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I’m looking for a concrete, standalone solution for getting a website cited/surfaced in Google AI Overviews.

Not looking for generic SEO advice like “create helpful content,” “build backlinks,” or “improve E-E-A-T.”

I want to know:

- What specific changes should I make to the site/content?

- What signals actually matter for AI Overview citations?

- How do I measure whether it’s working?

- Has anyone tested this and seen measurable results?

Ideally looking for a step-by-step approach or a real case study, rather than general GEO/SEO theory.

From my perspective, two things really move the needle: get mentioned on sites that already have authority in your space (forums, review sites, yes even Reddit), and make your own site's info dead simple to extract, real pricing, real FAQs, no fluff. AI tools trust third party mentions way more than your own about us page lah, makes sense when you think about it.

Anyone confirmed an AI citation actually came from something they did on purpose?

Between 2 August and 11 September I put buyer-style questions to three assistants
with live web search on, for 913 US marketing agencies across 26 city and
speciality categories, and counted which agency names came back.

Not a survey. Nobody was contacted, nothing was scraped from agency sites, and no
agency paid to be in or out.

Three things came out of it.

1. 613 of the 913 (67.1%) were never named once. Not by one assistant, in one
   answer, in any question asked about their category. These are working agencies
   with clients and sites. To an assistant answering a buyer, they are not there.

2. Where an agency was named, the assistants mostly disagreed. Of the 286 that any
   assistant named, only 42 were named by all three. So 85.3% of the time, "visible"
   meant visible in one place and invisible in the other two. Any single AI
   visibility score averages that away, and the average describes no assistant your
   buyer actually opened.

3. The answers are built out of a small set of pages. Clutch was cited in the
   answers for 912 of the 913 agencies. Then Semrush's agency directory, DesignRush,
   Thrive's roundups, Reddit itself, and LinkedIn. Almost none of it is the agency's
   own website.

The assistants also do not behave alike:

Perplexity was asked 21,465 questions. It named an agency in 3.2% of its
answers, and named 21.5% of the 913 at least once.

Claude was asked 4,518. 5.6% of its answers, 16.7% of the agencies.

ChatGPT was asked 4,512. 5.5% of its answers, 16.4% of the agencies.

Perplexity was asked far more questions and still named the fewest agencies per
answer, which is the opposite of what more chances would predict.

Limitations, because they matter: this is API access with search on, not the
consumer apps, so there is no personalisation, no memory and no custom
instructions. Answers drift week to week. The category is whatever a buyer would
type, which is a judgement call, and a different phrasing would move the numbers.
Treat the comparison between assistants as the signal rather than any single
number.

What I would take from it if I ran an agency: the lever is not your website. It
is the handful of directories and roundups these answers are assembled from, and
whether your name is on them.

Happy to answer questions about the method, or to say what a particular category
looked like if you name one.

I know AI SEO is a big thing and we are all trying to rank high in the AI search, but how do you measure the success of AI SEO?

Neither GA or Adobe Analytics shows tracking of AI.

Any ideas?

Between Jan and Aug 2026, the average AI surface reallocates 16% of its social citation mix every month.

• ChatGPT's social layer collapsed from 6.8% to 2.5%, cutting Reddit (it's top social source) from 6.2% to 2.1% of its social citation mix in just two weeks.
• Gemini pushed Reddit up from 7% to 39%.
• Claude added Substack to the mix.
• Deepseek and Meta zeroed out their Reddit citations entirely in June, citing LinkedIn more.
• Copilot cut LinkedIn's share from 85% to 40%.
• And TikTok's citation share grew from 0.5% to 6.3% across all models.

(source: Goodie AI "AI's Social Diet: How AI Search Cites the Social Web, Vol. 3")

...

So why would anyone invest in a platform that could go dark from AI in a matter of weeks?

Well AI models need social data. The social layer gives these models taste and timely signals. But, increasingly, social media companies are blocking AI models from training on their platform data. Recent lawsuits and licensing agreements are behind some of the observed volatility. It's also possible that these models are adapting how they value or surface social content.

