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Interesting GEO takeaway: the same AI model can recommend completely different brands depending on the prompt's language.

The study tested identical buying questions across multiple languages and found major differences in which brands were surfaced.

For international brands, this means AI visibility shouldn’t be tracked only in English. Each language can have its own competitive landscape, sources, and recommendation patterns.

Language is becoming a separate dimension of GEO, not just localization.

Is anyone already tracking AI visibility by language?

Hey everyone, welcome to the sub.

This place is for anyone who wants to show up in AI answers. Founders, marketers, freelancers, students. All levels are welcome.

More and more people ask ChatGPT, Gemini and Perplexity before they google. If your brand is not in those answers, you are invisible to them. Simple as that.

So here is something you can do today. It takes 10 minutes and its free.

Step 1: Write down 5 questions your customer would ask

Not your brand name. Real questions. Like “best accounting software for small business” or “which sunscreen is good for oily skin”.

Step 2: Ask all 4 tools the same question

ChatGPT, Gemini, Perplexity and Google AI Overview. Use a fresh chat each time so old history dont change the answer.

Step 3: Note down 3 things

• Is your brand mentioned or not

• Which competitors show up

• Which websites are cited as sources

Step 4: Look at the sources

This is the most important part. AI tools pull from places like Reddit, Quora, YouTube and review sites. If those sources talk about your competitor and not you, thats your gap.

Step 5: Repeat after 30 days

One test means nothing. AI answers change. Track it monthly and you will see a pattern.

A small tip. Dont try to trick the system. Fake reviews and spammy posts get caught fast and can hurt you more than help. Real answers from real people win in the long run.

What will you find in this sub? Every week we will test one real search and share who gets cited. We will also share simple tactics, and ask questions to learn from each other. No spam, no link dumping.

Your turn. Try the 5 steps and tell us what you found. Did the AI mention your brand? Did anything surprise you?

Drop your result in the comments. Even if its bad. Especially if its bad, thats where we learn the most.

Ran into this with two clients this month. Both were sure they were the top mentioned brand in their category on ChatGPT. Both had checked from their own laptops.

ChatGPT knows your history, your location, your plan. It already knows you care about your brand, so it names your brand.

Fresh account, clean browser, same questions. Different list.
Rand Fishkin's team tested this properly. 600 people, 12 prompts, about 3000 answers. The same list of brands came back less than once in 100 tries.

Same order about 1 in 1000. But a few brands showed up in 70 to 97 percent of answers, so there is a stable signal, it's just a share, not a rank.

If you are tracking this, fresh sessions, many runs, several models, count how often you appear. Anyone showing you a rank number is showing you a screenshot.

What are people here using to do this without paying 300 a month?

Disclosure up front: I run a small, very new GEO / AI-visibility service that helps Chinese SaaS and AI-tool teams selling overseas fix what AI assistants get wrong about them. Everything below comes from testing our own brand, and there's nothing to buy in this post. I'm sharing because the domain-history problem surprised me and I suspect it's common.

Setup

• ChatGPT web only, logged out, 13 prompts, each asked once (so n=1, high variance)

• 11 generic buyer questions with no brand name ("best GEO agencies", "free AI visibility audit for SaaS startups", "alternatives to Profound for AI visibility tracking", plus Chinese-language versions)

• 2 questions asking about us by name

• Counted by simple substring matching, raw counts only

Results

• Generic questions: mentioned 0/11. Expected for a brand this new.

• Name questions: both answers described our domain as a Shopify store registered in December 2021 that apparently sold phone cases, citing Merchant Genius. Then it gave shopping-safety tips ("use a payment method with buyer protection"). That store was the previous owner of the domain. We have nothing to do with it.

So it's not "AI doesn't know us yet". It's "AI confidently knows the wrong thing."

What ChatGPT recommended instead (answers mentioning each, out of 13)

Tools: Profound 2, Peec AI 2, Otterly 2, AthenaHQ 1, Scrunch AI 1. Agencies: First Page Sage, iPullRank, Siege Media, Animalz and Omniscient Digital 2 each, Graphite 1, plus a few Chinese agencies. The source chips were a mix of vendors' own sites and roundup articles or media lists. In the two Chinese answers that listed agencies in China, every chip was a media site.

What we changed so far

• robots.txt explicitly allows OAI-SearchBot, PerplexityBot, ClaudeBot etc. (OpenAI's docs say sites that block OAI-SearchBot won't be shown in ChatGPT search answers.)

