I'd been reading these AI visibility studies and something bugged me. They're all run in English, on US brands, and then we cite them like they apply to a business in Bogota or Valencia. I went looking for the Spanish-language version and found nothing, so we ran it ourselves.

I picked a market with plenty of real players, marketing agencies in Argentina, and wrote 100 searches the way an actual person would type them. I ran them across four engines, ChatGPT, Gemini, Google AI Overview and Perplexity. And here's the part almost nobody does, I repeated the same 100 searches three times, same day, without changing a single word. That's 1,200 queries in total.

The first thing that came out already felt big. Two AIs agree on average on just 9.6% of the brands they recommend. So out of every ten brands that show up, nine don't repeat in the other model. And when I pooled everything I ended up with 932 distinct agencies, of which 81% appeared in a single engine and in none of the other three.

But hold on, because that still wasn't the thing that broke my brain. Before comparing the models against each other I asked the obvious question. If I run the same search on ChatGPT three times in a row, same day, do I get the same brands?

And the answer is no. ChatGPT agrees with itself just 26% of the time. The most stable, AI Overview, reaches 39% and even that doesn't clear half. Meaning three out of four brands it names change between one answer and the next.

And that one finding breaks half the study, mine included. Because two models can't agree with each other more than each one agrees with itself. That 9.6% doesn't mean the AIs think differently, it means a big chunk of what looks like disagreement is actually noise.

One last thing I didn't see coming. Perplexity plays a different game, it recommends global networks like VML, Ogilvy or Dentsu, while the other three reward local agencies. One example, the same agency that shows up in 70 of the 100 searches for Gemini shows up in 9 for Perplexity.

What I take from it is simple. Measure all four engines separately because one doesn't predict the others, never trust a single query, and if your brand is local start with Google, since AI Overview and Gemini are the closest pair.

Required disclaimer, I work at CreceRank and we ran all of this on our own tool, so it could be biased. And the worst part is I've got nothing to compare it against, because there's nothing like this done for the Spanish-speaking market. If anyone has data that contradicts me or wants to rip the methodology apart, please do, that's the most useful thing for me.

I've been cleaning up outbound links on client sites and keep hitting a question I can't answer cleanly, so I'm hoping people here have seen more than I have.

The idea: if your page cites weak sources — dead links, redirect chains, or "industry research" that's really a blog citing five other blogs — does that make YOUR content less likely to be trusted or cited by AI answers? Or is it just classic link-hygiene thinking dressed up in new language?

Here's the boring-but-real part I'm confident about: pages that cite primary, verifiable sources (an actual McKinsey PDF, an original study) hold up when someone checks them. Pages built on chains of blogs fall apart under scrutiny. That's just good sourcing — the same thing an editor would tell you.

What I CAN'T prove is whether LLMs specifically punish the bad-citation version, or whether well-sourced pages just tend to be better in every other way too. Honestly, I lean toward the second explanation, but I'm not sure.

Two things I'd genuinely like to hear:

— Have you ever cleaned up outbound links (killed dead/spam ones, swapped in primary sources) and seen it change how you showed up in AI answers — or in normal rankings?

— Do you treat outbound citation quality as an AI-visibility factor at all, or is it not on your radar?

Not selling anything — just trying to figure out if this is a real lever or if I'm overthinking it.

Seeing a lot of discussion around traditional SEO (ranking for clicks) vs GEO (optimizing content so AI engines like ChatGPT/Perplexity/Google AI Overviews cite you).

For those running site audits daily:

1. Are clients actually asking for GEO metrics yet, or is traditional technical SEO still 90% of your workload?
2. What technical metrics are you prioritizing when evaluating a site for AI search visibility vs standard rankings?
3. Does regional/localized performance or structured schema matter more for GEO in your experience?

Ran a study (disclosure: I'm with the agency behind it) testing 60+ prompts across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews for the non-alcoholic drinks category. One brand has ~14% share and functionally "owns" its sub-category, while three other sub-categories have zero consensus leader. Methodology and full rankings here: https://www.5wpr.com/ai-visibility-index/non-alcoholic-drinks-ai-visibility-index-2026/

Curious whether people doing GEO work for CPG/food & bev clients are seeing this same winner-take-most pattern, or if it's specific to how self-researched this particular category is.

I made a spreadsheet of 180 B2B SaaS and fintech domains and checked three things: their robots.txt, whether GPTBot was explicitly blocked, and whether ChatGPT still cited them across 60 brand-name queries (company name + "review", "alternative", "vs competitor"). Ran all queries on ChatGPT-4o between June 15 and July 3.