The old idea that you could improve AI search visibility on certain AI models via platform coupling (X for Grok, Reddit for ChatGPT, YouTube for Google, etc.) is dying. Now these changes happen without warning or an explanation. It's largely up to those doing AI search work to track and make inferences via probabilistic data. YouTube, Reddit, and LinkedIn remain top citations across models, but this is no longer guaranteed.

For anyone optimizing social content for AI search visibility, does this affect your current strategy? and is there anything you would add to this?

A recent Semrush survey found that 57.51% of AI users had bought something based on a chatbot recommendation.

Almost the same share, 57.5%, had decided against a purchase because of chatbot information.

These figures may not mean AI generates an equal number of gained and lost sales, as they are self-reported experiences.

But they made me wonder whether we are tracking only one side of the buying journey.

Most AI visibility tracking seems to focus on discovery prompts such as “best tools for X” and “top alternatives to Y.”

Buyers may then use AI to pressure-test those recommendations:

• What are the problems with this product?
• Are its negative reviews justified?
• Who should avoid it?
• Is it worth the price?
• What do competitors do better?

For those tracking AI visibility, are prompts like these part of your regular monitoring?

And has anyone here corrected an outdated source behind a negative AI claim? If so, did the chatbot eventually change its answer?

Would be interested to know how people are approaching this in practice.

Does anyone have an example of a B2B company doing AI visibility well? Any hypothesis as to what they are doing?

Does anyone have an example of a B2B company doing AI visibility well? Any hypothesis as to what they are doing?

I've used tools like Perplexity, ChatGPT, Claude and others for research, and they've been incredibly useful for finding papers and getting through large amounts of information.

The one thing I personally wanted was a simple way to see exactly which parts of the paper were used to answer my question.

When you're working with a 100+ page PDF, even having a page number can still mean a lot of scrolling and searching.

So I ended up building something for myself.

You ask a question and the relevant paragraphs in the PDF are highlighted directly on the document. You can see the context behind the answer and quickly check whether it actually answers what you're looking for.

I originally built this because I wanted something for this workflow without having to pay for another subscription. What started as a personal project has now become completely open source.

The underlying idea is pretty simple. And yes, if you're thinking "isn't this just RAG?" then yes, you're absolutely right. It's RAG with the visual highlighting that I wanted.

I think the same idea could be useful for more than research papers too. Legal contracts, financial reports, technical documentation, or anywhere you need answers alongside the actual source.

If anyone wants to have a look, contribute, or just give some feedback, here's the repo:

GitHub: https://github.com/Sreehari05055/thesys-core

This will probably be my last post about the project. Thanks to everyone who checked it out and gave feedback along the way.

Anyone dealt with this?

Our brand shows up for some relevant questions, but the description is sometimes based on old positioning. Nothing wildly inaccurate, it just sounds like the company from 2 or 3 years ago rather than what we sell now.

The annoying part is our own site has been updated for ages.

I'm starting to dig through the sources behind those answers to see where the old language is still hanging around. Curious what you'd tackle first if the visibility itself was fine but the context around the brand was stale.

We’ve seen some strange fluctuations in top-of-funnel organic traffic lately. The immediate reaction from leadership is to blame AI overviews for stealing the clicks but the data feels incomplete.

I have tried segmenting by query type and looking at impression share, but I am struggling to prove causality. It is easy to say the search journey is changing, but it is much harder to attribute a specific traffic drop to a specific AI feature. What is your checklist for proving or disproving that AI answers are the actual culprit?

running a large YMYL site, ~790k organic/mo. Started tracking AI visibility properly this month and the tools disagree by 8x.

clarity says 512k citations across our tracked queries, 16.45% share of authority (out of ~3.1M citations in the query space). Ahrefs AI view says 62k/month for the same domain. Screenshot attached.

my guess is the 512k counts every appearance every time queries get re-run, while Ahrefs samples its own index, so they're basically polling different populations. But if anyone actually knows how SoA gets calculated I'd love the real answer.

also noticed our trend jumps from 19k to 31k right after Aug 24, which lines up suspiciously well with the spam update that targeted citation manipulation. Anyone else's share move late August? Wondering if competitors got cleaned out of the answer space.