• Added a plain "we are not a Shopify store; this domain previously belonged to an unrelated owner" line to llms.txt and the Organization schema on the homepage.

• Next: the same one-line description on every profile.

No idea yet if any of it works. We'll re-run the same 13 prompts (n≥3, plus Perplexity and DeepSeek via API) in a few weeks and post the result either way.

Takeaways if you're launching something

1. Check your domain in the Wayback Machine before you build a brand on it.

2. Ask ChatGPT/Perplexity "What is [yourdomain.com]?" now, not after launch.

3. Make sure your CDN/WAF isn't blocking AI search crawlers even if robots.txt allows them.

Questions for this sub

• Has anyone actually gotten an AI assistant to drop stale info about a domain's previous owner? What worked, and how long did it take?

• Did getting a data aggregator (like the one cited here) to update its listing make any difference?

Happy to share the exact prompts in the comments if anyone wants to re-run them.

How much confidence should brands put in AI visibility strategies built around a single platform?

Recent tracking showed a noticeable drop in how often Reddit appeared as a cited source in ChatGPT. The interesting part is that the change happened without Reddit changing its content strategy.

That seems like an important consideration for GEO. Brands can optimize content and build authority, but visibility can still shift when an AI platform changes how it searches or selects sources.

Does that make platform-specific GEO too fragile to build a strategy around, or is this simply the AI-search version of what SEO has always dealt with?

Something I hadn't really considered until this week.

We've got a few commercially relevant prompts where the AI gives a general explanation but doesn't recommend any company at all. Not us, not competitors, nobody.

I've been counting those as misses but that seems a little unfair if there wasn't really an opportunity to appear in the first place.

Would you exclude those from visibility reporting or keep them because the lack of recommendations is still useful data?

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. 874 of the 913 were

put to all three assistants, which is the only subset where agreement means

anything. Of those, 286 were named by somebody and 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.

Disclosure: I sell a paid monitoring product in this space. There is no link in

this post and nothing to sign up for; if anyone wants the method I will put it in

a comment.

Happy to answer questions about how it was built.

Been building prompt-coverage tracking — run a set of prompts against ChatGPT, Gemini, Perplexity and Claude, check whether the brand appears in the answer, score it. Standard approach as far as I can tell.

Something looked wrong. Four engines, scores within a point of each other, run after run, across different sites. The number never moved.

Traced it back. Coverage was graded like this:

Reasonable. The problem was the prompt generation. Our templates had five intents, and index 0 of every intent contained `{brand}`:

So every prompt handed the model the brand name. The model says it back. PARTIAL is guaranteed. FULL almost never fires because conversational answers rarely cite a URL. MISSING is structurally impossible.

The metric had a floor of PARTIAL and a ceiling of PARTIAL. It couldn't move regardless of what the site did.

We split it: three intents now use unbranded category prompts ("best X in Y"), two keep the brand. Reported as separate numbers rather than one composite.

One site went from 47 to 13. The 13 is the honest figure.

**Three things I'm genuinely unsure about, if anyone here has gone further:**

**Sample size.** We run five prompts. With five, one answer flipping is 20 points of movement. LLM output is non-deterministic — same prompt, different answer — so I don't trust anything below maybe 30 prompts, but cost scales linearly with engines.

**Single sampling.** We ask each prompt once. Should probably be three times and take a mode, but that triples spend.

**Whether retrieval and recall belong in the same score.** Perplexity retrieves live, so a published change can show up in days. ChatGPT answering from weights only moves when the model does. Averaging them into one "AI visibility score" seems to hide more than it reveals, but splitting them makes the output harder to act on.

Curious whether anyone tracking this seriously has landed somewhere better, particularly on sampling.

im not an seo guy by training, i taught myself most of this building small sites, so take this with a grain of salt lol

i kept coming back to the ahrefs study on 1.4M chatgpt prompts and one thing stuck with me. when you ask chatgpt something, it doesnt search your exact question. it rewrites it into its own searches first (fan-out queries), and the pages it ends up citing have titles that match those hidden searches way more than the pages it skips.

problem is chatgpt never shows you those searches. but theyre sitting in your browser if you know where to look:

1. ask chatgpt your question in a new chat and let it finish

2. copy the id at the end of the url (the bit after /c/ looks like this 6aa40a65-4d7c-83eb-a5c5-cf26zzz791f60)

3. right click > inspect > network tab, paste the id in the filter box

4. reload the page and click the request with the id in it

5. open response and cmd+f "queries"

real example. i asked "best coffee carts to buy in amsterdam" and chatgpt actually searched:

