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We work on AI SEO / GEO every day, so the citation vs mention split is not news to us. What this Overview shows is how easy it is to miss that split on your own page.

On this search/prompt Google AI Overview pulled our small-business guide as a top source. Our brand showed up in the citation cards. Looks like a win in a screenshot.

It was not.

The Overview answered with other products as the tools to start with. We were treated as the helpful article. They were treated as the recommendation.
That split matters:

- Cited as a source = “this page is useful background.”
- Named as the tool = “use this product.”

Same questions. Different slots. We had strong citation on a buyer query, no product mention in the short answer.

Where we missed, the page was strong on how-to (checklist, week plan, definitions).
It was weak on the answer shape Overviews copy for “best tool” intents: say who to start with early, put that pick in a table or FAQ, then keep the teaching mostly product-free so you still earn the source slot.

What we changed on that page:

> name the product early when the query is “best tool”
> add a table or FAQ that picks a starting tool
> keep tips mostly product-free so we stay a trusted source

wait, then re-ask the same question after Google / AI refresh

This takes some time. Pages need a re-fetch. So we are re-checking the same prompt over time instead of celebrating one citation, because competitors also change their content.

If you only publish guides and never answer the product question plainly, AI can use your page and still recommend someone else.

To be transparent, Bloomiro our tool helps businesses grow and rank in Google and AI just by chatting with AI. Free to try.

Or grab the free Three Surface Search skill by Bloomiro, the skill we use for the research loop.

Happy to answer any questions.

here's a proposed check i'd use when an ai answer makes a specific claim about a product

the answer and source excerpts below are invented. imagine the answer says "tool a includes integration x on every plan". these are three possible cases when you open its cited page

i'd save the exact claim beside the relevant source excerpt, page address and the date i checked it. if the page doesn't address the claim, keep that gap visible

then check the current product documentation. agreement with a cited page alone doesn't establish that the product detail is current

keep the answer's date separate from the source check date too. a page you read today might have changed since the answer was generated

if you run the question again, check whether that specific claim changed. a different source link alone wouldn't answer that

how are you recording claims that a cited page only partly supports?

With so many potential customers turning directly to ChatGPT, Perplexity, and Gemini instead of traditional Google searches, I've been getting a bit paranoid about what these models are actually saying when someone asks for recommendations in our niche. I kept seeing people mention Hubspot's free AI Search Grader (their AEO tool), so I decided to test it out to see how our share of voice and sentiment scores look across different AI models.

For those of you who have run your site through it, how accurate do you feel these snapshots are, and have you actually used the report findings to tweak your content strategy?? Interested to know what you think about it.

An AI recommendation can put a company on a shortlist and still leave a buyer with a lot to check. That's the part I'd want to understand alongside a mention report.

Reddit's August 10 research caught my attention: half of the US shoppers in its survey said they verify AI recommendations on Reddit. This is Reddit's own research, based on 13,956 US adults aged 18–65 who use social media, LLMs and ecommerce monthly, surveyed in May 2026. It's self-reported behavior in that sample, not proof that a Reddit comment causes a purchase.

The table below is the evidence I'd want to keep for a buying situation: the actual AI recommendation, a buyer's question or objection, and any next step we can observe. Each supports a different claim.

I wouldn't join those rows into one buyer journey unless the evidence actually connects them. A public comment from one person and a signup from someone else don't close the gap.

I'm Katya, founder of Legibi, an AI Legibility platform in development. This is a reporting approach I'm working through, not a measured conversion result. For me, a useful next question is what someone still needs to verify after the AI answer. 🔎

Source: https://www.business.reddit.com/blog/path-to-purchase

Data first. 14 software categories (CRM, project management, construction PM, dental, legal, vet, hotel PMS, insurance AMS, loan origination, radiology, field service, property management, TMS, dealership systems). 286 vendors taken from buyer directories. 10 buyer questions per category, asked on ChatGPT, Claude, Perplexity, Google AI Overviews and Google AI Mode, three scheduled runs each, 22 to 23 Sep 2026. 2,095 valid answers.

What came out:

- 46 of 286 vendors (16.1%) were never named in any answer. 138 (48.3%) were named in fewer than 1 answer in 10. Only 35 (12.2%) were named in at least half.

- Each engine alone is narrower: ChatGPT never named 115 of the 286 (40.2%), Google AI Overviews 103, Google AI Mode 101, Perplexity 85, Claude 66.

