LLM visibility, GEO, AEO and AI SEO: what the terms mean and which number to track

Search any of these five terms and you will find one activity described five ways: getting an AI assistant to name your business when a buyer asks it a question. The vocabulary is genuinely confusing, and the confusion has a price. It is how the same measurement gets sold twice under two names, and how a score of 62 from one dashboard gets set beside a score of 71 from another when the two numbers were never the same unit.

The five terms, and who owns them

The answer is short. Almost nothing: they are different names for the same work, and no standards body defines any of them. There is no agreed definition and no owner for most of the labels. The one term with a citable academic definition is GEO: a 2023 paper, presented at KDD 2024, defines generative engine optimisation and builds a benchmark for measuring it (Aggarwal et al., https://arxiv.org/abs/2311.09735, accessed 2026-10-27).

• GEO (generative engine optimisation). The umbrella term: work aimed at being named or cited in AI-generated answers. It is the label with an academic definition, and the one we use for the work itself.
• AEO (answer engine optimisation). Agency vocabulary for the same goal, usually with an emphasis on being the single answer in a result box or a voice reply. In practice, the vendors who say AEO mostly sell the same work as the vendors who say GEO.
• LLMO (large language model optimisation). Used two ways: as another name for the same goal, and more narrowly for making a site easy for language-model crawlers to read — llms.txt and similar proposals (see our llms.txt guide).
• AI SEO. The plain-English compound. The naming argument has run long enough to become content of its own: a Hacker News thread compares AI SEO, GEO, LEO, LLMO, AEO and AIO and argues that AI SEO won on search demand (https://news.ycombinator.com/item?id=44316654, accessed 2026-10-27). Whatever practitioners prefer, it is the phrase buyers type.
• AI visibility, or LLM visibility. The measurement-side label: not the work, but the thing you count. We say "AI visibility" when we mean the measured object, and GEO when we mean the work.

If someone tells you AEO and GEO are fundamentally different disciplines, ask which definition they are using and who wrote it. If the answer is a course they sell, you have your answer.

A GEO score is not a unit

This is where most of the confusion turns into wasted money. Can a GEO score from one tool be compared with a visibility score from another? No: a score is only comparable inside the tool that produced it, because the questions, surfaces, counting rule and repeat count all differ. Four choices sit underneath every score, and each one moves the number:

• The question set. Five questions or thirty? Branded or unbranded? A branded question ("tell me about Acme Dental") guarantees your name appears, so a tool that counts it is measuring its own prompt.
• The surfaces. One chat assistant, or five including Google's AI Overviews and AI Mode? An assistant answering from memory and the same assistant searching the web can disagree about whether you exist.
• The counting rule. A string match for your brand name flatters you: named in passing and recommended both count as a hit. A graded reading separates them.
• Repetition and date. The same question gets different answers on different runs. One run is an anecdote with a decimal point.

Buyers have started noticing. A thread titled "The Tracking Fallacy in Answer Engines" on r/AISearchLab describes dashboards full of LLM-visibility numbers that measure different things under the same words (https://www.reddit.com/r/AISearchLab/comments/1qjzpck/the_tracking_fallacy_in_answer_engines/, accessed 2026-10-27). The thread is a complaint rather than a study, but the arithmetic behind it is simple: change the question set and the percentage changes.

Mentions and citations: the two numbers worth tracking

Within one measurement, two outcomes deserve separate counts, and most dashboards blend them. Track both, separately: a mention is your name in the answer, a citation is a link to your site, and you can have one without the other.

They answer different questions. A citation says the assistant used your page as a source. A mention says it put your name in front of the buyer. You can be cited without being recommended — your page was one of ten sources and the recommendation went elsewhere — and recommended without being cited, because the model already knew you from memory. Our method scores the two separately and publishes the counting rule (how we measure).

For a local business, the number that moves first is usually mentions on unbranded questions: "best physio in Bristol", asked cold, with your name nowhere in the question. If the assistant names you there, across runs on different days, you have something real. If it names you only when the question contains your name, you have an echo, and an echo is worth nothing.

