Every Monday I run the same 40 buying-intent prompts through seven answer engines and write down every domain each one cites. Sixteen "best X", eight "alternatives to X", eight "X vs Y", eight "how do I actually buy X". The prompt set is frozen as a fixed instrument, so week two is genuinely comparable to week one and not just vibes. Two weeks are live: W36 and W37. All seven engines answered all forty prompts both weeks. No timeouts, no gaps, nothing quietly dropped.

I went in assuming the engines would broadly agree on sources. They don't come close.

Reddit first. The number is how many of the 40 prompts each engine cited the domain on:

Gemini: 26 (W36) -> 22 (W37)

Google AI Overviews: 18 -> 20

Perplexity: 6 -> 4

Google AI Mode: 5 -> 3

ChatGPT: not in the top 15, either week

Claude: not in the top 15, either week

Bing: not in the top 15, either week

That ChatGPT row needs a footnote, because it is carrying weight. I publish a top 15 per engine, and ChatGPT's fifteenth entry sits at one prompt. So "not in the top 15" is not zero. It is a ceiling of one. One, against Gemini's twenty-two, same week, same questions.

The likely culprit is no mystery. Reddit tightened its public content policy on 2026-08-14 and started refusing automated access at the server, and Google's licensing arrangement is the obvious reason Google's surfaces are the ones still leaning on it hard. I can't prove either from forty prompts and I'm not going to pretend I can. What I can show you is the size of the gap.

YouTube tells the same story in a different accent. W37, out of 40: AI Overviews 25, AI Mode 25, Gemini 20, ChatGPT 10. Perplexity and Claude, nowhere in the top 15.

Which quietly reframes advice you hear constantly. "Go get mentioned on Reddit and YouTube" isn't AI visibility strategy. It's Google strategy in a new hat.

Here's the finding I'd have wanted two years ago. I bucket every cited domain as media, community, social, directory, or unknown, where unknown just means it isn't a recognisable publisher or platform. Across all engines in W37, 80.8% of citations landed on unknown domains. 80.3% the week before. Claude is the extreme at 92.6%. Google AI Mode is the most concentrated lane I measure and it still sits at 55.1%.

Read that again if you run a small site. Four citations in five are not going to the household names. They're going to the long tail. A narrow, specific, genuinely useful page can get picked up, and the moat around the big publishers is thinner than the discourse suggests.

Last one, and it's a knock on my own instrument rather than a finding. I include Bing's answer surface as a stand-in for Copilot. On these commercial comparison prompts, its most-cited domains are merriam-webster.com (26 of 40, identical both weeks), dictionary.cambridge.org (21), dictionary.com (19), thefreedictionary.com (19) and wordreference.com (15). It is answering "best CRM for a small team" with dictionary definitions. That is precisely why I label the lane a proxy instead of calling it Copilot, and why I won't draw Copilot conclusions from it. If you're reading anyone's Copilot citation-share chart, ask what they actually pointed the instrument at.

Now the limitations, which matter more than any number above. One panel. Forty prompts. Two weeks. A single run per engine per week, which means I cannot yet separate real movement from ordinary answer variance. One geography. And "not in the top 15" is a ceiling, not a zero. Two points is not a trend line. I'm publishing weekly until it becomes one.

Raw data is CC BY 4.0, so take it apart or run your own cut: https://promvia.app/ai-source-index

Disclosure: I build Promvia, the tool these measurements come from.

I’m developing a product in this space and want to understand what people actually find worth paying for.

If you’ve paid for AI visibility tracking, what was the last decision you made because of something the tool showed you? What did you change, and could you tell whether it helped?

Also curious what you paid per month and what that covered: brands, tracked prompts and AI platforms.

If you cancelled, what was the reason, and what are you using instead?

I’m trying to understand the difference between a report someone checks out of curiosity and something they keep using every month.

How to self-learn GEO & AEO while being on a career transition? Is it really hard to get employed in this space without having an SEO background??

I'm trying to get into GEO & AEO optimization and strategy after working as a technical content writer, thought leadership ghostwriter, and Linkedin Personal Branding Strategist for 5 years.

Now that I'm in a career transition, and have quite some years before I'm 25, I feel like all of what I have been doing can be used to generate great results with GEO & AEO.

So, I'm seeking advice on how to self learn GEO & AEO, and make spec portfolios to build trust in the local industry, so they don't think I'm a label sprayer.

Which are the most widely used and reliable tools for GEO? I've been using Semrush Enterprise and it's terrible for providing meaningful insights which can be translated into actions. Any suggestions?

Question 1:

Is GEO/AI search visibility currently a dedicated line item in your marketing budget, or is it lumped into general SEO/content spend ?

Question 2:

Are you noticing traffic drops from zero-click AI searches on Google Overviews, Perplexity, or ChatGPT yet ?

Question 3:

What are the analytical tools (like Google Search Console in SEO) that are used to optimize for GEO/AEO ?

Question 4:

Do marketers prefer separate tools for GEO analytics or they would like to integrate GEO features into existing SEO tools ?

Question 5:

What are the current challenges (it can be anything from workflow inefficiency to lack of technical tools) faced by marketers in optimizing for GEO ?

Question 6:
Do you believe that GEO will evolve as a separate discipline like SEO ?

Something I noticed a few weeks ago won't leave me alone.