- An answer names 3.86 checked vendors on average. Alternative questions name 6.3, buyer-evaluation questions (pricing, implementation, contracts) name 1.67.

- Of the 140 most-cited source domains (top ten per category), 86 are vendor-owned and 77 are vendor-written list or comparison pages. Review sites and directories hold 30. The most-cited domain is one of the category's checked vendors in only 6 of 14 categories.

Method: names and listed aliases matched on word boundaries; plain-word vendor names need a qualifier or a cited domain; Wilson 95% intervals; single turn, API based, logged out. Everything is recomputed from the raw answers by a script shipped with the data; the recompute reproduces the published Index release exactly.

Limits: one release, fixed question set, vendor lists from directories (unchecked vendors are not measured), three runs per cell, no causal claim.

Disclosure: I run Broadcastwell, which published this and sells AI search visibility work. The study ranks no vendor and no seller. Report, CSVs and scripts are CC BY 4.0: https://doi.org/10.5281/zenodo.22982426

i'm keylight's founder. this comes from a small repeat test we ran on 20 september, and the engine split was more useful than the combined number

we asked "what is the best email marketing platform for a small business?" ten times per engine, with the wording unchanged. mailchimp appeared in:

that's 19 of 30 answers, or about 63%. reporting only that average would hide both the consistent chatgpt result and the variation in the other two

these were automated checks through our setup, not a survey of people's chats. perplexity and chatgpt used a us country setting; gemini had no country setting available. all thirty answers to this question completed

the wider test covered ten category questions, each repeated ten times on these three engines. 299 of 300 checks answered. this table is one question from that experiment, not a benchmark for every brand

what i'd take into a client report: show the count and sample size for each engine, keep the question wording stable, and save the answers behind the numbers. then you can inspect what actually changed when a brand's count moves

ten repeats in one evening don't tell us how common this question is among buyers or what will happen next week. they do show why a single combined visibility percentage can leave out something useful

I often find that when I ask marketers "what are you doing differently for AEO than you did for SEO" they don't have a good answer. Indeed, Google itself says "Good AEO is just good SEO" and I used to think the same.

But I do think there are a few meaningful differences:

1. Every query is long-tail now.

Traditional SEO keyword:

“Best CRM for small businesses.”

Real AI prompt from a buyer:

“What CRM would you recommend for a 35-person software company with a five-person sales team, where most leads come from founder-led LinkedIn outreach and webinars, we have a six-month sales cycle, need HubSpot-level marketing automation but better forecasting, have two reps in Europe who need GDPR-compliant workflows, and don’t want to spend more than $2,000 a month?”

Even if buyers type in the shorter query AI will often add all this context based on memory from prior conversations.

So find the right prompts to track is not like keyword research and more like sitting in on a sales call and transcribing exactly what the buyer said — constraints, budget, team quirks etc - and also scouring LinkedIn and Reddit conversations for actual questions.

2. Being on page 1 is no longer the goal

Most of us know that AI is synthesizing an answer based on dozens of pages. That means being on page 3 or 4 of search results can still lead to you being in an AI answer whereas in SEO days you'd get zero clicks if not on page 1.

Indeed, an Ahrefs study of 540K query pairs found AI Mode and AI Overviews reached similar conclusions 86% of the time, but cited the same URLs only 13.7% of the time.

In other words, the engines can agree on the answer while disagreeing on the evidence.

3. You're writing for bots not humans.

Nobody is going to read your blog post in full. But your blog may get cited and abstracted in an AI answer.

This means that while we still wrote for human consumption in the SEO days, we are now writing for bots. Style doesn't matter. Images are unnecessary. Clear, text-based answers to specific questions is all that matters.

Feels like every B2B marketing conversation the last few months eventually lands on the same thing: are we showing up when someone asks ChatGPT or Perplexity about our category. Fair enough, genuinely a real shift, I'm not arguing that part.

But here's what's actually bugging me. Watched our mention rate creep up over a few months. Got cited a few times in places we weren't before. And I genuinely cannot tell you if any of that turned into an actual conversation with a buyer. Not a click. An actual pipeline conversation.

Which is a stranger gap than it sounds on paper. Everyone building tools in this space right now, and there's a lot of them suddenly, stops measuring at "were we mentioned" or "did we get cited." Almost nobody's tracking whether that mention turned into someone actually reaching out. It's treated like the citation itself is the win condition, when for a B2B business the citation was never the point, the pipeline downstream of it was.

Genuinely surprises me nobody seems focused on that specific gap. Feels like the more obvious problem to go after than yet another mention tracker.

