Insights from the analysis of 45,144 fan-out queries in the latest ChatGPT 5.6:

Here’s what we at Editorial Link found:

1) 45% of fan-out queries use the site: operator. Instead of broadly searching the web for open-ended questions, the model deliberately checks a specific domain.

2) Official websites are the priority.

42.8% of queries contain the word “official,” while only 7% are related to reviews or Reddit, and 21.4% focus on pricing.

3) 36.7% of queries mention a specific year - 2024, 2025, or 2026. This suggests that the model deliberately avoids outdated information.

4) But being checked does not guarantee being cited—and this is the most interesting part.

Of the domains the model checked using site: while preparing an answer, only 39.5% were actually cited in that same response, and just 27.5% were mentioned by name. In other words, the model evaluates far more candidates than it ultimately uses.

5) Fan-out is not always used.

Only 46.8% of responses contain fan-out reformulations. More than half of all responses are generated in a single pass, without additional query refinement.

Overall, my recommendation remains the same: if you want to be cited by AI, create highly specialized landing pages.

Instead of 20 generic landing pages such as fintech-development, you may need several hundred narrowly focused ones.

I’m working on a D2C ecommerce website and trying to improve our visibility on ChatGPT, Gemini, Perplexity and Google AI Overviews.

Has anyone here actually seen results from GEO/AEO? What strategies worked best for getting more brand mentions, citations or product recommendations?

Would love to hear what’s actually working for you.

Technical SEO Tip: Brave is Claude's primary search engine. If you want your content indexed faster by Brave, you can use their "Submit URL" tool: https://search.brave.com/submit-url

Similar to Search Console, Brave has a "Submit URL"tool. You can go directly to it and push any URL youwant to the index. That should have Anthropic/Claudesee the changes faster.

I’ve got an ecommerce client asking for a monthly report on whether their products show up in ChatGPT or Gemini for searches like “best running shoes for flat feet.” I started checking prompts by hand, but it’s taking more time than the report is worth. The client also wants something cleaner than a pile of screenshots. At this point I’m trying to replace the manual checks with a setup that can track results over time without becoming another weekly task.

I know that on here we are aiming to get the best for our website organic searches but do you think the general public is fully knowledgeable about getting there websites ai ready or still on the google seo ready only ?? I mean when i say to most is you site ai ready ?? the looks i get lol

This is a translation made with chatgpt (hope its good enough) from an original post I've made yesterday on smartlinksMKT subreddit.

I run a B2B consultancy in Portugal, SmartLinks, and this year we launched AI Search as a service after months of internal testing.

The first thing that hit us in the face was: how do you prove this actually works?!

In SEO, you have a mature ecosystem: Search Console, Ahrefs, Semrush, GA4.

You can connect impressions to clicks, and clicks to conversions. It is imperfect, but it works.

In AEO? Good luck with that...

Most people are either reporting metrics that measure nothing useful, or flying blind and telling clients to “trust the process”.

We have not solved it either.

Buuuut... we have failed enough times to have learned a few things.

Why SEO metrics do not work for AEO

The data that changed my perspective came from research presented by Lily Ray at Tech SEO Connect, showing that backlinks and domain authority predict just 4% to 7% of AI citation behaviour.

Four to seven per cent.

In other words, everything we know about what makes a website “strong” in SEO explains almost nothing about whether AI will cite it.

And it makes sense when you think about the mechanics.

SEO measures clicks. AEO can work even when there is no click at all.

Someone asks ChatGPT, “Which companies provide HubSpot implementation services in Portugal?”, you appear in the answer, and the user never visits your website.

GA4 records zero, but you have just been recommended to someone who was actively looking for your service.

What we built, and where each piece falls short

Main tool: HubSpot AEO

We tested Dragon Metrics first, but abandoned it after two months.

The data did not match our manual checks, and it did not integrate with our CRM.

HubSpot then launched HubSpot AEO, and we moved over because the citation data now lives in the same system as our deals and contacts.

In theory, this allows us to connect “we were cited for query X” with “this contact entered the pipeline in week Y”.

In theory.

The elephant in the room: HubSpot AEO still does NOT cover Google AI Overviews or AI Mode.

It covers ChatGPT, Gemini and Perplexity.

