both, in a generated response.
• Queries are longer and more conversational. People ask assistants detailed, multi-part questions and follow up.
• Retrieval happens at passage level. A model may use one paragraph from your page alongside paragraphs from five others.
• Answers are not fixed. The same prompt can produce different wording and different sources each time.
• Clicks are fewer. Many answers satisfy the user without a visit, so visibility and referral traffic become separate measures.
• Off-site matters more. Models form a view of your brand from reviews, forums, news, Wikipedia and comparison articles, not just your own pages.

What stays the same

Quite a lot. AI search products retrieve from search indexes, mostly Google's and Bing's, plus their own crawlers. Pages that rank well in traditional search are cited more often. Technical accessibility, clear content, authority and trust all still count. Google's own position is that there is no separate set of requirements for its AI features beyond good SEO practice. GEO is best thought of as an extension of SEO, not a replacement for it.

Two ways models know things

• Training data: what the model absorbed when it was built. This shapes its default impressions of brands and topics, and it changes slowly.
• Live retrieval: the model searches the web at the moment of the question and grounds its answer in what it finds. This is often called retrieval-augmented generation, or RAG, and it is where most citations come from.

Influencing training data is a long game built on a consistent presence across the web. Influencing retrieval is closer to classic SEO and can show results much sooner.

The main platforms

• Google AI Overviews and AI Mode: built on Google's index and Gemini models.
• ChatGPT: uses its own search index and crawlers along with third-party search providers.
• Microsoft Copilot: grounded in Bing.
• Perplexity: its own index and crawler, with citations central to the product.
• Claude: web search through its own tooling and search partners.
• Gemini app, Meta AI and Grok: each with its own mix of sources.

They cite different sources for the same question, so results on one do not predict results on another.

The core pillars of GEO

• Access: AI crawlers can reach your content and read it without JavaScript.
• Extractable content: direct answers, clear structure and self-contained passages.
• Information gain: original data, experience and specifics that give a model a reason to cite you over a generic source.
• Entity clarity: consistent, unambiguous information about who you are and what you do.
• Authority beyond your site: mentions, reviews and coverage on the sources models trust.
• Freshness: up-to-date content with visible dates.
• Measurement: tracking mentions, citations, sentiment and referral traffic over time.

What the evidence says so far

The original academic paper that coined the term GEO (Aggarwal and colleagues, 2023) tested ways of rewriting content and found that adding statistics, quotations and citations to credible sources improved visibility in generative answers, while keyword stuffing did not. Industry studies since then keep finding the same broad themes: strong organic rankings, brand mentions across the web and clearly structured content all correlate with being cited. The field is young, the platforms change often, and anyone promising guaranteed placement is overselling.

Common mistakes

• Treating GEO as a completely new discipline and neglecting SEO fundamentals.
• Blocking AI crawlers by accident and then wondering why you are not cited.
• Judging performance from one or two manual prompts.
• Producing mass AI-written content that adds nothing new.
• Focusing only on your own website and ignoring where else your brand is discussed.
• Chasing tricks such as hidden prompts in pages, which is manipulation and is being actively filtered out.

Where to start

• Check robots.txt, your CDN and server logs to confirm AI crawlers can get in.
• Make sure key content is in the server-rendered HTML.
• Rewrite important pages so each section opens with a direct answer.
• Audit how AI assistants currently describe your brand and note inaccuracies.
• Set up tracking for a representative set of prompts.

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