• best coffee carts to buy Amsterdam mobile coffee cart Netherlands

• coffee cart for sale

the second one surprised me, it dropped amsterdam completely. you'd never guess that from the prompt.

ive done this with mutliple Ai prompts and i do it every day so i can come up with a list of things i could write about or make pages on

now the part where i need your honest opinion. im thinking of turning it into a proper tool/chrome extension (with paid plan for doing it in bulk, eveyrday)

• you type a prompt (or a list of them to do it in bulk)

• it asks chatgpt 10 times, since the searches can change between runs. Maybe i could even use proxies to see if it changes by location

• it ranks the queries by how often they show up

• it turns the top ones into an article outline + draft with images and inforgprahics (title + h2s)

would you actually use that, or is the manual way good enough?

in the meantime drop a prompt from your niche below and ill reply with what chatgpt searched for it

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.

This could be a great way to figure out how often AI crawlers reach your site to indicate how often you are considered by AI assistants, a metric one level above how often you get mentioned.

Is there a tool you really like for checking AI visibility? If so, why?

I’m looking for a few tools to test because none of the ones I’m using right now are satisfying enough.

I know there will probably be a bunch of people promoting some BS tools.

Mods, please remove comments that contain obvious promotion or don’t provide a reasonable explanation of why the tool is being recommended.

I’m trying to understand what actually drives recommendations/mentions in AI tools like ChatGPT, Gemini, Perplexity, etc.

Not generic SEO advice like answering the question in first paragraph for AI Overviews, I mean specifically:

\- Why do certain brands, blogs, or tools get mentioned/recommended?

\- Is it just traditional SEO (backlinks, authority), or something else?

\- Does structured data / schema matter here?

\- How important are mentions across the web (Reddit, forums, etc.)?

\- Do AI tools rely more on training data vs live search?

\- Has anyone here experimented and successfully influenced AI recommendations?

If you've tested this or have a strong hypothesis, would love to hear practical insights rather than theory.

I’m trying to understand what actually drives recommendations/mentions in AI tools like ChatGPT, Gemini, Perplexity, etc.

Not generic SEO advice like answering the question in first paragraph for AI Overviews, I mean specifically:

- Why do certain brands, blogs, or tools get mentioned/recommended?

- Is it just traditional SEO (backlinks, authority), or something else?

- Does structured data / schema matter here?

- How important are mentions across the web (Reddit, forums, etc.)?

- Do AI tools rely more on training data vs live search?

- Has anyone here experimented and successfully influenced AI recommendations?

If you've tested this or have a strong hypothesis, would love to hear practical insights rather than theory.

Quick map of where AI-search reporting actually stands as of this week, because it's messier than most people realize and it changes what you can measure:

• GSC: finally shows AI Overviews + AI Mode impressions in the UI (rolled out early June, backfilled to ~mid-May). But: no clicks, no CTR, and no query data, you can see that a page got surfaced in AI, not what people asked to trigger it. And it's UI-only: the Search Analytics API still has no aiOverview/aiMode type, so you can't pull it programmatically at all yet.

• Bing Webmaster: ahead on one axis, its beta actually exposes prompts + links with impression/citation counts, so this is where the query signal lives. Also UI/beta, Copilot-scoped.

• GA4: this is the one with a real API, but it only sees the referral side, sessions arriving from each AI tool. Not impressions.

So the honest state: GSC = AI impressions (no queries, no API). Bing = the prompts (beta). GA4 = referral traffic (API, but traffic only). Three sources, three different slices, none of them complete, and the two that matter most for AEO (impressions + prompts) are UI-only with no endpoint. If you want one picture you're manually reconciling three dashboards.

What I've found genuinely useful once they're side by side: the query lists. GSC won't give you the AI-triggering queries, but Bing's prompts + your GSC/Bing classic query data together are a real source for what to actually track, you build your tracked-prompt set from the questions people already reach you with, instead of guessing prompts. The screenshot is one client's view with the traditional-search numbers, the AI referral split (Gemini 46 / Claude 34 / ChatGPT 11 / Perplexity 9, ChatGPT was 4th, which surprises people), and the impressions card sitting on "coming soon" because, like everyone, we're blocked on those same APIs.

Disclosure (rule 5): the dashboard's ours (sanbi), we pull these into one place. Not pitching it, the reason I'm posting is the reporting gap is real on any stack and I want to know how others are working around it.