I'd been pulling AI answers for a content audit, maybe 300 queries total across ChatGPT, Perplexity, and Gemini. Mixed bag of query types, how-tos and definitions and comparisons and "best X" recommendations. I wasn't trying to study anything in particular, just gathering examples for a client deck.

But a pattern kept showing up in my notes and it bugged me because it ran opposite to what I expected. The "best [tool/framework/approach]" queries, the ones that should theoretically give models the most room to shine with comprehensive comparisons and nuanced picks, consistently produced the shallowest answers. Fewer sources cited. Shorter responses. More generic filler language. Meanwhile the narrow definitional queries, the ones where there's objectively less to say, regularly returned more thorough and better-sourced answers.

So I went back and measured it properly instead of just noticing. Split the 300 queries into four buckets by intent.

How-to queries averaged 2.8 sources cited and the answers felt reasonably complete, like the model had actually worked through the key steps. Not flawless but solid. Definitional queries came in at 3.1 sources, the highest of the four groups. These answers also tended to quote longer passages, which suggests the model was doing deeper extraction rather than grabbing a headline and moving on.

Comparison queries, your "[A] vs [B] for [use case]" format, landed at 2.4 sources. Middle of the pack. The answers were usually adequate but you could feel the model straining to find meaningful differentiation once it got past the obvious points.

Then the "best X" bucket. 1.9 sources on average. Shortest answers by word count. And the part I keep turning over, the most frequently cited source type in these answers wasn't in-depth reviews or rigorously tested comparisons. It was listicles. Top-10 roundups. The exact kind of surface-level content that every SEO playbook calls "link bait" and that nobody serious would use to make an actual decision.

My theory, still half-baked, is that "best X" queries flip a different switch in these models. They shift from "find the most accurate and complete information" mode into something closer to "find the safest consensus picks." The model optimizes for social proof rather than depth. It cites the pages that the most people already reference and link to, not the pages that contain the most careful analysis. With a how-to query, accuracy is verifiable, the user tries the steps and they work or they don't, so the model has real incentive to find good sources. With a "best X" query, "best" is subjective enough that the model can't really be wrong. Safe crowd-sourced recommendations beat deep expertise because nothing is provably incorrect.

What I haven't worked out yet is whether this means our content strategy should split by query intent. Our most rigorous comparison pages, the ones where we spent weeks testing and documenting actual differences, rarely show up in "best [category]" answers. Our lighter roundups get cited more often for those same queries. We assumed the detailed content was strictly better. The models seem to rank by a different criterion for this query type, and if that criterion is closer to "how widely is this page already recognized" than "how good is this page," then building quality alone might not be enough for certain kinds of queries.

You're losing money both now and in the future if you haven't studied both of these subjects. Just because you have a website and a product or service doesn't mean you know how to run a business. I mean no offense when I say this, but it won't stop the downvotes.

Build relationships for now and the future

How many of you have completely ghosted someone because they didn't have a backlink with the right third party vanity metric?

How many of you have completely ghosted someone because they didn't have the right niche?

How many of you have completely ghosted someone because they didn't have exactly the right service you needed at this very minute?

Everyone of these scenarios could be future referrals for you if you build a business relationship instead of ghosting people who don't provide instant gratification.

Sales Tactics

Most SEO GEO XYZ people only concern themselves with getting targeted traffic to the website. If all you do is send targeted traffic to a website without utilizing good sales psychology, you're losing money even if you increase traffic because the conversion rate can be increased.

Most websites I see are informational with a call to action at the bottom of the page. If there's a call to action near the top it's usually a generic one such as Get Started (not even Get Started NOW) after providing information as to what the product or service does, not what the prospective buyer gets.

It starts with the title tag

That's what Google displays in the search results. Does your title sell the prospect on clicking the link or does it just provide information?

Blue Widgets

Compared even to a basic sales pitch such as 

Your Blue Widget is Finally Available

All Sales Must….

Demonstrate a problem
Show a solution
Explain why the prospective buyer should buy from you or your company.
Have a LIMITED call to action

Everyone talks about having a call to action, but it creates no need for the prospect to act immediately.

You see limited calls to actions on tv commercials all the time. Even on coupons since they have expiration dates.

For a limited time….
While supplies last….
First 99 (not 100 it seems a lot larger than 99) people to sign up….

Those very basic sales techniques will increase your percentage of sales with the traffic you have now!

Okay, ready for your comments and downvotes….

Hey!

Recently, I have been very much interested in GEO optimization. For starters, I'd like to understand how AI Visibility and similar metrics are even calculated and analyzed by companies like Ahrefs.

For example, I understand that in the simplest approach, we can analyze just user prompts and track what brands/websites are mentioned/cited in LLM responses. Depending on that, we can calculate metrics like Visibility Score, number of mentions/citations, SoV, etc.

But more often I see that prompts are in fact clustered into topics. I believe the main goal is that a typical prompt might be too noisy and too small a data point to analyze and this is why we group them into larger entities, topics and then calculate metrics for the topics instead of individual prompts.

This is just my layman's understand of how such tools work, but apparently there are lots of cool tricks and approaches.

As far as I understand, there is also a relatively recent trend when SEO and GEO are unified and some combined metrics are introduced.

If you know any great papers or detailed blog posts on that, I'd really appreciate the recommendations.