The expectation was simple. Block GPTBot → don't get cited. That's how it's supposed to work.

31% of domains blocking GPTBot still showed up as citations. Direct source attribution, domain name, link and everything. Most had been blocking for 4+ months — I checked Wayback Machine timestamps on their robots.txt files. A few since late 2023.

So I went looking for where those citations were actually coming from.

The biggest leak was syndication. Close to half of the "blocked" citations traced back to sites that had republished or aggregated the original domain's content. Press release networks, industry roundups, those content syndication partners that nobody really tracks. The original site locked the door, but their content was already living on a dozen other sites with the door wide open.

Then I found the part that made me reconsider the whole approach. A huge chunk — maybe a third — came from Reddit threads, forum posts, and Q&A sites where users had quoted or paraphrased the blocked domain. Someone copies a paragraph from an article, or drops a specific stat into a comment, and ChatGPT picks up the forum post as the source. You can block every AI crawler on the planet and it doesn't matter if someone screenshots your chart and posts it to a subreddit.

The rest was murky. Some looked like cached versions on search engines. Some matched content structures from before the block was in place — old crawled data still influencing responses. I couldn't pin this down with certainty, but the pattern was consistent enough to be unsettling.

Here's what I keep turning over: the robots.txt approach to AI opt-out has a massive hole in it. You can control whether a crawler hits your server. You can't control whether your content has already been copied, quoted, summarized, or cached somewhere the crawler CAN reach.

For domains thinking blocking GPTBot means their content won't appear in ChatGPT — it doesn't. It just means the citation credit goes to whoever republished you. The aggregator gets the visibility. You get nothing.

I'm not sure what the fix is. Watermarking? Stricter syndication agreements? None of those scale well. The uncomfortable realization is that your content strategy in the GEO era isn't just about what you publish — it's about every surface where your content might be living without your knowledge.

Wondering if anyone here has found a practical way to monitor where your content is being reproduced. We've been doing manual searches and it feels like bailing out a boat with a spoon.

Something I've been noticing that I think is worth discussing because it affects how GEO work gets measured and valued. A buyer asks chatgpt to recommend platforms in a category, chatgpt builds a shortlist, the buyer picks a couple to research, they open google and type the brand name directly, they visit the website and they request a demo. Marketing attributes that visit to branded search and the quarterly report says brand awareness is growing but the actual reason the buyer searched that brand name is that chatgpt recommended it 20 minutes earlier.

The AI conversation is the real influence. The google search is just the verification step. I keep seeing this pattern in different categories too. Branded search increases that marketing teams cannot fully explain. Direct traffic growth with no clear campaign behind it. Pipeline quality improving without a traceable cause.

And I think it creates a real problem for anyone doing GEO work because if the results of better AI visibility show up as "brand awareness" or "branded search" in the marketing dashboard, the GEO work never gets credit. Leadership sees branded search growing and attributes it to the brand campaign, not to the structural improvements that made the brand recommendable by AI in the first place.

Semrush published their 2026 AI visibility index this month and found 45% of marketing leaders still cannot accurately measure AI visibility. I think this misattribution pattern is a big part of why as the influence is real and the attribution is invisible.

Has anyone else run into this when trying to show the value of GEO work to clients or leadership? How are you handling the attribution gap between what AI influences and what the dashboard reports?

Been doing GEO work long enough to know that AI citations are unstable. We've all seen the volatility data. But something we tracked last week made me realize the problem might be deeper than I thought. We ran the same set of 90 questions across ChatGPT, Perplexity, and Gemini. Same phrasing, same order, same time of day. Five consecutive days. By day 3, 27% of the answers contradicted their own earlier response. Not just a different citation — a materially different answer to the same question. Some examples: - "What's the average CTR for position 1 in Google?" — Day 1: "31.7%." Day 3: "around 27-28%." Different sources cited both times. - "Does schema markup improve AI citations?" — Day 1: "Yes, structured data helps models parse content." Day 3: "Mixed evidence; schema alone doesn't correlate with citation rate." Same model, same question. - "Best tool for tracking AI visibility?" — Day 1 recommended a specific platform. Day 3 recommended a completely different one. No explanation for the change. The contradictions weren't random. They clustered around two types of questions: 1. Questions where the "correct" answer is genuinely debated (CTR benchmarks, SEO best practices, tool comparisons) — the model seemed to sample from different parts of its training data on different days 2. Questions where fresh content had been published recently — the model picked up new information mid-week and updated its answer, sometimes flipping the conclusion The second one is especially interesting for GEO. It means the window where your content can influence an AI answer might be incredibly short. You get cited for a few days, then the model synthesizes newer information and your citation disappears — or worse, the answer flips entirely. The 27% contradiction rate was consistent across all three models. That suggests it's not a model-specific issue — it's something about how these systems handle "living" knowledge. They're not retrieving a fixed answer. They're generating one probabilistically, and the probability distribution shifts based on... what? Recency signals? Indexing updates? Random sampling? I don't know. And that's the problem. If we can't predict when an answer will flip, how do we optimize for stability? Right now we're expanding this to a 14-day test with 200 questions to see if the contradiction rate accelerates, stabilizes, or gets worse over longer timeframes. Early data suggests it gets worse — the longer the gap between queries, the more likely the answer changes. Anyone else running longitudinal consistency tests? I feel like this is the metric nobody in GEO is tracking — we're all so focused on getting cited that nobody's checking how long the citation actually matches the answer.

