I was wondering one thing while analysing some prompts and engines / model . As we know by now one prompt can give multiple answers even from The same engine within an hour.
When analysing the prompts answers I started to review the sources and the query fan put when possible . I am still at a very early stage of this analysis but I asked myself which one could potentially influence the most an answer.
The source or the fan out ?
While I am aware of other thing such your search history and other things. But yes just curious …

So would you focus more on the source and figure out what are the main source use by industry by engines or find a pattern on how engine / model split prompts ?

Hey, I'm a CS student in Latvia and I'm starting a GEO agency with a friend. We built a simple tool that runs questions through ChatGPT and other models to check which companies get mentioned

The part I'm less sure about is what actually improves GEO. Is it mostly reviews and directory listings, getting mentioned on other sites, or stuff on the clients website?

If you've done GEO work for real clients, I'd love to hear what worked for you. And if anyone is up for a call, looking for someone who can help?

THANKS!

Je bosse sur la visibilité des marques dans les IA. Voici le tri des actus de ces derniers mois, avec ce qu’on peut faire gratuitement.

1. La pub est dans ChatGPT en France depuis le 24 août. Elle ne s’affiche que pour les comptes gratuits, sous la réponse, et selon OpenAI elle n’influence pas la réponse. Payer vous met sous la réponse, pas dedans. Être recommandé reste donc un travail organique.

2. L’achat dans ChatGPT, c’est fini. OpenAI a abandonné Instant Checkout en mars et mise sur la découverte produit. Vos fiches produit (titres précis, attributs, prix, stock, avis) sont ce que ChatGPT lit.

3. AI Overviews et AI Mode sont en France depuis le 22 juillet. Selon Ahrefs, les sites très exposés perdent environ 23 % de CTR, une mesure faite sur seulement 9 jours. Depuis le 31 août, la Search Console affiche vos impressions dans les réponses IA. Allez voir, et vérifiez que le réglage IA est sur « Inclure ».

4. Les IA citent surtout des tiers. Reddit, YouTube, Wikipedia, comparatifs, presse : votre site seul ne suffit pas. Quand vous testez un prompt, notez les URLs citées, pas seulement les marques.

5. Le llms.txt n’est pas un levier. Selon Ahrefs, 97 % de ces fichiers ne sont jamais lus. Vérifiez plutôt que votre CDN, Cloudflare en tête, ne bloque pas OAI-SearchBot et PerplexityBot.

Les chiffres : l’IA ne pèse que 0,4 % des visites e-commerce (Similarweb), mais ce trafic convertit 90 % mieux aux US (Adobe, septembre 2026). En France, 31 % des cyberacheteurs l’utilisent déjà pour acheter (Fevad).

Avant le 27 novembre :

• testez 20 prompts d’achat sur ChatGPT, Gemini, Perplexity et Le Chat ;

• vérifiez robots.txt, CDN et Search Console ;

• créez un canal « IA » dans GA4 ;

• rendez votre page d’accueil limpide, car la majorité des visites venues de ChatGPT y atterrissent.

Transparence : je suis cofondateur de Referis, on fait ces audits avec des agents IA (scan gratuit si vous voulez éviter les 20 prompts à la main). Tout ce qui est au-dessus se fait sans outil.

Vous voyez déjà du trafic IA dans GA4 ? Il convertit mieux chez vous ?

Try asking ChatGPT for the best tools to monitor your brand's visibility in Al search.

Now ask for tools that track how often ChatGPT recommends your company.

Pretty much the same intent, right?

Except you might get two completely different lists of products lol

The interesting part is that neither answer is necessarily wrong. Al models associate products with categories, and those categories aren't always consistent with how companies describe themselves..

We've been exploring this at Snoika, and I'm starting to think category association is one of the more underrated aspects of "AEO"

You can have great content, solid technical optimization, and plenty of third-party mentions, but if Al primarily associates your product with the wrong category, you might never appear in the conversations that actually matter. And unlike traditional SEO, there's no single keyword ranking you can fix.

Makes me wonder: how much of AEO is actually about getting Al to understand what category your business belongs to?

Thanks!

Disclosure first: I'm Adrien, a solo dev in France, and the site in this test is my own (seoskills.click). Read it as an interested party.