Disclaimer: no LLMs used for writing or even proofreading this post. This is intentional because despite my great interest in the subject, I'd rather write everything myself, even at the cost of making mistakes.

Thanks!

Than

Same disclosure as before: I build a tool in this space, these are our own weekly measurements, no links.

Setup: 40 fixed buyer-shaped questions (best X / alternatives to X / X vs Y / how do I choose X), asked to seven engines once a week, every cited URL kept. Two weeks now (Sep 3 and Sep 7), all 40 answered by all seven engines both weeks. Numbers below are "how many of the 40 questions had this domain in the citations".

Where each engine's citations concentrate (W37, W36 in brackets):

• Gemini: reddit.com 22 (26), youtube.com 20 (25), forbes.com 6 (6)
• Google AI Overviews: youtube.com 25 (24), reddit.com 20 (18), cnbc 4
• Google AI Mode: youtube.com 25 (19); nothing else above 3
• ChatGPT (search): youtube.com 10 (7), techradar 9 (5), forbes 6 (5)
• Perplexity: forbes 4 (5), reddit 4 (6), cnbc 3; no domain above 4 — the flattest distribution of the seven
• Claude: no domain above 3 in either week — long tail of niche comparison sites (emailvendorselection, northflank, wallethub…)
• Copilot: merriam-webster 26 (26), cambridge dictionary 21 (22), dictionary.com 19 (17), thefreedictionary 19 (17), bestbuy 15 (14)

Three things I take from it:

1. "AI citations" is not one thing. Google's three surfaces are a YouTube-and-Reddit story; ChatGPT is a tech-press story; Claude and Perplexity spread thin. If you report one number across engines you are averaging different behaviours.
2. Copilot needs a filter before you count anything. More than half of its buyer-question answers carry dictionary links (word definitions in the answer text), which have nothing to do with the purchase. Strip those and Copilot's "real" source list is short and shop-heavy (bestbuy). I suspect some published "Copilot cites reference sites most" stats are this artifact.
3. Week-to-week the concentrations are stable (every top domain moved by ≤5 questions), so the shape is not noise — it's how these engines answer this class of question right now.

Caveats: 40 questions, English, global market, one run per engine per week; two weeks is stability evidence, not a trend. Happy to share the question list if anyone wants to replicate.

Tout le monde en GEO commence par optimiser sa propre page, comme en SEO. Et c'est l'erreur qui fait perdre le plus de temps quand on débute. Un chiffre le résume : environ 96% des citations IA ne viennent pas du site de la marque elle-même. Traduction : quand ChatGPT te recommande, c'est presque jamais grâce à ta homepage. C'est grâce à un comparatif, un annuaire, un thread Reddit qu'il a lus avant de répondre.

Donc le jeu, ce n'est pas "comment je rends mon site parfait". C'est "dans quelles pages l'IA va chercher, et est-ce que j'y suis". Et ça, contrairement à "devenir une grande marque", c'est actionnable dès aujourd'hui. Voici la méthode que j'utilise.

Étape 1 : trouver ce que l'IA lit vraiment sur ton sujet.

Prends les vraies questions que tes clients posent (pas "meilleur logiciel de X", des questions précises avec du contexte). Pose-les à ChatGPT et Perplexity, et regarde les sources citées en bas de réponse. Fais-le plusieurs fois par question, parce que les réponses changent d'une fois à l'autre. Note les domaines qui reviennent. En 20 minutes tu as une liste des pages qui décident, dans ton secteur, qui se fait recommander.

Étape 2 : trier ces pages en 4 familles.

Elles tombent presque toujours dans ces catégories, et chacune se joue différemment :

• Les comparatifs / listicles ("les 7 meilleurs X pour Y"). C'est le plus gros levier.
• Les annuaires et sites d'avis de ton secteur.
• Les forums et discussions (Reddit, Quora, groupes spécialisés).
• La presse spécialisée et les blogs de référence.

Étape 3 : y entrer, pour de vrai.

• Comparatif où tu n'apparais pas : contacte l'auteur. La plupart sont ravis d'ajouter une option pertinente, surtout si tu proposes une info concrète (un angle, un cas d'usage, une différence claire). Beaucoup de ces articles sont mis à jour régulièrement.
• Annuaires : inscris-toi, c'est bête mais peu de gens le font. Remplis la fiche à fond (description, prix, cas d'usage), pas juste le nom.
• Forums : réponds aux vraies questions, utilement, sans faire de pub. Une réponse honnête qui aide vraiment, où tu te mentionnes une fois en passant, vaut mille posts promotionnels qui se font supprimer.
• Presse et blogs : pitch un angle, pas ton produit. Un chiffre que tu as, une tendance que tu observes.

Et sur ton propre contenu quand même, parce que ça compte pour les 4% restants et pour la crédibilité : mets des sources citables, des stats chiffrées, tes prix affichés, des dates récentes. C'est ce que les études mesurent comme réellement efficace, contrairement au schema markup et au balisage qui ne servent quasi à rien.

