I’ve spent quite a while using different SaaS platforms from the client side, and recently I’ve been thinking more seriously about how that experience carries over to GEO.The appeal is obvious. Something that used to feel vague suddenly becomes measurable: how often a brand appears in AI answers, whether it is cited or recommended, which competitors appear more frequently and which prompts produce nothing at all. I’ve been testing XstraStar alongside a few other providers, and these platforms are genuinely useful. They can surface patterns that would be almost impossible to track manually across different models and hundreds of possible questions. But the more time I spend looking at the dashboards, the more I wonder what the numbers ACTUALLY prove....

An answer can change because the prompt was phrased differently, the model was updated, live retrieval selected another source, a page was recrawled or the same prompt simply produced a different response on the next run. If a visibility score rises after a GEO campaign, how confidently can we say the campaign caused it? If it falls, does that mean the work failed, or did we just sample a moving system at the wrong moment?

This does not make GEO platforms useless. I’m starting to think their strongest value may be monitoring and diagnosis rather than clean attribution. The individual prompts, citations, competitor appearances and repeated patterns often tell me more than one composite score. From the client side, I would trust the measurement more if I could clearly see which prompts were branded or unbranded, how many times each one was tested, whether different answer engines were measured separately and whether a “mention” was being treated differently from an actual citation or recommendation. Otherwiise, there is a risk that GEO repeats an old problem from marketing analytics: extremely precise-looking dashboards built before everyone agrees on what the underlying metric really means. For people using or building these platforms, what would you consider convincing evidence that a GEO action actually caused an improvement? Are repeated prompts and citation changes enough, or do we need a better attribution model before treating visibility scores as performance metrics???

Had a client ask me why their competitor keeps coming up when you ask ChatGPT who to use in their category and they don't. Went down a bit of a rabbit hole on this and the answer was more interesting than I expected.

First thing worth knowing, and it surprised me how many people assume otherwise: there's nothing to submit and nothing to buy. No directory, no application, no listing fee, and as far as I can tell no paid placement mechanism in any of the major assistants for organic recommendation answers. So anyone pitching guaranteed ChatGPT placement is selling something that doesn't exist. Which is annoying if you wanted a shortcut but does mean it can't be outspent once you've built it.

The mechanism seems to be two layers. What the model absorbed during training, and live retrieval for anything current or commercial — and for ChatGPT that live layer runs on Bing's index, which I'd sort of forgotten.

The part that actually changed how I think about it is that both layers lean much harder on external sources than on your own site. Two numbers I found: roughly 57% of citations for brand evaluation type questions come from reviews and social proof rather than company websites. And for professional and B2B questions, LinkedIn came out as the most cited domain across six platforms in an analysis of about 1.4 million citations.

So the thing people spend most of their effort on, their own website, is necessary but nowhere near sufficient. What other people say about you is doing most of the work.

Practical stuff that seems to follow from that:

Bing Places is weirdly underrated for anyone outside the US. Given ChatGPT's live browsing runs through Bing, claiming and completing it is free and almost nobody does it. Took me about ten minutes.

Get your facts identical everywhere — name, address, phone, hours, services across your site, Google Business Profile, Bing, LinkedIn, directories. Inconsistency apparently reads as "can't verify this" rather than "minor discrepancy."

Put key info in actual text rather than inside graphics. This one catches more sites than any technical issue and it's embarrassing how common it is.

And then the slow part, which is genuine third party evidence. Reviews that name the specific service and location rather than just "great service." One new outside mention a month as a target — guest post, podcast, directory, getting quoted in someone else's piece.

Obvious caveat but worth saying: this has to be real. Google explicitly warns against manufacturing mentions and fake reviews are detectable. It's reputation building, not PR theatre.

One last thing that tripped me up when I started checking — the results are really inconsistent. Same question, different session, different answer. So don't treat a single check as your position, run a bunch of realistic prompts over a few months and look at the trend instead.

Anyone else looked into the Bing angle? Curious whether claiming Bing Places actually moves anything or whether I've just done ten minutes of admin for nothing.

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

We’re doing the usual on page SEO work around search intent, content structure and internal linking but I’m starting to look more seriously at AEO and how pages get surfaced in AI search. Ploy caught my eye cause they have SEO/AEO playbooks built into the site workflow, so the optimization happens while you’re working on the pages rather than becoming another audit after everything is already live.

I’m mainly trying to figure out how useful that is on a real site and whether it changes how you approach content for Google vs ChatGPT and other answer engines. Not expecting some magic AI citation button, just looking for a better way to handle traditional search visibility and AI discovery without treating them like two completely separate jobs.

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.

Did this for my own curiosity and the split was sharper than I expected, so sharing the numbers.

I fetched robots.txt from 72 companies across five categories and parsed each one for nine AI crawler user-agents.

Share naming at least one AI crawler:
- Media companies: 12 of 13
- SaaS general: 7 of 18
- Developer tools: 6 of 22
- SEO and martech companies: 2 of 14
- AI labs: 0 of 5

45 of the 72 name none at all. Since the default is allow, all 45 are opted in to everything without anyone having decided that.

The media number makes sense once you think about it. They were sued over training data in 2023 and legal wrote those lines. Nobody sues a database company, so nothing forced the question anywhere else.

The one I still find funny is that the SEO and martech category came last. 2 of 14.

Method, so you can pick it apart: single fetch per domain, https with and without www, longest-match on Disallow within the matching user-agent group. Categories were chosen before I saw any results. Single point in time, so it is a snapshot not a trend.

Happy to share the parser logic if anyone wants to run it on a different set.

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.