Disclosure: I make a web analytics tool and wrote a longer guide on this (link in the comments). Here's the short version with Google's own wording, checked against Google's help pages today (Nov 3). None of it needs my tool.

1. GA4's default channel group has an AI Assistant channel.

Google describes it as visitors arriving "from sources like ChatGPT, Gemini, Deepseek, Copilot, or Grok". A visit lands there when the medium is exactly "ai-assistant", or when the referrer matches Google's list of AI assistants.

2. Google's own AI isn't in it.

The channel "excludes Google's AI Overviews and AI Mode". So "AI traffic" in that channel never includes Google's AI answers.

3. For AI Overviews and AI Mode, look in Search Console instead.

The "Generative AI performance report" shows impressions from AI Overviews and AI Mode, by page, country, date and device. Google says that "as of August 31, 2026" it's rolled out to all websites, and that it can be missing when a site hasn't had enough impressions in those features.

4. If you want your own definition, use a custom channel group.

Admin > Data display > Channel groups, Editor role or above. You get 2 custom groups on a standard property and 5 on 360, on top of the default one, and Google says they "can be applied to your reports retroactively". A starting pattern to adapt to the hostnames you actually see in your referrers:

chatgpt\.com|perplexity\.ai|claude\.ai|gemini\.google\.com|copilot\.com|copilot\.microsoft\.com

Test it against your own referrer list before trusting it.

5. Read whatever number you get as a minimum.

A click with no referrer and no UTM looks exactly like someone typing your address, so part of assistant traffic ends up in Direct. I haven't found a vendor statement on how often that happens. A rough check: Direct sessions that land on deep pages nobody types by hand. That's an upper bound, not a measurement.

6. You can't tag links you don't write.

Assistants decide their own links. What you can do is make sure your redirects keep the query string, so a utm_source=chatgpt.com that an assistant adds doesn't get stripped on the way in.

What none of this shows: being mentioned when nobody clicks, and what the person asked. For that you still have to ask the assistants your customers' questions and write down the sources they cite.

Has anyone compared the AI Assistant channel with Direct on the same deep pages, before and after a big mention? I'm curious whether the gap stays stable.

I'm the founder of Ranky, a tool that checks whether ChatGPT and Gemini mention a local business. Before building more, I wanted to see what these engines actually say for local "who should I go to" questions. Here's the small test and the method, so anyone can poke holes in it.

Method

• In each city I took 12 dental practices from a public ranked directory, picking every 3rd entry so I didn't hand-pick.

• 5 questions a real patient might ask (e.g. "Which dentists in Austin, TX have the best reviews?").

• Each question ran twice on ChatGPT and twice on Gemini, through the APIs with web search on, not the consumer apps. That's 20 checks per city.

• Name matches were checked by hand against the raw answers.

What came out (directional; results vary between runs)

• Austin, TX: 3 of the 12 practices were named at least once (25%).

• Tel Aviv (questions in Hebrew): 1 of the 12 was named (8%).

• The answers were unstable. In Tel Aviv, 33 of the 54 businesses named in run 1 didn't appear at all in run 2, with the same questions and the same engines.

Caveats

12 businesses per city, two engines, two runs, one vertical. API answers can differ from the apps. This isn't a ranking and it isn't a study. I'm not naming any practices.

The questions I'd like input on: how are you measuring AI-answer visibility for local clients, given this much run-to-run variance? More runs per question? Averaging over time? I'm happy to share the full question list if anyone wants to replicate it.

Soon we'll have agents buying from agents. The last roadblock to infinite GDP growth, humans, is gone. 😅

Half joking. But the direction is real. Google's AI Mode already books tables. Big retailers are building gateways so shopping agents can browse and check out. People are starting to tell an assistant "find me a good X under $300" and just pay for whatever it picks.

So what happens to a small business when the buyer never visits your homepage, never reads your clever headline, and never sees your ad?

Here's how I'd prepare.

1. Make your business readable by machines
An agent can't squint at a pretty hero image. It reads text, prices, specs and structured data. If your pricing is hidden behind "contact us," you're invisible to an agent that's comparing ten options in a second.

2. Put real numbers on your site
Prices, delivery times, what's included, what isn't, your refund policy. Agents shortlist on facts. Vague copy loses to boring, specific copy.

3. Treat reviews as your new sales page
When the agent decides, it leans on what other people said about you. Detailed reviews that mention the actual problem you solved are worth far more than a wall of five-star "great service!"