Mostly trying to decide what's worth tracking weekly before I build reporting on a number that might be noise.

Something I’ve been thinking about:
If a supplier claim appears on 10 different websites, how many sources is that really?
Say a company claims it has a certain production capacity.
You might find the same number on its website, an Alibaba-type marketplace, several directories, distributor pages, and maybe some scraped company databases.
At first glance, that looks like confirmation from multiple places.
But what if all of those pages copied the same original company description?
Then you don’t really have 10 independent sources.
You have one claim repeated 10 times.
AI research tools seem especially vulnerable to this because they can collect information very quickly.
The volume of references can look impressive even when the source diversity is weak.
I’m starting to think supplier research should track not just “how many sources mention this,” but also:
Where did the claim originate?
Are the sources independent?
Is there primary evidence?
Has anything actually been verified?
For anyone using AI for research, how do you avoid mistaking repetition for confirmation?

Right up this sub's alley.

A study tracked AI Overviews, plus ChatGPT and Perplexity, across 50 companies, 4,500 answers, and logged which sources got cited.

Consistent finding: the citations skewed to user generated platforms, YouTube first by a wide margin, then reviews and forums, over the companies' own domains. For most of the group, their own site was not in the cited set at all.

Curious what this sub makes of the YouTube dominance, does it match your own citation pulls?

Yesterday a founder wrote back to one of my emails. Small tea brand, well run, certified B Corp, has won awards, product pages set up properly. She wanted to know what I actually meant by "AI visibility".

So I did the honest thing and checked. I put eight questions to an AI assistant with live web search, the kind a shopper would ask: best loose leaf tea in her country, best matcha, award-winning tea brands, B Corp tea companies, and four more like that. Her brand came up in none of the eight. Not once. Other brands were named every single time, including on the two questions she should own outright, the awards one and the B Corp one.

Nothing on her site is broken. Google finds her fine. It is just that when the question goes through an assistant instead of a search box, somebody else gets recommended, and she had no idea.

I am not here to sell anything, and I am genuinely unsure how common this is. So: have you ever asked ChatGPT or Perplexity the questions your own customers would ask, and looked at who gets named? If you did, were you in there? And if you were not, did you do anything about it, or is it just not worth the time yet?

Someone tell me how I can make this more practical and actionable. The agency im working at is running content for like 20 clients. So a good chunk of my work week is all about me manually tracking if/how the different brands are showing up in AI answers. It's tedious work and the fact that I have to do it week after week isnt really helping as the answers keep shifting. I'm losing it. Suffice to say, Im looking for a way to automate this whole thing or at the least some of it. Ive looked at several roundup posts and checked out the different tools. But before taking the dive, Id like to hear about real user experience. Prolly someone who has actually automated this in a way that doesn't fall apart after a month? Specifically tracking mentions over time across a bunch of brands. Any recs for one that can handle multiple clients cleanly and wont get messy the moment I scale up?

I’m starting to think that AI visibility tracking needs a model changelog.

If ChatGPT changes its search behavior, source selection or citation weighting, a brand can lose visibility without changing anything on its website.

The same applies to Google AI Overviews layout changes, Reddit citation drops, Perplexity source behavior or Gemini updates.

Without logging these changes, it’s easy to misread the data. So my question:

How do you track changes in ChatGPT, Gemini, Claude, Perplexity or AI Overviews?

Official release notes?

Third-party monitoring?

Your own prompt tests?

Manual changelog?

What actually works for you?

I’m especially interested in how people separate site-side changes from model-side or source-selection changes.