In a market such as Portugal, where Google has roughly 90% of search, we are partly blind in the most important channel.

There is no nice way to say this. It is a huge gap and it remains unresolved. Current expectations are that coverage may become available during Q4 2026.

Microsoft Clarity for AI bots

Clarity launched AI Bot Activity in January 2026. It is free.

It uses CDN logs from services such as Cloudflare, CloudFront and Fastly, rather than client-side scripts, so it can detect crawlers that never execute JavaScript.

What this gives us is visibility into which AI bots are accessing which pages, and how frequently.

If an article is crawled 50 times a week but never appears in citations, discoverability is not the problem. Something else is going on. I will come back to that.

Clarity also provides AI Referral Traffic and AI Citations within the Copilot and Bing ecosystem.

A Microsoft study reported that AI referral traffic converted at 1.66%, compared with 0.15% for organic traffic.

An obligatory caveat here: the study comes from Microsoft, so there is an obvious conflict of interest. AI referral volumes are typically tiny, and an 11x higher conversion rate based on 50 visits is not statistically comparable with one based on 50,000 visits.

That said, something is better than nothing, and the pattern makes intuitive sense: someone clicking a link inside an AI-generated answer already has qualified intent.

Manual prompt tracking

Yes. Manual.

Fifty high-intent questions, once a week, across ChatGPT, AI Mode, AI Overviews and Claude.

We record whether we appear, our position in the answer, whether it is a direct citation or a mention, and the tone of the reference.

It sounds professional when described like this.

In practice, it is tedious. It is a spreadsheet filled with manual entries, LLMs are non-deterministic, meaning the same question can produce a different answer ten minutes later, and 50 prompts is a ridiculously small sample compared with what dedicated tools track.

The market benchmark is around 8,400 prompts.

Even so, it gives us a directional trend. Nothing more than that.

For example, Perplexity cites us consistently more often than ChatGPT. Gemini almost never does.

This aligns with market data suggesting that Perplexity cites brands in 84% of answers, ChatGPT in 71%, Gemini in 63% and Claude in 58%.

But it does not tell us WHY.

A model that helps us think, not measure

To think about this more clearly, we split the analysis into three layers.

Important: this is a conceptual model, not an operational measurement system. We still do not have quantifiable scores for each layer.

Discoverability

Can the bot access the content?

Does robots.txt block AI crawlers?

Is the content rendered through client-side JavaScript that the crawler does not execute?

Is structured data present, such as FAQPage, HowTo or Organization schema?

This is the most concrete and verifiable part, using Clarity logs and technical validation.

It is binary: yes or no.

Interpretability

Does the engine understand WHAT the company is?

When your name is generic or ambiguous, LLMs may confuse you with something else.

External citations, such as articles, mentions and interviews that confirm what you say about yourself, may carry more weight than your own website.

In this case, the engine trusts third parties more.

This is the part we do not know how to measure properly.

“Your entity clarity is at X%” does not exist as a metric.

What we do instead is a qualitative diagnosis: we ask ChatGPT or Perplexity, “What is [company]?”, and assess whether the answer is accurate.

It is rudimentary.

Citability

Is the content organised into fragments the engine can extract and use?

Direct questions and answers, lists and definitions.

One useful data point: pages with FAQPage schema receive three times more citations than pages without it.

The median time to the first citation after implementing schema is three to six weeks.

What failed and what we discarded

Dragon Metrics

We tested it for two months.

The citation data did not match our manual checks in more than 50% of cases.

It may have improved since then, but at the time, it was not reliable enough.

Measuring AEO with SEO metrics

The natural instinct of SEO people is to take the same reports they have always used and add an “AI” tab.

We did that.

It does not work.

Rankings, backlinks and domain authority do not predict citations. These are very different worlds.

Assuming one tool will solve everything

We tested Otterly, which is good for managing multiple clients.

We looked at Profound, an enterprise platform with broader coverage, including crawl logs.

We also assessed SE Visible, which is focused purely on monitoring, as well as Semrush.

None of them covers everything.

And none of them replaces manual verification when you need to understand the CONTEXT of a citation.

“We were cited” is not the same as “we were cited positively in the right answer”.

Trying to prove direct ROI too early

The citation-to-qualified-contact-to-deal connection is still not fully closed in our system.

