A practical guide to AI Search Visibility, GEO, LLM SEO, citations, recommendations, source intelligence, and repeatable measurement across AI surfaces.

In short: AI Search Visibility measures how often and in what way a brand, website, or source appears in answers generated by AI-powered search systems. It is not the same as traditional SEO rankings, and it cannot be reduced to a single “position”: a brand may be mentioned, cited, recommended, or included among the alternatives considered by ChatGPT, Gemini, Google AI Overviews, AI Mode, and other AI surfaces.

Terms such as GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and LLM SEO are often used to describe the work behind this new type of visibility. They are useful labels, but the more important point is this: Search and AI are not a single surface, and they do not necessarily return the same brands, sources, or competitors.

This distinction is one of the reasons I developed Telescop and the AI Search Visibility in Italy research project: 500 queries observed across four AI surfaces through three independent runs, for a total of 6,000 observations.

What is AI Search Visibility?

AI Search Visibility is the ability of an entity — for example a company, product, professional, or editorial source — to appear meaningfully in AI-generated answers when users ask relevant questions.

But simply “appearing” is not enough as a definition. An answer may:

• mention a brand only as an example;
• include it in a list of alternatives;
• explicitly recommend it for a specific need;
• use its website as a source;
• cite a third-party source that discusses the brand;
• not mention it at all, even when the brand ranks very well in traditional Search.

That is why I see AI Visibility as a problem of presence + context + consideration + sources, rather than another vanity metric.

GEO, AEO, LLM SEO and AI SEO: what do they mean?

In its official documentation, Google treats GEO and AEO as third-party terminology and makes it clear that the usual SEO best practices remain the foundation for AI Overviews and AI Mode. Its generative search experiences still rely on core Search ranking and quality systems, together with retrieval techniques and query fan-out.

This does not mean that every AI platform works in the same way. It simply means that for Google there is no separate GEO shortcut that replaces solid SEO.

SEO and AI Search Visibility are not the same thing

SEO and AI Search share many foundations: technical accessibility, useful content, authority, entities, links, sources, clarity, and relevance. But they measure different phenomena.

A brand can therefore be strong on Google and weak in AI-generated answers, or the other way around. This is one of the reasons I see Search and AI as two different views of the same market rather than two completely separate worlds.

Why there is no single AI Visibility

Saying “we are visible in AI” is too generic. The real questions are: where, for which questions, and in what role?

In the Italian research we conducted with Telescop, we observed the same panel of 500 queries across:

• ChatGPT;
• Gemini;
• Google AI Overviews;
• Google AI Mode.

Each query was observed across three independent runs. The result is a dataset of 6,000 final observations, published together with the methodology and dataset under DOI 10.5281/zenodo.22640375.

The most important methodological finding is not “who wins.” It is that different AI surfaces can build different ecosystems of brands and sources. A single average can therefore hide operationally important differences.

How to measure AI Search Visibility

Counting mentions is a useful starting point, but it is not enough. I separate at least five dimensions.

1. Presence

Does the brand appear in the answer or not? This is the simplest measure and helps quantify coverage across the panel.

2. Surface Presence

Across how many AI surfaces does the brand appear? Being visible in only one system is very different from having cross-platform coverage.

3. Share of Voice

How much competitive space does the brand occupy compared with alternatives across the observed panel? In Telescop we also use a Weighted Share of Voice, which weights visibility across the panel rather than relying on a simple raw count.

4. Recommendation Share

How often is the brand actually proposed as a relevant solution or choice? This is different from being merely mentioned.

5. Citation Share and Source Intelligence

Which sources are referenced or used to support the answers? Is the brand itself the source, or does it emerge through third-party publications, media, reviews, marketplaces, or other websites?

These metrics should not be confused with “internal ranking factors.” They are observational measures of how the analyzed AI surfaces behave.

The problem with measuring a single prompt

One of the most common weaknesses in AI Visibility analysis is asking a system one question once and turning that response into a strategic conclusion.

Generative systems can produce different answers across runs, formulations, and contexts. That is why measurement requires a sufficiently broad and repeatable panel.