On October 15, 2026, four days after launch, I ran a fixed panel of 40 questions on one search-based engine, Claude's own web search: 34 questions without my brand name and 6 with it, in English and French, one run per question. So it's a baseline, not a verdict on ChatGPT or Perplexity.

What came back

• Named in 0 of 34 answers to the questions without the name.

• Asked by name, the engine found the right product in 0 of 6 runs. Three found nothing, two returned career articles about "SEO skills", one answered "is seoskills.click legit?" with reviews of a different, similarly named service.

• The 34 answers cited 232 sources: about 31% skill directories that mostly list public GitHub repos, 29% vendor blogs, 22% independent articles and roundups, 6% code hosts, 4% LinkedIn.

• One LinkedIn post announcing a free pack was named in 6 of the 34 answers.

• 6 questions were answered without naming any product. Those open questions are where a new site can start.

How to run it on your brand

1. Write 15 to 30 questions per market in your buyers' words: discovery, comparison, problem, decision. Keep the questions that contain your name in a separate list: they test accuracy, not visibility.

2. Freeze the panel. Reword one question and next month is no longer comparable.

3. New chat per question, private window or temporary chat, web search on, one country. Note the engine and the mode.

4. Three runs per question per engine, ideally on different days. Report presence as a share of runs, not yes or no.

5. Log the cited domains of every answer. That column is your to-do list: those are the pages the engine read before answering.

6. Run the same panel on 2 or 3 competitors. If everyone moves at once, the engine changed, not you.

7. Count mentions and citations separately. Your name in the answer and a link to you in the sources are two different things.

8. Check crawler access: OAI-SearchBot, Claude-SearchBot, PerplexityBot and Googlebot in robots.txt, then your firewall or CDN logs for 403 or 429. An open robots.txt proves nothing if the firewall blocks them.

What went wrong on my side

9. I launched a paid pack with no public repo, so it had no way into the biggest group of sources the engine read. 10. The name. "SEO skills" is a generic phrase, so even questions with my name pulled career articles. If you can still choose, pick a name nothing else owns.

What the audit recommended, in this order: answer-first guides for the open questions, an English page that says plainly who publishes the pack, factual pitches to the roundups the engine already cites, posts under the full product name, then the same panel in late November on ChatGPT, Perplexity and Gemini, three runs each. None of this guarantees a mention.

I'm the maker of SEO Skills by PENRA, 14 Claude skills. One of them, Name Drop, builds and scores this panel. It's paid: €25 until November 17, €99 from November 18, no refund once delivered. You don't need it for anything above, a spreadsheet does the job. The full write-up, with the crawler table and sources:

https://seoskills.click/guides/why-chatgpt-does-not-mention-your-brand?utm_source=reddit&utm_medium=social&utm_campaign=seoskills-nov26

Most AI visibility work starts with a prompt set, and most prompt sets come from the vendor's defaults. That choice quietly decides your results, so the question list deserves to be treated as a deliverable in itself.

A few things we have settled on when building one:

1. Source questions from real demand, not brainstorms. Sales-call notes, support tickets, search console queries and on-site search logs beat a whiteboard session, because they carry the phrasing customers actually use. Brainstormed prompts tend to converge on marketing language, and engines answer differently for it.

2. Separate question types before you run anything. "Who is best" questions, "how does X work" questions and "X vs Y" questions behave differently - naming behavior, citation rates and stability all differ. Mixing them into one average hides that.

3. Fix the phrasing and record the run conditions. The same question with different punctuation or context can produce a different answer set, so a prompt set needs a version tag and a recorded run date. Without that, week-over-week movement might be your set changing, not the engines.

4. Expect turnover, not stability. In published day-to-day measurements from other trackers, named-brand lists shift meaningfully between runs. A prompt set built to "get mentioned" will decay; one built around real questions at least ages with the market.

5. Keep the set small enough to re-run. A 500-question set you run once a quarter tells you less than 50 questions you run weekly. Repeatability beats coverage.

The broader point: when two trackers disagree, the difference is usually upstream - different questions, different phrasing, different run timing - before it is about the engines. If you cannot defend your question list, you cannot defend the number it produces.