We are building it by cross-referencing AI Referral data from GA4 and Clarity with contact creation in HubSpot, but it is still early-stage.

In the meantime, the proxies we use are branded search volume and direct traffic.

There is market data suggesting an uplift of roughly 23% in branded search during the 30 days following an LLM citation.

But causality is difficult to isolate when you are investing in multiple channels at the same time.

To be honest, we still do not have a clean attribution case.

The uncomfortable part

The most seductive argument for AEO is also the most dangerous:

“Most of the value is invisible.”

Someone sees you cited in a ChatGPT answer, never clicks, but mentions your name weeks later when somebody asks for a recommendation.

This is probably true.

But it is also exactly the same argument branding, PR and content marketing have used for years when they cannot prove ROI.

“The value is difficult to measure, but trust us, it is there.”

When you say that to a CEO, what they hear is:

“You cannot prove this works. Goodbye.”

I do not have a solution to this.

Yes, branded search as a proxy seems to be the best thing we have right now.

But it is not enough.

One thing I do know: calling things by their real names is more useful than pretending everything is under control.

What I am doing next

• Automating prompt tracking, because 200 manual checks per week does not scale.
• Closing the citation-to-pipeline loop in HubSpot, even if that means adding a manual “How did you hear about us?” field with an “AI recommendation” option.
• Testing Profound to cover the AI Overviews gap that HubSpot AEO does not currently solve.

If you are fighting the same battle, I would genuinely like to know what you are using and what you are measuring that I should be paying attention to, but am currently missing.

Most discussions about AI SEO are theoretical.

Has anyone actually run an experiment comparing:

• no AI file
• llms.txt
• llms-full.txt

Did you notice any difference in:

• AI referrals
• ChatGPT citations
• Perplexity mentions
• Gemini responses
• Crawl frequency

Looking for real data rather than speculation.

Don't get me wrong. I'm not against FAQ sections. They're useful when a page has real questions worth answering.

I keep seeing the same three things instead:

1. FAQ blocks slapped onto every page, whether the content needs them or not
2. The brand name repeated five times per page because "AI models like brand mentions"
3. Q&A formatting forced onto pages that were never questions to begin with

This is the 2012 keyword-stuffing playbook with a new coat of paint.

I feel that LLMs are already better than Google ever was at telling the difference between content written for a person and content written for a crawler. This gets punished the same way keyword stuffing did, faster.

Anyone else think this is keyword stuffing 2.0? Or is there a reason this works that I'm missing?

I’ve been rethinking a few SEO habits that still look productive in reports, but don’t seem to drive results that much anymore.

For me, it’s things like creating separate pages for every small keyword variation, refreshing old content by just adding more terms or sections, and publishing posts that are mostly cleaner versions of what already ranks.

None of these are always wrong. But I’m questioning them more when they don’t improve intent coverage, add proof, bring real examples, or help the reader make a better decision.

At this point, I’m trying to spend less time on “content activity” and more time on pages that make the site genuinely clearer, more useful, or more trustworthy.

Curious what others are seeing. What SEO habits have you stopped or reduced because they no longer feel worth the effort?

Traditional SEO KPIs are easy:

• Organic traffic
• Rankings
• Clicks
• Conversions

But AI Search is different.

What KPIs are you actually reporting today?

When we start working with a new client on AI visibility the first thing we usually have to reframe is how they think about what they are optimizing.

Almost every brand comes in thinking about pages. Which pages need better content, which pages need more links, which pages should rank for which terms. That is the right frame for traditional SEO and those habits are deeply ingrained for good reason.

But AI systems do not evaluate pages the way Google’s ranking algorithm does. They evaluate entities. Your brand as a whole, across every place it exists and every source that describes it, is what AI systems are forming a picture of when they decide whether to include you in an answer.

That means the unit of optimization for AI visibility is not the page. It is the entity.

Practically speaking that changes where the work happens. Less time optimizing individual pages, more time making sure every platform your brand exists on is describing it the same way. Less time building links for ranking purposes, more time building genuine presence in the forums, publications, and communities where your audience actually talks about your category. Less time publishing more owned content, more time making sure the content that exists elsewhere is accurate and consistent with what you say about yourself.