A more robust methodology should define:

1. a universe of queries or decision moments;
2. clear rules for what counts as presence, recommendation, and citation;
3. multiple runs when the surface is variable;
4. separate analysis for each platform;
5. a precise date or observation window;
6. versioning of the panel so comparisons over time remain meaningful.

In other words, the problem is not simply “asking ChatGPT what it recommends.” The real challenge is building an observation that can be repeated and compared.

How to improve visibility in AI search

1. Start with technical SEO

For Google AI Overviews and AI Mode, the official documentation is explicit: a page first needs to be accessible, indexable, and eligible to appear in Search. There is no separate route that allows you to ignore crawling, canonicals, robots directives, technical quality, or indexing.

SEO therefore remains the technical starting point before asking how a page may perform in AI-generated experiences.

And if you want an operational diagnosis of a specific page, we built a tool at WMA that lets you analyze a public URL and understand how solid it is from the perspective of SEO Foundation, AI Search Readiness, and Query Fit.

It is called the SEO AI Overview Analyzer, and it can help you quickly identify the main improvement priorities on the page.

There is, however, an important distinction between page readiness and actual visibility in AI-generated answers. The tool analyzes observable on-page signals, while brand presence, citations, and recommendations are different phenomena that need to be measured separately across individual AI surfaces.

2. Make the entity clear

Who is the author? Which company is behind the content? Which products, research projects, and initiatives are actually connected? Inconsistent naming, fragmented profiles, and pages with unclear relationships make it harder to build a stable representation of the entity.

That is why I consider brand consistency, author pages, Organization/Person markup where appropriate, official profiles, and verifiable links between entities to be important.

3. Publish specific information, not commodity content

If one hundred websites repeat the same definition, there is very little differentiation. Proprietary data, methodologies, experiments, cases, tools, examples, and original research add information that did not exist before.

Google itself, in its guidance for generative search experiences, emphasizes the value of content that is specific, useful, and grounded in real experience rather than generic repetition.

4. Make content easy to extract and verify

This does not mean “writing for bots.” It means reducing ambiguity:

• direct definitions;
• descriptive headings;
• tables when they genuinely clarify distinctions;
• data with sources;
• clear authorship and dates;
• a distinction between facts, methodology, and opinion;
• pages focused on a specific problem.

5. Cover decision moments, not only keywords

Traditional keyword research is still useful, but many AI prompts are conversational: “which solution should I choose?”, “what alternatives are available?”, “what makes sense in my situation?”, “which companies are reliable for…?”

This shifts the analysis from individual keywords toward a universe of decision moments. Content should help answer the questions that appear throughout the evaluation process, not only the query with the highest search volume.

6. Build a coherent internal graph

An isolated page communicates very little about the role it plays within a site. Connecting pillar pages, supporting articles, research, products, and authors creates a structure that is easier for both users and crawlers to understand.

This is the same reason I separate and connect the SEO, strategy, lead generation, and AI Marketing clusters on Gentian.it.

7. Pay attention to what exists outside your website

AI-generated answers can draw from information ecosystems that are much broader than a company’s own website. Publications, databases, repositories, reviews, profiles, datasets, and third-party citations all contribute to the information available about a brand.

This does not mean manufacturing artificial mentions. It means making real, coherent evidence available wherever the market already goes to learn and evaluate.

8. Check access for AI crawlers

For ChatGPT Search, OpenAI documents a specific crawler called OAI-SearchBot. Its controls are separate from GPTBot, which means publishers can allow Search retrieval while managing model-training access differently.

Blocking OAI-SearchBot can prevent website content from being retrieved and cited in ChatGPT Search. The configuration should therefore be a conscious decision rather than something copied from a generic robots.txt template.

9. Measure AI surfaces separately

A single “AI score” is convenient to display, but it can be misleading. If ChatGPT, Gemini, AI Overviews, and AI Mode produce different patterns, the dashboard should make those differences visible.

Only after that does it make sense to build a synthetic indicator.

Does llms.txt really matter?

For Google Search, no: Google’s official documentation states that llms.txt is not used to determine visibility or ranking in its generative search experiences.

This does not mean that an llms.txt file is always useless. Other services may decide to support it. But it should be treated as a potential interoperability file, not as a guaranteed SEO lever.