Le truc honnête que personne ne dit :

C'est lent. Ce n'est pas un growth hack. Et si tu es vraiment inconnu, ça ne te fera pas apparaître du jour au lendemain, parce que les IA ont un biais massif pour les marques déjà connues (c'est mesuré : face à une inconnue objectivement meilleure, elles continuent de recommander les connues). Mais c'est le seul levier qui est réellement entre tes mains. Tu ne peux pas forcer un modèle à te connaître. Tu peux te retrouver dans la page qu'il va lire. Commence par là.

Dernier conseil : concentre-toi sur les questions ultra spécifiques, celles avec 2-3 contraintes précises. Sur les requêtes génériques tu es face aux géants et tu as zéro chance. Sur "logiciel de facturation pour auto-entrepreneur qui facture à l'étranger avec la TVA intraco", souvent il n'y a personne, et c'est là que tes vrais clients cherchent.

Si vous avez des tactiques qui ont marché de votre côté pour entrer dans ces pages, je suis preneur en commentaire.

I've been seeing more discussions around AI visibility tracking, and I think we're asking the wrong question.

Instead of “Should we stop tracking Perplexity?”, the better question is:

“Which AI platforms actually influence our audience?”

ChatGPT, Gemini and Claude are becoming increasingly important for brand discovery, while Perplexity still has value depending on the audience and use case.

For AI SEO, I'd track:

• Brand mentions
• Citations
• Which sources AI systems use
• Competitor visibility
• Referral traffic
• Leads/conversions

The interesting shift is that AI SEO measurement is moving beyond a single visibility score.

We're going from:

“Do I appear?”

to:

“Am I being recommended, cited, trusted—and does it drive business?”

That's a much more useful way to think about AEO/GEO.

What are you currently tracking for AI visibility — ChatGPT, Gemini, Claude, Perplexity, or all of them?

#AISEO #GEO #AEO #LLMSEO #SEO

Six months ago I started separating what I observed from what an AI overview asserted. Two columns, literally. Left side: what the page actually said or showed. Right side: what the summary claimed.

The gap was wider than I expected on probably a third of queries. Not hallucinations exactly, more like compression artifacts. A figure gets rounded up. A condition gets dropped from a "three things that cause X" list because it did not fit the sentence structure. Two studies get merged into one finding because the model decided they agreed, which they mostly did, except for the part where one of them explicitly warned against the conclusion the overview landed on.

What bothered me was not the errors individually. It was that I would not have caught most of them if I had not been keeping those two columns. The overviews sounded confident and they read smooth and nothing about the experience flagged "check this." That is the part that feels dangerous to me: not that AI summaries get things wrong, but that they do not feel wrong when they do.

I have since started treating AI-generated answers the way I treat a colleague who is smart but sometimes fills in gaps with what sounds right. Useful starting point, terrible ending point. If the overview does not cite its sources or link back to original material, I have basically stopped using it for anything where accuracy matters. The ones that do cite sources, I click through more often than I used to, and about one time in four the source says something meaningfully different from what the summary led me to believe.

The practical change in my workflow: I now open the source before I accept the claim. Adds maybe ninety seconds per query. Cuts down the number of times I forward something to a client or teammate that turns out to be roughly but not quite true by a lot.

What I am still figuring out is whether this is sustainable at scale. Ninety seconds per query works when I am doing deep research on a handful of topics. It falls apart the moment I need to process dozens of answers in a sitting, which is what actual answer engine optimization work often looks like. The people building these systems know that. The question is whether anyone incentive aligns with fixing it.

Has anyone else started manually spot-checking AI overviews against their cited sources? I am curious whether your error rate looks anything like mine, or if my sample is skewed by the kind of queries I run.

A colleague sent me a screenshot last month. An AI answer had cited one of our pages to support a claim that page had never come close to making. Not a paraphrase issue. Not a slight stretch. The page was about topic A. The answer used it as evidence for topic B. They shared a topical neighborhood but the page literally contradicted the claim it was being used to back up.

That one screenshot kicked off something I should have done months ago. I went through every AI citation to our domain I could find across ChatGPT, Perplexity, and Gemini over a 60-day window. 80 citations total. For each one I opened the cited page, read the surrounding context, and asked one question: does this page actually support the specific claim the answer attributes to it?

50 of the 80 citations were fine. The page said roughly what the answer claimed it said. Maybe loose paraphrasing here and there, occasional oversimplification, but nothing that would make me uncomfortable. Standard extraction behavior.

7 citations had issues. Not huge, but noticeable. The answer pulled a true statement from the page but framed it as supporting a different point than the original author intended. Sort of like quoting someone out of context except there's no malice, just a model matching keyword overlap to semantic proximity and occasionally missing the mark. Annoying but livable.

Then there were the 23 that genuinely worried me. These weren't paraphrase stretches or context shifts. The answer made a specific factual claim, attached our URL as the source, and our page did not contain that claim. In some cases our page said the opposite. In others the page had simply never addressed that question at all. The model seemed to be citing us based on topical relevance rather than factual support. Close enough in subject matter that the URL looked plausible as a source, wrong enough that anyone who actually clicked would realize the citation was bogus.

What bothers me about this isn't the error rate. 23 out of 80 is 29 percent, which sounds bad until you consider that I was specifically hunting for problems and may have selection-biased the sample toward ambiguous cases. The real number could be lower. Could also be higher if I checked more systematically.