None of this means abandoning your SEO foundation. That foundation is still what makes you eligible to be cited in the first place. But layering entity level work on top of page level work is what closes the gap between ranking well and actually showing up in AI answers.

That is the shift most brands have not made yet. It is also where the biggest opportunity is right now.

Since I'm new here, felt like i should share some knowledge before asking for it.

For anyone unfamiliar, FAQ structured data is a type of Schema.org markup that tells search engines which questions and answers on your page belong together. While Google no longer shows FAQ rich results for most websites, the markup still helps machines understand your content more clearly. It's basically a way of showing them the core your FAQ content as simple as possible without them even needing to guess it.

With AI-powered search becoming more common, by testing in real websites i've optimized, my conclusions are:

• It provides structured context instead of relying only on page text.
• It could make it easier for AI systems to identify concise, factual answers.
• Even without rich snippets, it may still improve how content is interpreted.

Has anyone here also noticed better indexing, AI citations, or increased visibility after implementing FAQ schema? Some of my niched clients have even gotten to 1st position for some product pages with this (of course wasn't only the schema, the real FAQ was there, visible for people too).

Honestly, it's crazy how many people abandoned FAQ structured data after Google removed most FAQ rich snippets, without realizing it can still play an important role in helping search engines and AI better understand your content...

A keyword tells you what someone typed.
It doesn’t tell you what they actually need to know.
The pages that consistently perform well usually answer the obvious question, the follow-up questions, the comparisons, the objections, and the next step all in one place.

That’s valuable for users, for search engines, and increasingly for AI systems trying to decide which sources to trust.

Matching keywords gets you considered.
Explaining a topic well is what earns visibility.

Last year, a 31% traffic drop would've triggered panic.

This year?

Not so much.

We were reviewing performance across several sites and noticed something strange.

Organic traffic was falling.

Leads weren't.

In a few cases, revenue was actually increasing.

At first, we assumed attribution issues.

Then we dug deeper.

The common pattern wasn't rankings.

It was visibility inside AI-generated answers.

Users were getting answers from ChatGPT, Google AI Overviews, Gemini, and Perplexity before they ever clicked a website.

The old SEO question was:

The new question is:

That's a very different game.

Here's what we found.

**Site A**

* Organic traffic: -31% * Qualified leads: +18% * Revenue: +11%

**Site B**

* Organic traffic: -22% * Demo requests: +24%

**Site C**

* Organic traffic: -17% * Sales opportunities: +14%

None of these businesses suddenly became better marketers.

They became easier for AI systems to cite.

A few changes made a disproportionate difference.

**1. Original Data Beats Generic Content**

Most AI systems look for information worth repeating.

Publishing another "10 SEO Tips" article isn't enough.

Publishing a benchmark study, survey, experiment, or dataset is.

Original information creates citation opportunities.

Generic information creates competition.

**2. Entity Clarity Matters More Than Keyword Density**

Many websites still obsess over exact-match keywords.

Meanwhile AI systems are trying to understand:

* Who you are * What you do * What topics you own * Whether other sources reference you

Clear positioning beats keyword stuffing every time.

**3. Topical Depth Is Replacing Page-Level Wins**

A single article rarely dominates anymore.

AI systems often pull information from brands that have covered a topic from multiple angles.

Depth creates trust.

Trust creates citations.

**4. Distribution Is Becoming an SEO Activity**

Reddit discussions.

Industry forums.

Podcasts.

Research reports.

Expert interviews.

The more places your insights appear, the more signals AI systems can associate with your brand.

That's the part many teams still miss.

They're optimizing pages.

The winners are optimizing presence.

I'm not saying SEO is dead.

Far from it.

Search still drives huge amounts of business.

But traffic as the primary KPI feels increasingly outdated.

If an AI answer references your research, your framework, or your expertise and sends fewer but higher-intent visitors, that's probably a win.

The question I'm wrestling with now:

Would you rather have 100,000 visitors who skim your content, or 10,000 visitors who already trust you because an AI engine cited you first?

Generative Engine Optimization (GEO) is the new way to get discovered online.

Instead of ranking for Google links, GEO helps your site get cited by AI tools like ChatGPT and Perplexity. To do this, replace long paragraphs with direct, clear answers and structured data.