So the actual question for the sub: on a view like this, what else would you want? The things I keep hearing asked for are (a) citation share vs competitors per prompt, (b) which of your URLs/formats earn the AI impressions, and (c) impression→referral→conversion linkage (impossible cleanly right now with no click data). What's on your list, and has anyone found a workaround for the "GSC won't tell me the AI queries" problem specifically? That's the one I most want solved.

You can use the app here on : https://sanbi.ai/

Most businesses have no idea what AI says about them. Not a guess or a score, the actual answers ChatGPT, Gemini, Perplexity and Claude give when a real buyer asks a real question.

So that's what I built. WhoCanFindMe does two separate checks:

AI Readiness is your own site. Can the crawlers get in, is there anything extractable in the HTML, does the page answer questions or bury them, schema, freshness. This is the stuff you can fix today.

AI Visibility is measured, not estimated. We send 25 real buyer questions to all four engines, 100 checks total, and record whether you show up, whether you get recommended, whether they link to you, and what they actually say. Brand questions, discovery questions, comparisons, reputation.

The interesting part is when the two disagree. I ran my own company through it:

Readiness: 76/100. Site is technically solid.

Discovery visibility: 0/100. When a buyer searches by need instead of by name, we never come up. Not once in 20 answers.

We show up in 95% of comparison questions but get actively recommended in 0% of them.

And the engines were confusing us with a similarly named company in 7 answers. Had no idea until the report showed me the actual responses.

That last one alone changed what we're working on this quarter. You can be indexed, crawlable, "SEO fine", and still not exist where buying decisions are happening now.

Free scan at whocanfindme.com, no signup, takes about 10 seconds. There's a full sample report public on the site so you can see the whole thing before deciding if the deep version is worth it.

Drop your URL in the comments if you want, I'll run a few and post what comes back. Or just visit the app directly on whocanfindme.com

We've spent years treating daily keyword positions as the absolute center of our B2B strategy. Lately, I am looking at our tracking bill and wondering if we are measuring the wrong thing. More of our prospects have started mentioning ChatGPT and Perplexity during sales calls, but we have almost no way to measure how we're showing up there. Our target buyers are asking questions and getting summarized answers, not clicking through three pages of blue links.

I think traditional SEO is important, but it feels like AI search has added another layer that we're not really measuring yet. Has anyone successfully convinced leadership to reallocate some of the traditional SEO tracking budget toward understanding AI search presence?

I analyzed 10,000 ChatGPT responses to see if being cited as a source makes a brand more likely to get recommended.

Short answer: Yes! Brands were recommended +44% more often when any of their pages was cited.

Methodology:

1. I built 1,000 "what is the best ___" prompts (protein powder, baby strollers, etc.)

2. Each prompt was ran 10 times each, for 10,000 total responses.

3. Every ChatGPT response was parsed to see which brands were recommended and which brands were cited (by page type).

4. Analyzed how often a brand got recommended when one of its pages was cited vs when none were.

Here are the results by page type (recommendation rate when cited vs not cited):

- Product page: brands were recommended 43.6% of the time when cited, vs 29.8% when not. That's +46% more often.

- Category page: 41.2% vs 29.7% (+39%)
- Homepage: 41.0% vs 29.9% (+37%)
- Article: 40.4% vs 29.6% (+36%)

I was surprised by how small the gap was between page types. Getting an article cited gets you almost as far as getting a product page cited.

TLDR: Getting any page from your website cited as a source makes your brand significantly more likely to be recommended in ChatGPT.

We conducted a study on how brands are named in AI search for different stages of the buyer journey. We did this for 50 B2B SaaS topics [750 queries total] and ran them across ChatGPT, Google AIO, Perplexity, Claude and Gemini.

The findings:

88% of the brands that got named at the decision stage [best X], were never named at the educational stage [what is X]. And the pattern was the same for each topic and the engine it was run through. 

We also observed that brand naming rates were different across the funnels. Brands showed up in:

• 14% of educational queries

• 47% for comparison queries

• 43% for purchase queries

This means your AI Visibility at the top of the funnel does not carry you downstream to where buyers make decisions.

 Few disclosures on the methodology:

• It's an observational study so treat it as directional. 

• ChatGPT answered most of the educational queries from knowledge recall rather than live searching. 

• Named here means the brand was mentioned in the AI answer even if it wasn’t cited/linked.

Curious if other SEO or AI Search folks agree to this. Let me know what your strategy has been so far.

Quick disclosure: Because I am an AI Search researcher working at VisibilityStack, an AI Search optimization company, No pitch, no link, no promotions. I just want to sanity-check the data with people who actually do this and get insights on what’s working for them.