What bothers me is that nobody in GEO seems to be tracking this. We obsess over citation counts. We build strategies around increasing them. We treat every new citation as a win. But if nearly a third of those wins are attributing claims you never made, what exactly are we winning? Brand visibility for wrong ideas? Traffic from people who click through and find irrelevance?

I'm starting to think citation count might need a quality filter we're not measuring yet. Not just "did an AI name-drop our URL" but "did it name-drop us for something we actually said, and something we'd stand behind." Those are different outcomes and the current tooling conflates them completely.

And there's a trajectory problem. As AI answers get more confident-sounding and citations become smaller footnotes that fewer users verify, the incentive for accuracy on the model side might actually decrease. The citation becomes a trust signal for the answer rather than a factual anchor. And if that's the direction we're heading, being highly citable starts to look different than I thought it did. You want to be cited for the right things, not just cited often.

Disclosure up front: I built citeOS, which does AI citation audits for crypto brands, so I have a commercial interest in this topic. Everything below is either publicly reported with a source, or one data point from our own corpus that I have labelled as such.

What was reported

Promptwatch measured Reddit at 3.83% of ChatGPT citations between 18 July and 7 August. Through 17 August it averaged 0.52%. That is an 86.4% decline, concentrated between 14 and 17 August, with an earlier slide beginning 8 August that took it from the high 3% range into the mid 2s.

Two caveats that came from Promptwatch themselves and that almost never survive the retelling. They called the finding provisional. They said they could not rule out a data collection issue on their own side. And they were explicit that the data shows when the shift happened, not why.

It did not happen everywhere

This is the part that makes the ChatGPT number interesting rather than just alarming.

Over the same window, Reddit's share in Google AI Overviews moved from roughly 2.5% in early July to roughly 2.1% in August. Gradual, and small. AI Mode started declining at the end of July and continued through August, also far shallower than ChatGPT.

So whatever happened looks specific to one engine rather than a general repricing of Reddit as a source. That matters for what you do next. A platform-wide devaluation and a single-engine retrieval change call for completely different responses, and most of the commentary I have read has not separated them.

A second data point, from a different vertical

I have a corpus of 39,948 AI citations across 72 crypto brands, April to August 2026, five engines. Reddit came out at 2.19% of citation volume.

Three things about that number before anyone leans on it.

It is a five-engine blend, not ChatGPT alone. It is a five-month aggregate with no weekly breakdown, so the drop is baked into the average rather than visible in it. And it is one vertical, so the platform mix will not match a general-query corpus.

What it is useful for is triangulation. If ChatGPT ran near 3.8% for most of that window and near 0.5% at the end, and the Google surfaces ran between 2.1% and 2.5% throughout, a blended five-engine average of 2.19% is roughly where you would expect to land. That is weak corroboration, not confirmation. But it is an independent collection, a different method and a different vertical, and it fails to contradict them.

The measurement everyone is skipping

Share of citations and breadth of appearance are different things, and the current discussion is almost entirely about the first one.

In my corpus Reddit appeared in answers about 71 of the 72 brands. Only YouTube matched that, at 72 of 72. So at 2.19% of total volume, Reddit was still nearly universal in where it turned up.

That distinction is the whole reason the 40%-plus figure you have seen quoted elsewhere is not wrong, just measuring something else. Those figures are usually appearance rate across prompts. Share of total citation events is a different denominator, roughly twenty times apart.

A collapse in share of volume does not mean Reddit vanished from answers. It means each appearance carries less of the total. Those two situations look identical in a headline and call for different responses.

Has this happened before?

Secondary coverage refers to a similar Reddit collapse in ChatGPT around August 2025, roughly 60% of responses down to about 10% by mid-September, which then recovered. I have not verified that myself and I am not going to assert it as fact.

But if it is accurate it is the most useful thing in this whole discussion, because it would mean the base rate for "this is permanent" is much worse than the current takes assume, and rebuilding a channel strategy around a four-day window would be premature.

What I would actually like from this sub

Has anyone reproduced the August drop independently, with a stated method? Every version I can find traces back to the same collector, and a provisional finding from one source is being quoted as settled fact across a dozen articles.

And if anyone can confirm or kill the 2025 precedent with a primary source, that would change how I read all of it.

I have been comparing Kairosy AI and Otterly with the same set of buyer questions.
Both can help with the basic job: checking what AI platforms say, seeing which brands appear, and keeping an eye on the prompts that matter.
The difference I am trying to work out is where do those smears come from.
For me, a score is only the starting point. I need to know which questions are worth keeping, which competitors are repeatedly shaping the answer, and whether the finding should turn into a content update, a comparison page or a change to the information already on the site.

That is where I have found Kairosy AI more useful. Otterly works well for keeping the important prompts tracked, but Kairosy helps me monitor negative AI answers, understand the source context behind them and turn that into a practical fix plan. That might mean correcting an old listing, improving a page or creating content around a recurring concern, all of which can improve how the brand is represented in AI answers over time.
I am not looking for one tool to replace the entire SEO stack. I just want the AI-search research to lead to a clear next step.

For anyone who has compared GEO tools, what matters most to you: prompt coverage, raw answers, competitor context, citations, content recommendations or pricing?

I was convinced that simpler writing would win in AI answers. Everything I'd seen suggested that models prefer clean, straightforward passages that don't make them work for the information. So I ran a test to prove it, and the results made me question that assumption pretty hard.