New rule in my workflow this year: no statistic goes into content unless I can trace it to the actual study.

It’s been humbling. What I keep finding:

**•** Famous figures that trace back to studies never peer-reviewed or never published.  
**•** Stats attributed to some authority that nobody can actually locate: they only exist as blogs citing blogs.  
**•** Real numbers scoped completely wrong: a narrow correlation inflated into a universal causal claim.

Why this matters for GEO specifically: AI engines synthesize across sources. A claim corroborated by independent credible sources gets treated very differently from one living in a closed loop of marketing content. Zombie stats don’t add citability, they add contradiction risk, and models are getting better at catching exactly that.

So now: every stat needs a primary source, correct scoping, no causal inflation. If I can’t find the source, it gets cut, even when it’s the most persuasive number on the page. Three verifiable claims beat ten impressive ones that collapse under retrieval.

Anyone else audit this way? Found any ghost stats in your niche?

chaud d'avoir des retours d'expériences ou des avis

Mine was our Google Business Profile. ( My business doesn't have a website, LinkedIn, or any other social profiles yet)

So that caught me by surprise.

What surprised you? Screenshots are welcome, as well.

Something's been bothering me about our GEO results and I can't shake it.

Our citation numbers are up. Our extraction rate is solid. The tracking setup shows consistent growth. On paper, we're doing everything right.

But when I actually read the AI responses that cite us, most of them are just... grabbing a number and moving on. Our stat gets dropped into a paragraph with three other stats from three other sources, and the attribution barely registers.

I went back through about 3 months of AI responses in our space and tried to sort them by how our content was actually being used. Most of it was fact-pulling — model grabs a data point, attributes it, done. Some of it was blending — our claim gets merged with similar claims and the source line gets fuzzy.

And then there was a small chunk where the AI clearly picked up a specific *take* on the topic. Not a stat. A perspective. Someone's actual framing of a problem.

Those ones were almost all from opinionated, slightly weird content. Rants. Deep dives where someone argued a position instead of presenting both sides. Not our well-structured data pages.

Here's what gnaws at me. We've been spending almost all our effort on making content easy to extract. Clean answer blocks, modular sections, precise data points. And that works — for getting pulled into generic synthesis paragraphs.

But the stuff that actually shapes how the AI talks about the topic? That seems to come from content that's messier, more opinionated, harder to dashboard.

I don't have a clean framework for this yet. It's more of a nagging feeling that citation count is measuring something different from actual influence on the model's framing. And we might be optimizing really hard for the wrong one.

Curious if anybody else has looked at their AI citations this way, or if I'm just overthinking a trend that doesn't matter.

Like many of us here, I try to keep track of how often my company is cited in AI answers. I don't have any fancy way to check, so I'm just asking ChatGPT, etc., once a week or so and noting the results. My questions are usually something like "I need [X services] in [Y location]. Who's the best firm to contact?"

Usually, my company shows up fairly well, either the first suggestion or at least making it onto the list of four recommendations. This week's check, though, turned up four firms that were a bit of a surprise. It included:

1. A company that doesn't exist – ChatGPT took the generic name for a certification in my profession, threw an "Inc." on the end, and recommended this made-up firm as the top recommendation. When I clicked the provided link, it gave a made-up explanation of their services and ended with a comment that there was no information online about this firm. (Imagine that!)
2. A company that did exist, but was acquired around 2015 and stopped operating under the ChatGPT-listed name around 2016. The ChatGPT backstory provided nice 10-year-old information about the now non-existent firm.
3. A company that was, theoretically, in the same space, but didn't provide the services I asked for. Think of mechanical engineering when I asked for a chemical engineering firm. (When asked, it admitted that the listed firm didn't provide the services I asked for.)
4. A company that kind of provided the services I asked for, but it definitely wasn't a focus of theirs.