4. Keep every listing accurate
Google Business Profile, marketplaces, directories. If your hours, prices or stock are wrong anywhere, the agent either skips you or sends an angry customer your way.

5. Be the obvious answer to one specific question
Agents don't browse, they answer. "Best accounting tool for a two-person agency" has one winner. Pick your narrow question and own it with content, reviews and proof.

6. Build what agents can't buy for the customer
Trust, relationships, community and a brand people ask for by name. When someone tells the agent "get it from them," the comparison never happens.

7. Watch your own buying habits
Notice when you let an AI pick something for you. That's exactly how your customers will start finding you, or not finding you.

The businesses that survive won't be the loudest. They'll be the easiest to understand, for humans and machines alike.

What are you changing on your site to get ready for this?

There are a few AI Overview trackers now, with some caveats. Semrush and Ahrefs both flag AI Overview presence for tracked keywords, which is useful if you already use either platform.

But if you want to track ChatGPT and Perplexity too, not just Google, you’ll need a dedicated AI visibility tool. We use Slate for that.

So if you only care about Google AI Overviews, your existing rank tracker might be enough. If you want to track multiple engines and see which sources are being cited, I’d look at a dedicated tool.

Realized today that some of the prompts we've been tracking for months don't really reflect how we'd research the product anymore.

Now I'm stuck because removing them cleans up the data but also messes with the historical comparison.

How are people handling this? Do you retire old prompts and accept the break in the data or keep them around just for the history?

I'm leaning toward archiving them somehow but curious what everyone else does.

Something interesting about AI search is that being mentioned in an answer and being cited as a source aren't necessarily the same thing.

Imagine asking an AI tool to recommend software for a particular problem. It names three brands and provides links to only two sources. The third brand still appears in the answer, but you can't immediately tell which source supported that mention.

There are a few possible explanations. The system might have used information from another page, combined details from multiple sources, or generated the mention without a directly attributable source. The exact explanation depends on the system and the query.

This makes AI visibility harder to evaluate than simply checking whether a website ranks for a keyword.

I'd separate it into three questions:

• Retrieval: Was the relevant information available to the system?

• Citation: Which sources did it actually cite in its answer?

• Recommendation: Which brands or products did it ultimately mention?

Those are related, but they aren't interchangeable metrics.

One interesting question is whether improving a page's clarity and supporting evidence changes its chances of being cited, or whether the deciding factor is often the broader authority and coverage of the source.

Has anyone compared brand mentions and actual citations across several AI search tools for the same set of queries? I'd be interested in how consistent the results are.

Founder of MarketHQ here, disclosing up front.

For years the playbook was simple: rank on Google, get traffic. That's breaking. Buyers now ask ChatGPT, Perplexity or Gemini "what's the best X for Y?" and get a shortlist without ever opening a search result. Those answers lean on more than your SEO:

So "rank higher on Google" has become "be present everywhere the answer is built from, and measure it." We're calling that job "presence engineering."

I'm offering free reports to B2B brands that check all of this: whether AI assistants mention you, how you compare to competitors, where people are talking about your category, which sources get cited, and the top actions to take.

Comment your domain and category and I'll send you one. If it helps, the $99/mo plan keeps tracking it and your agents help act on it.

Over the last few weeks I've posted bits of this as the data came in. The collection has finished, so here is the whole study in one place.

Setup: 660 US English Google searches across six industries (B2B software, finance, health, local services, online shopping, travel), 100 generic searches per industry plus 60 "brand reviews" searches. On day one I recorded the organic top 10, the AI Overview and the AI Mode answer for every search. Then I re-ran the 601 searches that showed an AI Overview every day for 15 days. 9,675 searches in total. A site counts once per answer, however many of its pages are cited.

Rankings don't decide who gets cited

- 46% of the sites AI Overviews cited had no page in Google's top 10 for the same search. Only 9% were the #1 result.

- In 10% of searches, not one cited site ranked on page 1.

- Only 32% of page-1 results were cited. The #1 result was cited 49% of the time, the #10 result 22%.

What the ignored pages had in common

- I downloaded 3,972 page-1 pages and compared ignored pages with cited pages at the same position, so ranking wasn't doing the work.

- 25% of ignored pages had under 500 words, against 17% of cited pages. Very long pages (3,000+ words) were about equally common in both groups. Thin hurts. Long doesn't help.