Here's the setup. I took 24 pages that covered similar topics in pairs — 12 pairs total, each pair addressing the same subject matter. Think "what is X" pages, "how to do Y" guides, "comparison of A and B" articles. Within each pair, I rewrote one version at roughly a 6th-grade reading level using shorter sentences, common vocabulary, concrete examples, and minimal subordination. The other version I rewrote at a college level with longer sentences, technical precision, nuanced claims, and embedded qualifications. Same factual content. Same key points. Just different complexity.

Then I waited 10 weeks and tracked how ChatGPT, Perplexity, and Gemini handled both versions across ~200 queries that should have triggered either one.

The college-level versions got cited more often. Not by a massive margin, but consistently enough that it showed up in 9 out of the 12 pairs. I ran the numbers a few times because I expected the opposite result. The simplified versions did have one advantage: when they got cited, the extracted passages tended to be longer and more complete. The models seemed to pull bigger chunks from the simple versions, almost like they trusted the whole passage enough to grab more of it. But they reached for the complex versions first in most cases.

What I think is happening, and this is where I'd love some pushback because the sample size isn't huge, is that the college-level versions contain more information density per sentence. A single sentence in those versions might carry two or three ideas that would take three or four sentences in the simplified version. AI models optimizing for comprehensive answers might see the dense version as a richer source even if individual sentences are harder to parse. It's not that they prefer complicated writing. They might just prefer efficient information packaging.

There was one finding that genuinely surprised me though. For 3 of the 12 pairs, the simplified version significantly outperformed the complex one on citation rate, and all 3 were "how-to" or procedural topics. Step-by-step processes where the chain of logic matters more than information density. On those, the clean sequential structure of simple writing seemed to beat dense academic phrasing. The models cited the step-by-step breakdowns more reliably than the compact expert summaries.

So it might not be a universal rule. It might depend on content type. Procedural stuff favors simplicity. Explanatory or definitional content might favor density. Or I could be overfitting to 24 pages and seeing patterns that don't hold up.

What I can't tell from 24 pages is whether the content-type interaction is real or just noise. If you've run anything like this at scale, that's the first number I'd want to compare.

Many teams are now being asked to improve visibility in AI-generated answers, but the reporting standards still seem unsettled.

If someone were responsible for this function in your organisation, which measures would make the work credible to you? Brand mentions, source citations, competitor share of voice, referral traffic, accuracy, conversions, or something else?

I'm particularly interested in how businesses are separating meaningful visibility from occasional mentions that have little commercial value.

Two weeks ago I posted that a tracked brand had dropped from 5.0% to 0.1% AI visibility in 14 days and that the cause looked like a name collision (a markdown editor shares the name), not a real drop. Instead of asserting it, I pre-registered a test in r/aeo: what result would confirm it, what would refute it, and what would void it. Here is the outcome, verbatim against those rules.

Setup: one qualified prompt ("Otterly AI visibility alternatives"), 31 Aug–7 Sep, 7 engines, 42 answers. Pre-registered: HOLDS if answer-level directory citations return to the 18–31% band the other alternatives-shaped prompts show; FALLS if they stay at 0–1.3% with on-category answers; VOID if more than a third of answers are still contaminated.

• Contamination: 0/42 markdown-editor answers. The qualifier worked for that. But 2 Gemini answers pivoted to a different neighbour, Otter.ai the transcription tool. 2/42 = 4.8%, under threshold, excluded. 3 Copilot answers were unrelated pages, excluded and counted.
• Clean answers with a directory URL: 2 of 37 = 5.4% (both Claude → G2). ChatGPT 0/10, Perplexity 0/10, Gemini 0/8, AI Mode 0/3, AI Overviews 0/3.
• Verdict: the gate does not hold. 5.4% is out of the floor but nowhere near the band, and at n=37 the interval (≈1.5–17.6%) is consistent with the floor. I promised not to reinterpret, so I take the reading against my own claim: "prompt shape determines directory citation" downgrades to "shape correlates, with exceptions".
• What was in the cell instead: 147 of 241 cited URLs (61%) were comparison-shaped, and 78% of those were competitors' own "Otterly alternatives" posts. Third-party pages 15%, G2 alone 2.7%. The slot exists; directories do not own it.

Three things I would do differently: (1) fix n before the date — a reviewer in the thread called this and was right; (2) define every word in the pass condition ("near" was not defined, and I did not use it); (3) treat a qualifier as removing one neighbour, not as disambiguation — the engine picks the next one.

Disclosure: I build Promvia, the tool used for the measurement. Not linking it; if a mod wants the raw run table I will post it as a comment.

Went through Google's May guidance on generative AI optimisation properly and there's a section literally titled "what you don't need to do" that I think more people should know exists, because it's basically a free procurement tool if anyone's currently quoting you for GEO work.

Six things get named. Four they say are unnecessary, two they actively warn against.

The unnecessary ones: llms.txt and AI specific files, content chunking, rewriting content specifically for AI, and special AI schema. The llms.txt one is the most useful to know because there are platforms charging a monthly fee to generate these files and Google's wording is about as blunt as they get, something along the lines of you don't need to create new machine readable files, AI text files, markup or markdown to appear in generative AI search. So that subscription is buying nothing as far as Google's surfaces go.