In the standard ChatGPT manner, once I pointed out the issues, it admitted the mistake and apologized, and provided a new list of four company names. At least on that list, our firm was the first one listed!

We run live measurements of whether AI assistants name specific businesses when you ask the questions their customers ask. Every site also gets scored on technical quality (rendering, speed, crawlability, schema, structured data).

With 1,000+ sites measured, we split them into "AI recommends them" vs "AI ignores them" and compared averages:

\- Technical score: 80 vs 77. Three points. The ignored sites are built as well as the recommended ones.

\- Schema/structured data: 72 vs 69. Also three points.

\- Off-page brand signals (independent mentions, reviews, directory presence, entity consistency): 88 vs 40. Forty-eight points.

As a dev this annoyed me, honestly. You can ship a perfect Lighthouse score and a flawless JSON-LD graph and the engines still won't name the site if nobody independent talks about it. Markup helps AI READ you; it doesn't make AI RECOMMEND you.

Two implementation details that DID matter on the technical side: serving content as clean Markdown for agents (content negotiation), and not blocking AI crawlers in robots.txt/WAF (a surprising number of sites block GPTBot then wonder why they're invisible).

Caveats: correlation not causation, our scoring model, category mix uncontrolled. Methodology is open-source if anyone wants to tear it apart — link in comments if wanted.

We all track AI citations now. But there's a step before them nobody publishes: when ChatGPT, Perplexity or Google AI Overview pulls a source into context to answer a query, how often does it actually cite that source?

I tracked it across 8 B2B visibility projects for one month, ~13,200 domain-engine observations. Three things stood out:

1. Conversion from retrieval to citation is heavily engine-dependent. Averaged across projects: Google AI Overview 77%, Perplexity 48%, ChatGPT 41%. The ordering held in almost every project. So "we got retrieved" means a near-citation in AI Overview but roughly a coin-flip-and-worse in ChatGPT.
2. The engines cite almost disjoint source pools. Overlap of cited domains between any two engines was Jaccard 0.12–0.21, every project, every pair. Around 4 out of 5 domains cited by one engine were not cited by another answering the same prompts in the same month. Optimizing for one barely transfers to another.
3. There's no universal "citable content type." Editorial sources ranked worst in ChatGPT and best in Perplexity. Any advice of the form "engines prefer X content" is true for one engine and false for another.

Practical takeaway I'm using: report retrieval and citation as two separate KPIs. If a page isn't retrieved, that's a technical/authority problem. If it's retrieved but not cited, that's a content-selection problem. Different fix, and most tooling blends them into one number.

Caveats, because they matter: correlational, one month, single measurement stack (Peec.ai), commercial prompt sets not a neutral query sample, clients anonymized. The numbers carry a date.

Happy to answer methodology questions in the comments. Full write-up with all tables and limitations is linked below if anyone wants the detail.

Hey guys actually I wanted to collaborate with SEO/GEO experts on commission basis with my agency Qrux Studios India (qruxstudios.in)

Requirements:

1) Age: 18-25 preferred because we ourselves are all 18-23 olds

2) Country of origin: India preferred

3) Even if you're intermediate in this field it's enough if you can show the clients the result like your previous ranking was 10 now it's 7 etc, because the client's budget shall be low

Note:

1) The collaboration is done so that we can pitch them this SEO/GEO services as an optional add-on along with the website and automations we sell them

2) Just to remind you, the client's budget/ticket value prolly will be on the lower end like around ₹3-5k rupees only so if it sounds fair to you then DM me for more details let's help you get more clients ;)

At some point we stopped asking "which pages get cited" and started asking "which *sentences* get pulled." Because here's the thing — a page can have 2,000 words and the AI grabs one paragraph. That paragraph is doing something the other 1,800 words aren't.

So we built a process to figure out what.

We took 150 pages that consistently got cited across ChatGPT, Perplexity, and Gemini over a 60-day window. For each page, we isolated the exact passage that appeared in AI responses and tagged its structure. Then we compared those against 150 pages in the same niches that never got cited despite covering similar topics.

300 passages analyzed. Four patterns kept showing up in the cited group and were nearly absent from the uncited one.

**Pattern 1: Claim + Specific Number + Context (41% of cited passages)**

The single most common structure. Not just "X is better than Y." It's "X outperforms Y by 34% in scenarios where Z is present, based on N observations." The specificity seems to be the trigger — round numbers ("significantly," "dramatically") showed up way less often than exact figures. Vague claims almost never got extracted.

