What Is AIO, GEO, and LLMO?

Tomohiro Iida · Published June 26, 2026 · Updated July 7, 2026

The terms "AIO," "GEO," and "LLMO" are not standardized across the industry — different people and companies use them to mean different things. What they share is only the goal: getting a company’s information reflected accurately in AI answers. In an official guide updated in June 2026, Google states plainly that optimizing for generative AI search is optimizing for the search experience — in other words, it is SEO. This article sorts out the terminology based on official guidance, and separates what is genuinely necessary to be found in AI search from what you can skip.

Key takeaways

Terms like "AIO," "GEO," and "LLMO" are used differently by different people and companies; the industry has not standardized their definitions. What they share is only the goal of getting a company’s information reflected accurately in AI answers.
Google’s official guide, updated in June 2026, states that optimizing for generative AI search is optimizing for the search experience — SEO itself. There is no special extra requirement to appear in generative AI features.
What you actually need to be found in AI search is high-quality content and a solid foundation of crawling and indexing. Adding llms.txt or AI-specific schema is not stated as required.

Terms are not standardized

AIO (AI Optimization), GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and LLMO (Large Language Model Optimization) — the scope each term covers differs depending on who is using it. The only thing they share is the goal: getting a company’s information reflected accurately in AI and generative AI answers.

Where terminology proliferates like this, it becomes easy to sell "special measures" on the strength of a "new term" alone. The first thing to keep in mind is that a new-sounding term and genuinely new work are two different things.

Google’s position: optimizing for generative AI search is SEO

Google’s official guide states that "SEO best practices remain effective" for optimizing for generative AI search. There is no special extra requirement to appear in AI Overviews or AI Mode.

The premise Google lays out is simple: to be eligible for generative AI features, a page needs to be indexed and eligible for snippet display in ordinary search. Official guidance states there is no special requirement beyond that.

In other words, the foundation for being found in AI search is the same as it has always been for SEO: high-quality content, a technical structure that doesn’t obstruct crawling and indexing, and clear information architecture.

A deeper look at the mechanism behind Google’s AI Overviews.

Guide: how AI Overviews works

RAG and query fan-out

Why does traditional SEO carry straight over? It helps to understand how Google’s AI features actually work.

RAG (Retrieval-Augmented Generation): When AI generates an answer, it searches indexed content and builds the answer based on what it finds. Because a "search" is running underneath, content that is indexed and evaluated by search forms the foundation.
Query fan-out: For a single question, several related queries are generated at once, and relevant information is gathered for each. A page doesn’t need to match the user’s exact wording — pages covering a related topic can still be picked up through these surrounding queries.

What these two mechanisms show is that there is no "back door" reserved for AI — AI answers are assembled on top of ordinary search infrastructure.

The baseline SEO you still need

The baseline SEO needed as a foundation for AI search is nothing special.

Crawling and indexing are not obstructed (robots directives, status codes, rendering).
The page is eligible for snippet display.
Titles and headings clearly convey the content.
Internal links let related information be followed.
The page displays and operates correctly on mobile.

None of this is newly required because of AI search — these have all mattered for a long time. AI search becoming widespread has simply reconfirmed their importance.

Original content

Since RAG generates answers based on "content it found by searching," what raises the chance of being cited is unique first-hand information, original insight, and concrete facts that exist nowhere else.

A rehash of general knowledge, or content that could be found anywhere, gives AI little reason to specifically reference that page. Data, case examples, procedures, and judgment criteria that a company genuinely holds — verifiable specifics — are the realistic way to raise the odds of being cited.

What sites not cited by AI have in common

Technical structure

What is needed on the technical side is not a special AI-only mechanism, but a state where "machines can correctly retrieve and understand the content."

Content is rendered as HTML, and the body doesn’t disappear due to JavaScript dependency.
Basic information — company name, location, what you offer — is consistent across the entire site.
Structured data is used in line with the purposes Google actually supports (such as rich results).

More on structured data below — but official guidance states there is no need to add "special schema for AI."

Observing AI outside Google

Everything so far assumes Google Search (AI Overviews and AI Mode). ChatGPT, Perplexity, and other AI services, meanwhile, run on their own mechanisms and their own indexes, separate from Google.

How a company appears in those falls outside what Google’s official guidance covers. Behavior differs by service, and results shift with model updates, the presence of search features, region, and point in time. How a company appears in AI other than Google is therefore something to observe, not something that can be guaranteed. We treat it as the result of actually running queries and observing what comes back.