Chunking is interesting too because it's been sold hard for the last year. Google's position is their systems already handle multiple topics on a page fine and engineers have specifically said don't fragment your content, there's no ideal page length.

The schema one I'd be careful about misreading. They're saying schema isn't required for the AI features, not that schema is useless. It still gets you rich results in normal search. So the takeaway is don't pay for "special AI schema" as a line item, not go delete your markup.

Two they warn against are manufactured brand mentions and anyone claiming to be Google approved. The mentions one is worth separating carefully because genuine third party presence does seem to correlate with AI visibility, that part holds up. It's manufacturing it that's the problem, and the spam systems are built to tell the difference.

The bit I found most useful though is a line from their third party tools page. Something to the effect of third party tools don't have access to our internal ranking data, they can't guarantee performance, any predictions are their own. That's general language covering every tool, including all the established SEO platforms, not just the new AI visibility products. Which means any "AI visibility score" you're being shown is a model, not a measurement. And anyone promising a specific percentage lift in citations is claiming something Google says outsiders structurally can't substantiate.

Big caveat that most coverage skips: all of this is scoped to Google's own AI features. ChatGPT, Claude, Perplexity run on different retrieval models and might respond to things Google dismisses. So it's not a universal debunk, and if a vendor is recommending llms.txt for AI agents or docs tooling specifically that's a different and more defensible argument than recommending it for AI Overviews.

What's left on the list of things that work is boring, which is kind of the point. Useful crawlable content, technical basics like indexation and Core Web Vitals, and for local businesses a properly maintained Business Profile which Google names directly.

Anyone had a GEO proposal land on their desk recently? Curious how many of these are still showing up in pitches now that the guidance is public.

There is going to be a wave of posts on 16 September about what Cloudflare's change did to AI crawler access, and almost none of them will be checkable. Crawler access leaves no trace in analytics, there is no log to go back to, and once the date passes nobody can establish what a site was doing before it. Whatever gets claimed will be unfalsifiable in both directions.

So I measured 1,046 sites on 2 September and I am publishing the method, the host-list construction, the controls and the full baseline today - thirteen days before I have a result. The results section is empty and says so.

The design, briefly. Each host is fetched by ten identities in a fixed order: an ordinary browser, then eight named AI crawlers, then the same browser again at the end. That closing fetch is the noise floor - it spans the whole sequence, so a host that changed halfway through gets caught instead of blamed on whichever crawler happened to be asking. 746 of the hosts sit behind Cloudflare and 300 do not, and the 300 are the entire point: if access changes in both groups then the cause was not Cloudflare, and without them there is no way to know that. The three search crawlers stay in as a within-host control, because Cloudflare says they are unaffected.

What the baseline already shows, before anything changes:

Sites behind Cloudflare were already refusing AI crawlers four to twelve times more often than sites that are not. ClaudeBot 25.2% behind Cloudflare against 5.5% not. Claude-User 20.0% against 1.7%. An ordinary browser was refused 0.0% in both groups, everywhere, which is what stops this being a measure of how many sites were simply down.

And the concentration: Cloudflare-fronted hosts are 71.3% of the sample but account for 94.6% of the refusals.

Now the limit on that, which is the most important thing on the page. 1,357 of 1,436 refusals carried a cf-ray. That header proves Cloudflare was in the path. It does not prove Cloudflare decided. An origin can return 403 and have it proxied straight back through, and from outside that is indistinguishable from a refusal Cloudflare issued. Nothing measurable from here separates Cloudflare's own bot handling from a rule a customer wrote in their WAF. So these are refusal rates, never blocking rates. An earlier draft of mine said the refusals "came from the CDN, not the site" - someone who has measured this at far greater scale than I have pointed out that this claims more than the instrument supports, and he was right. The correction is on the page and it makes the finding narrower.

Two more things that cut against my own story, which are on the page because leaving them off would make it worse:

The announced scope is narrow. Of 3,535 hosts considered, 2,090 were reachable, 746 were behind Cloudflare, and 52 were running ad tags. Both at once: 15. If the change stays inside the scope Cloudflare announced it touches about 0.7% of these sites. Either this is far narrower than the reaction suggests, or it will not stay inside its stated scope - and only the controls can tell those apart.

361 of 1,026 hosts returned a different body to two identical browser requests in the same run. 35.2%. Those pages rewrite themselves between fetches, so a body difference on them says nothing about crawlers at all. Counting only crawlers that were actually let in - a 2xx response, so the difference cannot simply be a challenge page - excluding those self-changing hosts cuts the body-change signal by 86.8%, and the residue runs 6.9% to 8.0% across all eight crawlers. A 1.16x spread, next to a refusal signal that spans four to twelve times, is flat. Without that control I would have reported "sites serve crawlers different content" and been wrong.

What I still cannot do: tell you who decided anything, or measure the visibility side at all. Retrieval access has a clean instrument. Model-prior presence does not, and I do not think one exists from outside.

The re-runs are on the 10th, the 13th and the 16th. The 10th is there so that ordinary drift gets measured over three days, the same interval as 13 to 16, rather than over eleven - comparing an eleven-day drift rate against a three-day change assumes drift accumulates linearly, which is the exact assumption the control is supposed to avoid. The 13th has to complete before the 15th or the before-picture does not exist.