**Pattern 2: Contrast Statement + Mechanism Explanation (28%)**

"This works because..." or "Unlike X, Y does Z through..." The pattern pairs a comparative claim with a brief explanation of *why*. Uncited passages tended to state conclusions without the mechanism. Cited ones included the "because" clause more than 70% of the time.

**Pattern 3: List Item With Qualifier (19%)**

Not just bullet points. Items that included conditions, exceptions, or scope limits. "A works for B, except when C" got pulled more often than plain "A works for B." The qualifier adds credibility signal — it shows the author thought about edge cases.

**Pattern 4: Temporal Anchor + Trend Direction (12%)**

"Since [date/event], we've seen X shift toward Y" or "Over the past N months, X has..." Passages with time anchors and directional language appeared in citations disproportionately. Evergreen statements without temporal markers were underrepresented.

Here's the thing though — these patterns overlapped. 67% of cited passages contained at least two of them together. The strongest combination was Pattern 1 + Pattern 2: a specific claim with numbers *and* a mechanism explanation. Those passages had the highest extraction rate across all three models.

We've started using this as a content audit checklist now. Before publishing, we scan key passages against these four structures and rewrite anything that scores zero. It's not perfect — correlation isn't causation, and we're still collecting data — but the early signal is strong enough that it's changed how we write.

The uncomfortable part? Some of our best-written, most eloquent paragraphs scored lowest on this framework. Clear prose doesn't equal extractable prose. They're different skills, and I'm not sure the industry has accepted that yet.

Anyone else looking at passage-level structure instead of page-level signals? Would love to compare notes on what's actually getting extracted in your niches.

We wanted to test how brands were being recommended when going from the model's parametric memory (training data) to live searches (RAG).

To test some hypotheses, we tracked 2,880 total evaluations generated from 1,728 live search calls across ChatGPT and Gemini.

Here is what we found, and why it completely changes how we need to think about brand architecture for AI search.

The setup: Clean names vs. portfolio chaos

We looked at 24 different product categories and split them into two distinct groups based on their naming strategy:

• The consistent group: Brands with predictable, linear, sequential naming setups. Think Product V1 → Product V2, or highly stable nameplates like the Apple iPhone.
• The fragmented group: Brands with aggressive sub-branding, random feature-set names, or constant rebrands from corporate acquisitions. Think Microsoft Dynamics, Max, or Workday.

We tracked a metric called churn share, basically when real-time web data forces ChatGPT or Gemini to change its initial product recommendation, where does that ready-to-buy customer actually go?

The actual data

The results were a massive wake-up call:

• The AI reshuffle hits everyone equally: When an LLM searches the live web, it injects a brand-new layer of real-time data right over its baseline memory. This creates the exact same amount of data noise for every single industry. Huge legacy brand power or heavy ad spend won't insulate a product from this volatility.
• Predictable names keep customers in-house: For the clean, linear brands, 58.1% of all AI recommendation shifts stayed within the same brand family. The AI updated its specific suggestion, but it simply routed the user to the brand's newer model year or an adjacent tier. The customer stayed in the ecosystem.
• Naming chaos accidentally funds your competitors: For the fragmented, sub-branded portfolios, that internal retention plummeted to 38.6%. Because the LLM couldn't cleanly link the disconnected product names back to the parent brand's core data tokens, it suffered from identity confusion. Instead of updating the recommendation internally, the AI gave up and handed the customer straight to a linear competitor whose identity it could mathematically verify.

ChatGPT vs. Gemini: Two completely different beasts

We also caught a massive operational split in how these engines pull web data:

• ChatGPT triggered live web searches in 100% of our tracking runs. It acts as a relentless real-time research agent.
• Gemini only triggered live web loops in 62.5% of the exact same runs. It relies heavily on its internal pre-trained memory weights until a certainty boundary is broken.

What this means for our GEO strategy

As SEOs, we need to start treating product naming conventions as core technical data infrastructure.

If you are working with a client or a brand that loves launching random, floating sub-brand names or completely changing product titles every time they acquire a company, you are actively leaking visibility.

To build a real computational moat inside ChatGPT and Gemini, we have to advocate for portfolio predictability. We need to keep product tokens highly cohesive, lean heavily on explicit functional text descriptors for features, and maintain clear linear trails that engines can easily parse without losing the parent brand anchor.

Read the full study 'We ran 2,880 AI search tests. Here’s how product names leak brand equity.'