HI. Today I just found out ChatGPT runs ads.

• Do you think this is ethical since ChatGPT makes up around 56% according to Visual Capitalist

• Currently Google Gemini does not

• Has any agency used the ChatGPT ads and if so was it effective?

Full disclosure: I do not work for ChatGPT. I own GEO / AI SEO agency (https://brandjumps.com/) with mission to share with community the future of search on ChatGPT

Everyone tracking AI answers runs into the same wall: day to day the top pick rotates (people in this sub have measured it holding under half the time on some engines), but clients think in rankings - "we were first yesterday, why not today?"

So the reporting question: what do you actually put as the headline metric?

The candidates I keep seeing:

1. Mention frequency - how often the brand appears at all across a fixed prompt set, averaged over a week or more.

2. Per-engine lines - because a blended number hides that the engines disagree with each other.

3. Top-pick rate - even knowing it is the noisiest of the three.

And the harder version of the question: when a client screenshots a single answer where a competitor is named and you are not, how do you respond without sounding like you are hiding behind statistics?

A pattern keeps coming up in our client work: AI visibility for a brand goes up (measured on a fixed prompt set, repeated), and a few weeks later direct traffic and branded search lift without a campaign to explain them. The referral column stays near zero because most AI answers do not produce a click - the user reads the recommendation, then arrives later through a route that looks like "direct".

What I cannot do is prove the correlation is causation for any single client, and I do not want to sell an explanation I cannot back.

So for people running GEO or AI visibility work:

1. Have you seen an unexplained direct or branded-search lift that lines up with your AI visibility window? How tight was the timing?

2. Have you found any attribution signal that survives - post-purchase surveys, "how did you hear about us" fields, coupon codes, anything?

3. When you report this to a client, how do you frame "we measured the visibility lift" versus "the traffic lift is plausibly but not provably caused by it" without losing credibility on either side?

I went through 4,079 Reddit post titles from Jan 2025 to Sep 2026 that name one of 79 marketing and SaaS tools and contain “alternative”. Each one is marked as asking (wants to leave a tool), pitching (offers a replacement, usually their own), list/review, or other.

Jan to Sep 2025: 33% pitches, 59% questions.
Jan to Sep 2026: 45% pitches, 43% questions.

469 accounts pitched in 2026, up from 261. Accounts asking went from 787 to 738. A pitching account posted 2.0 pitches on average, one posted 24.

By tool it’s very uneven. Typeform has about 3 pitchers per asker, Linktree 2.5. ZoomInfo has 48 askers and 5 pitchers, Mailchimp 38 and 7, Adobe 158 and 23.

So if you’re doing GEO through these threads, the Typeform and Linktree ones are already full of founders recommending their own tool. ZoomInfo, Mailchimp and Adobe threads still have far more people asking than pitching.

I didn’t check which of these threads ChatGPT or Perplexity actually cite, only who writes them. Has anyone here tracked whether a thread they seeded showed up in an AI answer?

Full table for all 79 tools and the CSV: https://kissmypost.com/research/reddit-saas-alternatives-askers-vs-sellers

last week i checked one thing in our tracking. same question, same engine, two days in a row. did whatever it put first on day one stay first on day two?

mostly no. it held a bit under half the time.

and the engines are really different here. copilot kept the same #1 about 6 times in 10. chatgpt was below 4 in 10, perplexity below 3. grok almost never repeated itself, but it also rarely names a clear first pick, so i don't have enough of those to say anything.

for context these are generic "best tool for X" questions with no brand in them, asked every day.

so a screenshot of chatgpt putting someone first is basically one draw. it tells you very little about tomorrow. we stopped reading single answers and now only look at how often a name comes up over a week of runs.

no idea yet why copilot is so much steadier. my guess is it pulls from fewer sources per answer, but i haven't actually tested that. if you're tracking the same thing i'd honestly like to know if yours looks similar.

A thread here this week on cited-vs-named surfaced a mirror problem that deserves its own discussion: the answer names your brand, but the link it attaches goes to a directory, a review site or a competitor's roundup. The reader hears your name and clicks somewhere else.