The priority remains making the website accessible, coherent, specific, and authoritative.

Structured data and AI Search

Structured data helps describe what a page contains and can improve the understanding of entities, articles, products, organizations, and other objects.

However, there is no special “GEO” or “AI visibility” schema that guarantees citations. Markup should represent the content that is actually visible on the page and should be treated as part of the information architecture, not as a trick.

AI Search Visibility and brands: the most important question

For a brand, the key question is not only “is my website being cited?” A more useful question is:

when someone describes a problem that my company can solve, does my brand enter the consideration set built by the AI?

This changes the way visibility should be interpreted. A source may be cited without the brand being recommended; a brand may be recommended because of third-party sources; an unexpected competitor may appear frequently even without dominating traditional SEO.

This progression from visibility → consideration → recommendation is what I find most useful for marketing and strategy.

An operational framework

The framework helps avoid turning GEO into a checklist of tricks. Each layer addresses an observable problem.

What not to do

• Measure a single prompt: one answer does not describe the market.
• Confuse mentions with recommendations: they are different signals.
• Publish AI-generated content at scale without differentiation: volume is not the same as specificity.
• Look for secret markup: there is no tag that guarantees inclusion in AI-generated answers.
• Treat every platform as identical: this hides meaningful differences.
• Ignore SEO: especially for Google, the fundamentals remain central.
• Treat proprietary metrics as internal ranking factors: an observational indicator does not reveal a platform’s algorithm.

The AI Search Visibility in Italy research project

To make this analysis reproducible, we published the methodology and dataset behind AI Search Visibility in Italy 2026.

The observational design uses:

• 500 Italian queries;
• 4 AI surfaces;
• 3 independent runs;
• 6,000 final observations.

The research and methodology are available on the Telescop Research page; the dataset and documentation have DOI 10.5281/zenodo.22640375.

My goal is not to prove that SEO is “dead” or to invent a new discipline every six months. It is to build a more reliable way to observe how Search and generative systems influence discovery, consideration, and choice.

Frequently asked questions

What is GEO?

GEO stands for Generative Engine Optimization and is used to describe work aimed at improving visibility in generative search experiences. For Google, GEO does not replace SEO: standard SEO best practices remain the foundation of its generative features.

What is the difference between SEO and LLM SEO?

Traditional SEO focuses mainly on visibility and rankings in search engines. “LLM SEO” is an informal term used for activities intended to improve how content can be retrieved, understood, or cited by systems based on large language models.

How do you measure visibility on ChatGPT?

You need to define a prompt panel that is relevant to the market and measure at least presence, recommendations, and sources across multiple observations. A single answer is not enough to describe a stable competitive position.

Do I need OAI-SearchBot to appear in ChatGPT Search?

OpenAI states that websites should not block OAI-SearchBot if they want their content to be retrievable, cited, and surfaced in ChatGPT Search. GPTBot and OAI-SearchBot have separate purposes and controls.

Do I need llms.txt for Google AI Overviews?

No. Google states that llms.txt is not required and does not positively or negatively affect ranking or visibility in its generative search features.

Does structured data improve AI Visibility?

Structured data can help systems understand content and entities when implemented correctly, but there is no special markup that guarantees a citation or recommendation.

Are AI Visibility and citations the same thing?

No. A citation refers to the source used or displayed in an answer. Visibility can also include brand mentions, competitive presence, and recommendations.

Sources and further reading

• Google Search Central — AI features and your website
• OpenAI — Overview of OpenAI Crawlers
• OpenAI — Publishers and developers FAQ
• Telescop Research — AI Search Visibility in Italy
• Dataset and methodology — DOI 10.5281/zenodo.22640375

Last updated: September 30, 2026.

Conclusion

AI Search Visibility does not replace SEO, and it cannot be reduced to a new universal ranking. It adds another layer of observation: understanding whether a brand appears in generated answers, in what role, through which sources, and on which AI surfaces.

The useful part is not chasing another acronym. It is connecting demand, Search, entities, content, sources, recommendations, and measurement within a single system.

That is the direction in which I am developing Telescop: not another dashboard, but a tool for turning Search and AI signals into clearer decisions.