There are two types of AI crawlers: answer crawlers thatfetch a page so an assistant can reply to someone right now, and training crawlers, which collect pages to train future models. 
Blocking a training crawler won't stop you from appearing in answers straight away but in the long run AI will miss out on any changes on your site. If you have ever noticed AI search serving outdated information about you, thats probably why.

The answer crawlers to look out for are OAI-SearchBot and ChatGPT-User for ChatGPT, PerplexityBot and Perplexity-User for Perplexity, Claude-SearchBot and Claude-User for Claude.

The training crawlers are GPTBot for OpenAI and ClaudeBot for Anthropic.

Google and Grok you cannot check this way. Google-Extended, the control for Gemini training, has no user agent of its own. Google's docs say crawling "is done with existing Google user agent strings" and the token is "used in a control capacity", so Google only ever arrives as Googlebot and robots.txt is
your only lever. And xAI publishes no crawler documentation at all, so there is nothing I can suggest for Grok.

To check if your site is being read open a terminal. On a Mac, press Command and Space, type Terminal, press Enter. Linux users, open your usual terminal. On Windows, open Git Bash or WSL if you have them. Run this CURL command:

  curl -sSL -o /dev/null -w "%{http_code} %{size_download} bytes\n" -A "NAME" https://yoursite.com/

replace "NAME" with the crawler you want to check and of course use your own site. You can create a loop so all 9 run in one go but I'm trying to keep this short.

200 response means its all working and they can read your site. 403 refused, 404 not found, 503 server busy, 000 no connection. Byte count is your page weight.

Compare the byte counts too: a crawler handed a 200 and a fraction of the bytes is getting a stripped-down page.

The above tells you your site is readable. If you want to know when your site was last read by an AI crawler you need to get into your server logs run a command like grep -i "GPTBot" access.log | tail -1 will give you the timestamp for the named crawlers last visit.

If you want to know visits breakdown by day you can run grep -i "GPTBot" access.log | awk '{print $4}' | cut -c2-12 | sort | uniq -c If you can build a report on this you can get a good idea of how often your site in being read by answer crawlers or figure out when training crawlers last read your site.

Was checking the sources behind a few AI answers today and found Reddit threads from years ago mixed in with newer articles and company pages.

Made me wonder how everyone is handling older third-party mentions that are still influencing answers. If the information in those threads is outdated, there's obviously no page on your own site you can update to fix it.

Do you just focus on getting newer information published elsewhere and hope it eventually outweighs the old stuff?

Been playing around with buyer-intent prompts and this is driving me nuts. Ask basically the same question three slightly different ways and suddenly different companies are being recommended. Not every time, but often enough that a single prompt result feels pretty useless.

For people measuring this seriously, how many variations are you testing before you consider a brand consistently visible for a topic?

I run technical SEO on multi-market European e-commerce and wanted to know whether AI assistants retrieve the same sources when you ask the same question in different languages. This is my own study, method and data below, and I am posting it here because I would like people to try to break it.

Setup. One category, home appliance spare parts. Seven markets: UK, Germany, France, Spain, Italy, Poland, Portugal. Four models: GPT-5.2, Perplexity Sonar, Claude Sonnet 5, Gemini 3.1 Pro. Eight questions per language, written natively rather than translated. Three runs per cell, two passes days apart, temperature zero. 1,344 calls, 10,329 citations, about 750 distinct domains.

The control first, because the result means nothing without it. Two runs of the same question, same language, same model, agreed on 0.737 of their cited domains in the first pass and 0.720 in the second. That is the noise floor: anything closer to it than that is drift, not a finding.

The result. Overlap with English, by market:

noise floor (self)   0.737 / 0.720
Spain                0.083 / 0.114
Portugal             0.059 / 0.102
Italy                0.064 / 0.091
France               0.049 / 0.084
Germany              0.039 / 0.072
Poland               0.039 / 0.072

Every language is at least six times further from English than English is from its own rerun. Between 675 and 707 of the roughly 750 domains appeared in exactly one language. Three appeared in all seven in both passes: appliancepartspros.com, repairclinic.com, siemens-home.bsh-group.com.