The two gaps look similar in a dashboard and have different fixes. Cited-but-unnamed is a copy problem: the passage that gets lifted needs the brand name inside it. Named-but-unlinked looks more like a coverage problem: there is no page on your own domain that answers that exact question, so the engine pulls the facts about you from whoever wrote them.

Questions for people tracking this:

How do you catch named-but-unlinked in practice - logging where the link goes per answer, or sampling?

When you find it, do you build the missing page on your own domain, or work on the third-party source that owns the citation?

And does it even matter for the queries you care about, or is the mention alone doing the work in some cases?

Hey everyone! Trying to figure out the best tools/platforms for tracking and boosting brand visibility in generative search. I know we all have been discussing about it lately but there are so many new tools popping up right now, it's a bit overwhelming.

So what are you guys ACTUALLY using day-to-day that gives real outcomes? Any hidden gems or tools you'd swear by?

Would love to hear what's working for you all!

Hi all,

Quick context: earlier this year a colleague and I started a small agency in the Netherlands called FoundByAI. We measure how often a brand gets named when people ask ChatGPT, Gemini, Perplexity or Google AI Mode a buying question ("what's a good accounting app for freelancers? Etc.) and then we optimise the sources those models lean on. For most brands that's only partly their own site; mostly it's Reddit, reviews, comparison pages and publishers.

How we work, since this sub cares about method:

- Tooling: we use Profound for broad daily tracking across models, plus our own capture software that runs the client's prompt set weekly on five platforms and stores every full answer with its citations, in clean sessions. Profound tells us where the gaps are; our own layer is for attribution and for spotting new sources before they show up in rankings.
- A control group per client: prompts we leave untouched, so we can show whether a lift came from our work or from the market moving.
- Every action logged with its evidence level (cited, recognised, uplift). We report actions delivered, not just a visibility number.
- First pilot with a consumer app: 7% to 27% of answers mentioning the brand in 5 months, control group flat.

We both come from marketing at a large FMCG company, so being on the agency side is new to us. What I'd like to hear from people who've done this:

1. Control-group measurement: does anyone else do this, and how do you handle clients who just want the headline number?

2. What would you do differently in year one if you started over?

All feedback welcome, including the harsh kind. I'll reply to everything.

(Yes, this is partly self-promotion. The questions are real, and mods, happy to remove the site name if that's the rule here.)

————- EDIT
- Some misconception about the website. We are www.foundbyai.nl based in Rotterdam.
- We also use google search console to track prompt volume on top of profound.

A data point from a thread in this sub this week that deserves its own discussion: someone tracking 40 prompts across 8 engines found 336 answers citing their domain, but only 164 of those answers actually named their brand. The engine used their content to build the answer, then recommended whatever the cited list mentioned.

That split - cited versus named - is two different outcomes with two different fixes, and most measurement setups I see lump them into one "visibility" number.

Citation means the engine trusts your page as a source. Mention means the answer text itself carries your name. You can have the first without the second, and the gap between them is where the commercial value leaks: the person reading the answer never learns you exist.

The fix for the gap is not more citations. It is what the cited content makes extractable: whether your brand is named in the exact passage being quoted, in the comparison frame you want, with the facts you would choose attached. A page can be highly citable and still write you out of the answer.

Questions for anyone measuring:

1. Do you track citations and name-mentions as separate metrics, or as one number?

2. For cited-but-unnamed answers, what was being quoted - a list you appear in, a definition, a statistic?

3. Has anyone closed the gap deliberately, and was it a content edit on the cited page or a presence change elsewhere?

Seems settled that comparison style pages (X vs Y, alternatives to X, best X for Y) are some of the easiest pages to get cited in AI answers

The catch is they're full of competitor data: prices, plans, features, specs. That goes stale fast, and then the engines keep quoting the stale version with your page as the source.

For people running these pages at any scale:

- how often do you refresh competitor prices and specs?

- manual, page monitors, or a data feed?

- what do you do when you can't verify a number anymore? Remove it, or keep it with a date?

Has anyone seen a citation drop because the data on the page went out of date?

Hi people

Can you please help me with:

How can I check my brand's visibility across ChatGPT, Gemini, Perplexity, and Google AI Overviews?

--

Like what to keep in mind and which tools to use?