What you don’t need to do

Google’s official guide explicitly names measures that are "not needed" for generative AI search. The following are stated officially as things you do not need to build, or that are not required, to appear in Google’s generative AI search.

Building llms.txt or other AI-specific files — official guidance states there is no need to create a new machine-readable file, AI text file, markup, or Markdown just to appear in Google Search.
Chunking content into small pieces — stated as unnecessary to appear in AI search.
Adding AI-specific schema or markup — structured data is stated as not required for generative AI search, with no special schema.org markup that needs adding.
Writing in an AI-specific style, such as forcing an answer-first format — stated as unnecessary purely for the sake of generative AI search.

What is llms.txt?

None of this means these are forbidden — it means official guidance does not provide grounds that doing them makes a site more favored in AI search. It’s a reasonable call not to spend limited effort on measures without that grounding.

On FAQ structured data specifically: Google ended the display of FAQ rich results in May 2026. Keeping FAQ structured data in place is not a problem, but you can no longer expect it to produce a rich result in search listings.

How to measure

How a company appears in AI search cannot be measured from a single screenshot. AI answers vary by model, search feature, region, and point in time.

On the Google Search side: Check indexing status, impressions, clicks, and traffic on an ongoing basis using Search Console and GA4. Traffic that originates from AI Overviews is also reviewed within ordinary organic-search measurement.
On the AI-service side: Observe a fixed set of queries repeatedly and under multiple conditions, recording whether the brand is mentioned, whether the official URL is referenced, and whether the facts match. Judge by the pattern across repeated observations, not by a single result.

The starting point for measurement is not declaring "we’re showing up in AI" based on a single observation.

Netsujo SIGNAL’s approach

Netsujo SIGNAL is designed around the premise laid out in this article: the foundation of AI search is SEO, and there is no AI-specific magic.

Organize a company’s core information as first-party fact, and remove inconsistencies across the website, PDFs, sales materials, and structured data.
Check the foundation of retrieval — crawling, indexing, and rendering.
Observe AI services with a fixed set of queries, recording mentions, references, and factual accuracy.
Cross-check against Search Console and GA4, and prioritize only improvements that have evidence behind them.

What we can promise is not that a company will definitely appear in AI. It is that we build, on a factual basis, the foundation for being described accurately by AI.

Start by checking how your company currently appears to AI. This does not guarantee search ranking or inclusion in AI answers.

See Netsujo SIGNAL plans

Frequently asked questions

What are AIO, GEO, and LLMO?: AIO (AI Optimization), GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and LLMO (Large Language Model Optimization) are all terms used toward the same goal — getting a company’s information reflected accurately in AI and generative AI answers — but the scope each covers differs by who is using it; the industry has not standardized the definitions. What they share is only the goal; a new-sounding term and genuinely new work are two different things.
Do I need AIO/GEO measures separate from SEO?: Google’s official guide states plainly that "SEO best practices remain effective" for optimizing for generative AI search. To be eligible for generative AI features, a page needs to be indexed and eligible for snippet display in ordinary search — there is no special extra requirement beyond that. The foundation for being found in AI search is the same as it has always been for SEO.
Why does traditional SEO carry over to AI search?: Google’s AI features run on RAG (Retrieval-Augmented Generation) and query fan-out. RAG searches indexed content to assemble an answer; query fan-out generates several related queries from a single question to gather information. There is no AI-only back door — AI answers are built on top of ordinary search infrastructure, so content that is indexed and evaluated by search forms the foundation.
Should I build llms.txt or AI-specific schema?: Google’s official guide states that AI-specific files such as llms.txt, chunking content, AI-specific schema or markup, and AI-specific writing styles are not required to appear in generative AI search. None of this is forbidden, but official guidance gives no grounds that doing it makes a site more favored in AI search. There’s no need to spend limited effort on measures without that grounding.
Does FAQ structured data still help?: Google ended the display of FAQ rich results in May 2026. Keeping FAQ structured data in place isn’t a problem, but you can no longer expect it to produce a rich result in search listings.
How should I measure how a company appears in AI search?: Because AI answers vary by model, search feature, region, and point in time, a single screenshot can’t measure it. On the Google Search side, check indexing status, impressions, clicks, and traffic continuously via Search Console and GA4. On the AI-service side, observe a fixed set of queries repeatedly under multiple conditions, recording mentions, references, and factual accuracy. Judge by the pattern across repeated observations, not a single result.