GEO, AEO, LLMO, AISO: are they the same thing?
In practice, yes: the four acronyms describe the same discipline, making a brand readable, understood and cited by AI systems. What changes is the emphasis: GEO looks at the generative engine, AEO at the answer engine, LLMO at the model and what it claims, and AISO at the search surface. None of them names a technique the others ignore. The real divergence is not between the acronyms; it is between vendors who measure the result and vendors who do not.
Why are there four names for the same thing?
Because the discipline emerged in several places at once, and each one named what was in front of it. Agencies coming from SEO saw a generative engine and called it GEO. Those looking at the product, the answer box, called it AEO. Those starting from the language model called it LLMO. Those starting from the search surface called it AISO.
Every vendor then defends its own term, which puts the buyer in the worst possible position: having to pick a vocabulary before understanding the method. It is a branding debate dressed as a technical one. The four words are worth knowing, because they show up in proposals and in searches; defending one of them is not worth the energy.
What each acronym emphasizes, side by side
| ACRONYM | WHAT IT STANDS FOR | EMPHASIS | QUESTION IT ANSWERS |
|---|---|---|---|
| GEO | Generative Engine Optimization | The generative engine | How do I get cited in the answer the AI generates? |
| AEO | Answer Engine Optimization | The answer engine | How do I become the source behind an answer box? |
| LLMO | Large Language Model Optimization | The model and its claims | What does the model say about me, and is it right? |
| AISO | AI Search Optimization | The search surface | How am I present across every AI-mediated search? |
Source: ReBo consolidation of market vocabulary, 2026.
There is a fifth term in circulation, relevance engineering, used by the US agency iPullRank for the discipline that succeeds SEO. Its declared scope is broader (it includes surfaces such as TikTok search, Amazon and app stores), but the core techniques are the same.
Where all four genuinely converge
On everything that decides whether a page is retrieved and cited. The content has to exist in HTML that is readable without JavaScript, because AI crawlers do not execute JS. It has to be organized in blocks that answer one specific question in a self-contained way, because the engine breaks the user’s question into several sub-searches, the so-called query fan-out.
It needs factual density, with figures, dates and named entities sitting next to the claim they support, and verifiable authorship, because that is the tiebreaker between two equally readable sources. And it needs external signal pointing at the domain, because a good page in isolation is rarely retrieved. None of the four acronyms disagrees with any of this.
Where they actually diverge
On exactly one thing, and it is not the name: LLMO covers a problem the other three treat in passing, what the model claims about the brand when no source was retrieved at all. GEO, AEO and AISO are about being among the chosen sources. LLMO is also about representation: whether the model describes the firm with two-year-old information, names a partner who has left, or attributes a strategy the firm does not run.
For financial markets this is not a detail. Being cited wrongly is worse than not being cited, because the allocator reads the claim with the same confidence they would read a verified figure. That is why factual accuracy is a metric in the monthly report, alongside citation rate.
Which term to use, and with whom
Use your counterpart’s. In the English-speaking market, "AEO" has taken over the buying vocabulary and "relevance engineering" has gained ground among agencies. In Brazil, "AI Visibility" and "GEO" circulate more in commercial conversation. In a technical conversation with a data or engineering team, "retrieval" and "LLMO" usually communicate better than any of the acronyms.
What cannot change is what sits underneath: the method and the measurement ruler. If a vendor switches acronyms and the method changes with it, the problem was never vocabulary.
The question that separates real vendors
It is not "do you do GEO or AEO?". It is "what is my baseline today, and was it frozen before you published anything?". AI answers are not publicly archived and they change with the index, the model and the date: without a record predating the first publication, no result presented afterwards is demonstrable.
The second question is about the prompts measured: are they category questions, the ones a client actually asks, or only branded questions, which any brand "wins"? The third is about proof: a number, a date and an engine on record, or an organic-traffic report? A vendor who answers those three well is doing the same work, whatever they call it. The full list is in how to evaluate an AI Visibility vendor.
Frequently asked questions
Are GEO and AEO the same thing?
In practice, yes. Both describe optimizing content to be cited inside AI-generated answers. GEO emphasizes the generative engine and AEO the answer engine; the techniques and the metrics are the same.
Which acronym dominates the English-speaking market?
"AEO" has largely taken over the buying vocabulary, with "relevance engineering" advancing among agencies. "AI Visibility" and "GEO" are more common in Brazil and in other Portuguese-speaking markets.
Is LLMO different from the other three?
It is the only one that explicitly addresses what a model claims about a brand with no retrieved source. The other three focus on being chosen among the sources. In practice a complete operation covers both sides.
Do I need separate services for each acronym?
No. A vendor selling GEO, AEO, LLMO and AISO as four separate products is slicing up the same work. What you should require is a single method, with declared scope and measurement against a baseline.
How do I tell whether a vendor knows the discipline, whatever the acronym?
Ask about the baseline frozen before publishing, the set of prompts measured, and the form of proof. A vendor who answers those three with a number, a date and an engine is doing the work; one who answers with organic traffic and client logos is not.