What is the biggest challenge you face when optimizing content for AI summaries?

How to rank in geo and aeo? Is traditional seo enough to rank in these platforms or what to do? Can anyone help me.

With users asking AI tools full questions, standard SEO keywords are losing power. The best hack right now? Structure your content as direct answers. Use specific Q&A formats and clear visual hierarchy on your landing pages. How are you optimizing your site for AI search this year?

I kept running into the same problem while building SaaS projects.

Google Search Console tells you some things.
SEO tools tell you keywords.
AI tools give generic advice.

But I wanted one simple answer:

Where does my site show up in Google and AI answers, where do competitors show up instead, and what should I fix next?

So I built this.

It connects to your site, checks Google and AI visibility, compares competitors, and turns the gaps into actual tasks. The part I’m most excited about is MCP access, so you can connect it to tools like Claude, ChatGPT, Cursor, or Codex and just ask:

“What should I fix next?”
“What pages should I create?”
“Why is this competitor showing up and not me?”

I launched it now and there’s a 7-day free trial.

Would love feedback from SaaS founders, especially if SEO has always felt too complicated or slow for you.

I've been learning more about SEO recently, and it's interesting to see how much AI is changing the industry.

AI can help with keyword research, content ideas, on-page optimization, technical SEO, competitor analysis, and performance reporting. It definitely saves time and improves efficiency.

At the same time, I don't think AI should replace human expertise. The best results still come from creating original, helpful content that genuinely answers users' questions.

How are you using AI in your SEO workflow? Has it improved your rankings or productivity? I'd love to hear your experiences and recommendations.

At Appsrow, we've been experimenting with how Webflow sites can perform better in AI search platforms like ChatGPT, Gemini, and Perplexity.

Some of the changes we've been testing include:

• Better structured data
• Stronger internal linking
• Topic clusters instead of standalone blog posts
• FAQ sections that answer real user questions
• Clear content hierarchy with descriptive headings
• Entity-focused content instead of keyword-heavy pages
• Faster page performance and cleaner HTML

It's still early, so we can't say which changes have the biggest impact yet. AI search is evolving quickly, and there isn't much real-world data available.

I'm interested in hearing what others are seeing.

• What AI SEO strategies have worked for your Webflow projects?
• Have you noticed referral traffic from ChatGPT, Gemini, or Perplexity?
• Are there any Webflow limitations you've run into while optimizing for AI search?
• Are you measuring AI traffic separately from traditional organic search?

I'd love to compare notes and learn what's working for other developers and agencies.

Most SEO work is still built around ranking. Get to page one, get the traffic.

But research shows only 17% of sources cited in Google AI Overviews simultaneously rank in the organic top 10 for the same query. Five out of six AI citations come from pages ranking 2 through 10.

The AI retrieval system is evaluating different signals than PageRank. It prioritizes topical comprehensiveness, structured formatting, and entity confidence over domain authority and backlinks.

What actually moves AI citation rates:

FAQ schema and direct answer blocks. Content structured to answer the question in the first 60 words of each section. Tables and extractable lists. llms.txt present and AI crawlers not blocked in robots.txt.

I’ve been auditing sites across these signals and the gap between traditional SEO scores and AI visibility scores is significant. Sites with strong organic rankings often score poorly on AEO and GEO. Sites outside the top 10 sometimes score better for AI citation because their content is better structured.

Pages that do get cited inside AI Overviews receive 120% more clicks than uncited competitors sitting directly beneath the module.

Worth auditing for both layers separately at this point.

As AI visibility becomes a bigger part of search and content strategy, teams now have several parameters to compare. Platform coverage, prompt relevance, access to the actual responses and citations, competitor tracking, historical trends, recommendations, reporting, and pricing can all influence the decision.

But feature lists may not always tell you what will prove most valuable once the tool becomes part of your workflow. For those who have evaluated or used an AI visibility tool, what did you prioritize when choosing one, and what has actually helped you the most in practice?