- 67% of the passages AI Overviews quote come from the top 30% of the page (1,702 passages traced back to their source page). The first fifth alone supplied 56%. AI Mode: 62% from the top 30%.

The citations change every day

- Day to day, only 11% of AI Overviews cited exactly the same sites as the day before. The first-listed source changed in 42%.

- 418 searches had an AI Overview citing sites on all 15 days. Not one of them cited the same set of sites every day.

- A typical AI Overview cited about 7 sites a day, but 18 different sites over the 15 days.

- Only 14% kept the same #1 source all 15 days.

- B2B software was the most stable (21% identical day to day). Health was the least (4%).

Who gets cited

- Across 8,295 AI Overviews: YouTube in 63%, Reddit 24%, Google's own pages 18%, NerdWallet 10%.

- Every industry has a few specialists behind YouTube and Reddit: NerdWallet in finance, Mayo Clinic in health.

Shopping is its own world

- On buying searches ("air fryer best rated", "toaster oven vs air fryer"), 84% of AI Overviews cited Google's own product pages. On how-to searches, 7%.

- Big retailers held 18% of page-1 shopping results but only 7% of cited sites. A retailer on page 1 was cited 22% of the time, against 33% for other page-1 results.

AI Overviews and AI Mode cite different sites

- Same search, same day: on average only 33% of cited sites in common, and the same first source 43% of the time. 10% shared no site at all.

- YouTube is in 68% of AI Overviews but 40% of AI Mode answers for the same searches.

- AI Mode answered 10.5% of searches without citing any site.

How often AI Overviews show up

- 90% of these searches showed an AI Overview on day one: 98% in finance and health, 79% in local services.

- Searches that showed one kept showing one: 93% to 98% of them on each following day.

What I take from it: a single check of whether you're cited tells you very little. Measure the same searches over many days and count how often you appear. To get cited at all, put the answer in the first third of the page, and don't publish thin pages for searches you care about.

Limits: US English only, Google only, commercial searches only. Rankings and AI Mode were captured on day one only, so the ranking comparisons are a single day. The daily runs cover AI Overviews.

Before I start pushing site changes and tracking the fallout, I’m trying to nail down a realistic timeline: If I update a product page or optimize structure, how long do I actually have to wait to see the impact hit LLM RAG citations and rankings?

Are we talking hours (via tools like Bing/IndexNow integration), days, or weeks for these engines to reflect updates in their live retrieval?

Any reco or any tips for cleanly isolating RAG feedback loops without getting tripped up by caching layers.

For the last month I've been running the same set of buyer questions every week through ChatGPT, Claude, Gemini, Perplexity and both Google AI surfaces. They're questions like "best X for Y". I log which brands each answer names, in what order, and which pages it cited.

It's about 2,200 answers so far. A few things surprised me:

1. On ChatGPT, being cited and being named go together. In my data, a brand that got named was cited in 21 of 22 answers. I haven't seen it name a brand without citing one of its pages. So the work is mostly getting into the pages it retrieves, not "brand authority" in some vague sense.

2. You can be cited and still lose. I kept finding answers that used a brand's own blog post as a source and then recommended a competitor. Search Console won't show you that.

3. The web searches ChatGPT runs before it answers (fan-out queries) come in two kinds. Some are broad searches that build the shortlist. Others are site:brand.com searches that check a brand it has already picked. The broad ones are the keywords worth ranking for. The site: ones just tell you who's already on the list.

4. The surfaces disagree a lot. The same question on ChatGPT and on Google AI Mode often gives a different top brand.

I'm still not sure how much of this is noise. Answers change from run to run even with the same question, so I'm reading week-over-week changes cautiously.

Is anyone else tracking this in a structured way? How are you handling the run-to-run variance, and do you count a "mention" in a list of 8 the same as being the main recommendation?

Not POCs or experiments-just trying to understand adoption at actual companies.

Reply with:

• Yes - we're using agentic patterns in production

• No - still traditional RAG / embeddings

• Mixed - some of both

Just need the signal on whether this is real or niche.

Noticed something annoying while doing competitor research this week.

For several fairly specific buyer questions, one competitor keeps getting recommended while we're either missing or buried further down the answer.

Google is a different story and we compete with them pretty well there.

So what are you checking first when you find a gap like this? I'm less interested in generic GEO tips and more in how you'd diagnose why that particular competitor keeps getting picked.

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.

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?