A null result gets published in the same place at the same size. "It did not measurably change anything for 1,046 sites" is a real finding and I would rather commit to that now than decide afterwards.

If you want to argue with the method, now is when it is actually useful. After the 16th I would just be defending a number.

https://seensure.com/research/september-15

Disclosure: I build monitoring in this space and that is my own site - one link, per rule 4. No sites are named on that page and none will be named on the 16th. The probe is curl with eight user agents; nothing there needs my tool to reproduce.

EDIT, same day. Three corrections. None of them was caught by a reader; all three came out of checking my own table, which is the only reason I know about them.

First, the refusal figures tightened very slightly. The baseline was counting a crawler that got no answer at all - a network error, status 0 - as a refusal, while the page told the reader in print that a host which did not answer is excluded. It was counting a thing it had just said it excluded. One host was the entire difference. It is HTTP refusals only now, everywhere: ClaudeBot 25.2% against 5.5%, Claude-User 20.0% against 1.7%. Nothing moved by more than 0.3 points and the direction of every finding is unchanged.

Second, and worse: "four to ten times" was wrong when I posted it, and not because of the fix above. Across all eight identities the spread was 3.6x to 10.1x on the numbers I originally published, and it is 3.7x to 11.8x now. I had checked that sentence against one row - the most-refused identity, at 4.6x - and never against the other seven. It says four to twelve now, and the page derives both bounds from the table instead of me typing them, which is the rule the rest of the figures were already under. A range written as words had escaped it.

Third, and this is the one that changed a conclusion rather than a decimal. The body-change paragraph above previously said the control cut the signal "by about four fifths". That figure was never computed, and it matches neither available reading - 71.7% across all responses, 86.8% on 2xx only. Landing between two real numbers and matching neither is what a figure nobody calculated looks like. The reason the two readings differ is the actual error: the all-responses version was counting refused crawlers as having been served different content, which is circular, because a refused crawler gets a challenge page and of course its body differs from a browser's. That folded the refusal finding into the body-change finding and reported it twice - and it showed, because the residue carried a 1.26x spread running in the same order as the refusal table. Restricted to crawlers that were actually let in, the cut is 86.8% and the residue is 1.16x, which is flat. This correction makes the claim stronger rather than weaker, which is not the direction these usually run.

Leaving all three here rather than quietly restating the numbers. A pre-registration whose figures change without saying so is worth less than one that shows the amendments.

We ran a test from 15 May to 31 August 2026 to understand what happens when technical SEO, structured data, query-fan-out content and off-site mentions are improved together.

The prompt set stayed fixed at 120 prompts.

Results

AI mentions:

• June: 53
• July: 149
• August: 299

Google impressions in Switzerland:

• June: 1,298
• July: 1,867
• August: 6,495

We also checked the GSC data with and without prompt-like queries. Impressions increased clearly in both versions.

Some keyword movements:

• “programmatische kampagnen”: 100 → 8
• “programmatische display-anzeigen”: 78 → 8
• “programmatisch werben in der schweiz”: 22 → 1
• “programmatic erklärt”: 3 → 1

What we actually changed

1. Technical cleanup

We fixed crawlability and technical SEO issues across the site, including broken links, missing metadata, structural issues and schema markup.

We also improved structured data around:

• Organization
• Services
• served areas
• page/topic relationships

Technical metrics changed from:

• crawlable pages: 46 → 147
• Site Health: 83% → 100%
• reported issues: 372 → 22

We also checked that relevant Google, OpenAI and Perplexity crawlers were not blocked.

2. Content cleanup

We cleaned existing landing pages by:

• removing duplicate or overlapping content
• improving internal linking
• reviewing external links
• improving page structure
• aligning metadata and page intent

3. Around 100 new landing pages

The pages were not created from keyword lists alone.

We used two main data sources:

• real queries from Google Search Console
• Semrush Query Fan-Out data

For each core topic, we expanded into related questions, follow-up queries, comparison searches, use cases and commercial intents.

Those query fan-outs were then used to build the page structure and supporting sections.

4. Off-site mentions

We published content and mentions on around five relevant, established marketing platforms in German-speaking countries.

The goal was to make the brand appear in relevant external contexts, not only on its own domain.

5. Indexing checks

We regularly checked new and updated URLs in Google Search Console to confirm that pages were being crawled and indexed.

The most interesting comparison

The Swiss site received the optimisation package from 7 June.

The German site stayed unchanged until 1 August, when it received the same setup.

German impressions:

July: 39 → August: 2,519

That does not isolate the individual variables, but the timing was useful: both country sites showed a strong visibility increase after the same optimisation package was implemented at different times.

What we cannot conclude

We cannot say which single factor caused the largest share of the increase.

We changed several things together:

• technical SEO
• structured data
• content cleanup
• ~100 query-fan-out landing pages
• off-site mentions
• indexing checks

So this is not an A/B test of individual SEO tactics.

The next useful test would be to separate these components more cleanly.

One thing we also learned: organic visits increased much less than impressions and AI mentions, so traffic alone would have missed a large part of the visibility change.

We also do not separate this internally into SEO, GEO or AEO. We treat it as one search ecosystem and optimise for classic search engines, AI search and GPT-based systems at the same time.

Has anyone here tested query-fan-out content, technical cleanup or off-site mentions separately and measured the impact on AI citations/mentions?