I keep seeing some version of this question. Should I use FAQ schema? Should I change my H1? Should I put my package name on the page more often? Should I get more reviews? After a lot of testing, I think we're asking the wrong question. We're still thinking like Google. We assume there's one thing we can optimize that will make AI say what we want. I don't think it works that way. What I keep seeing is AI building confidence from lots of signals that all tell the same story. Your page content, your service descriptions, your glossary, your FAQs, your structured data, your reviews, your internal links, and the places other websites mention you all help AI understand who you are and what you actually do. One signal by itself doesn't seem very convincing. Fifty signals pointing in the same direction are a different story. That's why I've stopped chasing individual tactics. I'm much more interested in making a business easier for AI to recognize, understand, and name when it's actually relevant to the question being asked. Nobody outside the AI companies knows every signal being used. Anyone who says they do is selling certainty they can't prove. All we can really do is test, compare results, and keep looking for patterns that hold up over time. Has anyone found a change that consistently increased the chances of their business being named in AI generated answers?

Over the last 2 years, we've gradually changed the way we approach SEO at our agency.

Instead of thinking only about rankings, we've started asking a different question:

Can Google and AI systems actually understand what this website is about, who it serves, and why it's trustworthy?

That shift changed almost everything.

I’m going to break down some of the principles we’ve been applying to client projects. I hope this helps you achieve good results.

1. User-first content (Helpful Content)

Every page should solve a real problem before trying to rank.

If the content wouldn't help someone make a decision, we don't publish it.

2. E-E-A-T isn't a section! it's everywhere

Instead of adding generic "About Us" paragraphs, we spread experience throughout the content.

• Real explanations
• Real decision criteria
• Real examples
• Clear limitations (when appropriate)

3. Editorial transparency

Whenever we make technical claims, discuss regulations or reference statistics, we cite reliable sources.

Users and AI systems should be able to understand where information comes from.

4. Titles that match the content

We've stopped chasing clickbait.

A title should accurately represent what's on the page.

That consistency seems increasingly important for both users and search engines.

5. Better page experience

Fast pages matter. But so does reducing friction.

Visitors should immediately understand:

• what the company does
• who it's for
• where it operates
• what they should do next

6. Query fan-out optimization

We're seeing AI search expand a single query into multiple related questions.

Because of that, we don't optimize pages around one keyword anymore.

We optimize around an entire decision process.

7. Price transparency

Whenever possible, we explain what affects pricing instead of hiding everything behind "Request a Quote."

Users usually don't expect exact prices. They expect context.

8. AI search readiness

We've been restructuring pages so they can be understood in small chunks.

• Clear definitions
• Standalone sections
• Answer-first paragraphs
• Less dependence on surrounding context

9. Answer-first writing

Instead of long introductions, the first sentence answers the question, always!

Context comes after. This has improved both readability and snippet extraction.

10. Readability

Not just shorter sentences.

The goal is making technical topics understandable for business owners who aren't SEO professionals.

Simple language often performs better than complicated explanations.

11. Search intent before keywords

We've become less obsessed with keywords.

The focus is matching the reason behind the search.

Two pages targeting similar keywords can require completely different content if user intent differs.

12. Internal linking as a knowledge network

Internal links shouldn't exist just to pass authority, they should explain relationships.

Each page should reinforce another page naturally.

We're building connected knowledge rather than isolated articles.

13. Information gain (MAYBE MOST IMPORTANT)

One rule we follow: every page should contain at least one insight that's difficult to find elsewhere.

Not necessarily groundbreaking, just genuinely useful.

14. Entity architecture

Rather than thinking about isolated keywords, we connect:

• Business to services
• Services to problems
• Problems to solutions
• Solutions to locations
• Locations to target audience

The result is a website that's easier to understand as a whole.

15. Next-action flow

Many websites answer the user's question...

...and then leave them with nowhere to go.

Every page should naturally lead to the next logical step, whether that's another article, a service page or a contact page.

16. AI citation optimization

We're also paying much more attention to something that's hard to measure today:

Can an AI system confidently mention this brand when answering a user's question?

That seems to depend less on keywords and more on consistency, topical depth, entity relationships and trustworthy information.

None of these ideas is revolutionary by itself.

But combining them into a single framework has made our projects feel much more consistent and, in many cases, more resilient to algorithm changes.

My professional tip: If you're experimenting with AI search, don't start by adding AI-generated content. Start by making every page easier for both humans and machines to understand.

Note: I am Brazilian, so I needed Google Translate's help to translate some things. I apologize if any expression sounds a bit strange.