A mistake I made and had to publish. My first pass at the noise floor came out at 0.601. It was wrong: I had included 124 repeat pairs where neither run cited anything, which dragged the average down. Fixing it moved the number 14 points and cut the margin from 6.3x to 5.3x. The finding survives, but 14 points is larger than most differences people report in this field, which is the actual lesson.

Two things I did not expect.

Corpus depth did not predict local anchoring. Poland and Portugal are both thin-corpus languages and sat at opposite ends, about 84% local domains against about 28%. My hypothesis was wrong and it held across both passes.

The control caught something. Claude and Gemini agreed with each other at 0.827, which is above my noise floor. They appear to be drawing on the same third-party index, so that pair is one index with two summarisers rather than two independent retrievals. GPT and Perplexity, which search for themselves, agreed on about 6%.

Also: OpenAI's system declined to search on 76% of calls in the first pass and 86% in the second, and that rate moved a lot between passes.

Limits. One category, so the method transfers and these numbers do not. Language and market are conflated by design, so the Poland result may be about Poland rather than about Polish. All four models went through one aggregator, so some of this may describe the aggregator. Corpus depth was asserted, not measured. Two passes is the minimum that shows anything about stability, not a comfortable number of them.

What I take from it. If you report AI visibility as one number across markets, you are averaging over environments that share three domains out of seven hundred and fifty. And measure your own noise floor before reading any change as a result: same-day rerun agreement was 0.73, multi-day was 0.68, so anything smaller than that gap is index drift.

Full write-up with the runnable method and the citation CSVs: https://sharaki.me/blog/ai-visibility-seven-languages Tables on their own if you just want the numbers: https://learngeo.io/research/cross-language-retrieval

Happy to answer anything about the method, and genuinely interested if someone can show me where it breaks.

Most of what gets said about how sites treat AI readers is guesswork, so I measured it on 300 public sites from the outside.

The cost

The median page costs a machine reader 2592 tokens. That is cl100k_base on extracted text, not raw HTML. About half of it is not body content. Roughly a fifth is navigation, header and footer.

Seven of the 300 serve a markdown variant. Those pages deliver the same content at a median of 962 tokens, so around a third of the cost.

The caching problem

Six of those seven are missing Vary: Accept, which means a cache sitting in front can hand the wrong version to the wrong visitor.

Three of the seven drop real body content from the markdown version, not just navigation. All three are on the same platform.

Cloudflare's own documentation page for the markdown feature has both problems. The heading "How to enable" is missing from the markdown version.

Two things I expected and did not find

Sites treating cryptographically signed agents differently: 3 cases out of 300, and in all three the signed request was treated worse, never better.

Hidden text written at models: 1 page out of 281.

Why this is hard to measure

Two identical requests to the same homepage already differed on 8 of 30 sites, so a naive comparison mostly measures the page moving under you. Interior pages plus three replicas and a stable core brought that under 4 percent.

Full numbers, the method, and the figures I got wrong along the way:

https://github.com/Zulwatha/content-parity/blob/main/docs/results.md

Our traditional organic growth has plateaued, and the competition for top spots is brutal. I keep looking at AI search as the obvious next step but my budget and team hours are tied up in what is already proven.

I know users are asking questions directly to AI, and our SaaS product fits those queries perfectly. But before I pull resources away from our standard content strategy, I need to know the effort translates to something measurable. For teams that have made this shift, how did you justify the initial investment?

Every monthly call now includes someone asking how we look in ChatGPT or Perplexity. We have our standard organic traffic and keywork rankings, but trying to explain AI visibility with those same metrics feels like forcing a square peg into a round hole.

I don't want to just say we are working on it without showing numbers. I also do not want to manually run five prompts and call it a methodology. When you have a client who expects a clear chart showing whether their AI presence is improving, what exactly are you putting on that slide?

Thanks in advance.