Four questions for any vendor

Before paying anyone — us included — ask four things:

1. Can I see the exact question list before I pay, and how many of those questions never mention my brand?
2. Which surfaces do you measure, and do you separate an assistant answering from memory from the same assistant answering with web search on?
3. How many times is each question asked, and what counts as a mention, a recommendation and a citation? Will I see the raw answers?
4. Is anything guaranteed?

Ask for the exact question list, which surfaces are measured, how many times each question is repeated, and whether you will see the raw answers. A vendor who can answer those four without hesitating is probably measuring something real.

The answer to the fourth should be no. Nobody can guarantee it, and a vendor who promises guaranteed rankings is telling you about their sales process, not their method. The models change, their sources change, and Google's own documentation states that its AI features require no special optimisations beyond being indexable and eligible for a snippet (https://developers.google.com/search/docs/appearance/ai-features, accessed 2026-10-27). A thread on r/SEO_LLM about what this work actually costs makes the market version of the same point: "guaranteed ChatGPT rankings" is a red flag, usually offered by the vendors who will not publish a price (https://www.reddit.com/r/SEO_LLM/comments/1vzgfd0/what_geo_ai_search_optimisation_actually_costs/, accessed 2026-10-27).

The evidence for what does work is real, modest and qualified. The GEO benchmark found that adding statistics, quotations and cited sources lifted visibility by 30–40% — for top-five sources, in English, in one experiment that has not been replicated. That is worth knowing and worth testing. It is not a promise that the same tactic moves your market.

What a vendor can promise is a method: this question set, these surfaces, this many repeats, this counting rule, this price. Ours is 30 real buyer questions across five live AI surfaces (176 graded answers, 302 API calls) for $4.99, once. The scoring rule is public (method), the price is on the pricing page, and you can read a complete real report (the sample) before spending anything.

Which number should a small business track?

If you track only one number, track this: the share of your unbranded buyer questions where the assistant names you, measured across repeats, on more than one surface, with the raw answers kept.

Everything else — a composite score, an authority index, a mark out of 100 — is derived from that, and derived numbers are fine on a dashboard. They are poor guides for spending, because you cannot repair a score. You can repair a wrong fact about your opening hours, a service page that does not exist, or a directory listing that says you closed in 2019.

If you are still deciding whether any of this matters for your business, find out cheaply: the free check reads your site and shows you the questions a measurement would ask. For the audit side, see what an AI visibility audit contains and how it differs from an SEO audit. The plain definition of GEO lives on our GEO page.

Common questions

What is the difference between GEO, AEO, LLMO and AI SEO? Almost nothing: they are different names for the same work, and no standards body defines any of them.

Can I compare a GEO score from one tool with another tool's score? No: a score is only comparable inside the tool that produced it, because the questions, surfaces, counting rule and repeat count all differ.

Should I track mentions or citations? Track both, separately: a mention is your name in the answer, a citation is a link to your site, and you can have one without the other.

What should I ask a GEO vendor before paying? Ask for the exact question list, which surfaces are measured, how many times each question is repeated, and whether you will see the raw answers.

Does GEO guarantee that ChatGPT will recommend my business? No. Nobody can guarantee it, and a vendor who promises guaranteed rankings is telling you about their sales process, not their method.

Sources

• Aggarwal et al., "GEO: Generative Engine Optimization", arXiv:2311.09735 (KDD 2024) — https://arxiv.org/abs/2311.09735 (accessed 2026-10-27)
• Hacker News, "AI SEO vs. GEO vs. LEO vs. LLMO vs. AEO vs. AIO" — https://news.ycombinator.com/item?id=44316654 (accessed 2026-10-27)
• r/AISearchLab, "The Tracking Fallacy in Answer Engines" — https://www.reddit.com/r/AISearchLab/comments/1qjzpck/the_tracking_fallacy_in_answer_engines/ (accessed 2026-10-27)
• r/SEO_LLM, "What GEO (AI search optimisation) actually costs" — https://www.reddit.com/r/SEO_LLM/comments/1vzgfd0/what_geo_ai_search_optimisation_actually_costs/ (accessed 2026-10-27)
• Google Search Central, "AI features and your website" — https://developers.google.com/search/docs/appearance/ai-features (accessed 2026-10-27)
• SeeWAIS method — https://seewhataisays.com/method (accessed 2026-10-27)