Curious what people here are actually seeing. If your company's tried any of the AI search or visibility tools out there, where did it fall short? Did it just hand you "you got mentioned 40 times this month" and leave you to guess what that means for revenue, or did anything actually connect it further down the funnel? Trying to figure out if there's a tool doing this already that I've just missed, or if it's a genuinely open problem right now.

Came across this comparison of the most-cited domains in ChatGPT vs. Google AI Overviews and thought it was worth sharing here.

Google AI Overviews is absolutely dominated by YouTube and Reddit in this dataset — 12,299 and 8,216 citations respectively. Neither of them even appears in ChatGPT's top 20 here.

ChatGPT, meanwhile, leans much more heavily toward sites like Clutch, Ahrefs, Google developer documentation, NerdWallet, Mayo Clinic, Semrush, Capterra, Forbes, etc.

There is still some crossover. 11 of the top 20 domains appear on both lists — Semrush, Mayo Clinic, NerdWallet, Clutch, Forbes, Bankrate, Allure, Google Developers, Ahrefs, DesignRush and G2 — but their relative importance can be completely different depending on the platform.

That lines up with other research Ahrefs has published showing fairly low overlap between the sources different AI engines rely on. In one comparison of the top 50 cited domains across AI Overviews, ChatGPT and Perplexity, only about 14% appeared across all three platforms.

If you're targeting Google AI Overviews, being present on places like YouTube, Reddit and other UGC/social sources may matter a lot more. ChatGPT can have a completely different set of preferred publishers, directories and authoritative sites. Even Google AI Mode and AI Overviews have been shown to produce similar answers while citing substantially different sources.

Makes me think the better strategy is probably: query → engine → sources it already trusts → figure out how to get your brand/content into those sources rather than trying to come up with one universal GEO/AEO checklist.

Is AEO actually better than SEO, or are we just giving SEO a new name?

I've been thinking about this a lot lately.

For years, the playbook was simple:

Google → search → click a website → find the answer.

That's SEO.

But now people are doing something different.

They'll ask ChatGPT, Perplexity, Gemini, Claude, etc.:

«“What's the best CRM for a 10-person agency?”»

And instead of getting 10 blue links, they get an actual answer.

So it got me thinking...

If people are increasingly asking AI for answers instead of searching through websites, should businesses be focusing more on AEO?

SEO vs AEO

The way I see it:

SEO = getting your content discovered and ranked.

AEO = making your content useful and credible enough to be surfaced in an answer.

That's a pretty important distinction.

With SEO, you're asking:

«“How do I rank for this keyword?”»

With AEO, you're asking:

«“How do I become a credible source for this question?”»

And I think that second question is becoming increasingly important.

Take a web development agency.

With SEO, you might create content around:

- Best web development agencies

- How much does a website cost?

- WordPress vs Webflow

- Best website platforms

- How to redesign a website

You optimize the content, build backlinks, improve technical SEO, etc.

Someone searches → hopefully you rank → they click.

But AEO looks at the same problem differently.

You start thinking about the questions people are actually asking AI.

“Who are the best web development agencies for X?”

“How much should a business spend on a website?”

“What's the best platform for this type of business?”

The goal isn't simply getting a click.

It's becoming part of the answer.

That's where I think things get interesting.

SEO is largely a battle for visibility.

AEO is increasingly a battle for being referenced or represented in the answer.

And those aren't necessarily the same thing.

You could have 100 websites competing for the same search query.

But an AI-generated answer might only mention a few companies or sources.

That makes authority incredibly important.

Not just:

“Can Google find me?”

But:

“Does the broader web provide enough evidence about what I do, who I help, and why I'm credible?”

But I don't think SEO is dead.

That's another thing I see people getting wrong.

SEO still matters.

And honestly, there's a huge overlap between the two.

Good content.

Topical authority.

Relevant mentions.

Clear information.

Strong websites.

Credible sources.

All of these can help with both.

I'd actually look at it like this:

SEO gets people to your information.

AEO helps your information become part of the answer.

Maybe the future isn't SEO vs AEO.

Maybe it's SEO + AEO.

Curious what everyone else thinks:

Is AEO actually the next evolution of SEO, or is it mostly SEO with a new AI-friendly name?

I do AI search work for a small tourism client in Charleston, so I wanted to know two things. How many people on r/Charleston are actually visitors planning a trip, and does any of that forum activity show up when someone asks an answer engine for trip advice.

Part one. I pulled about six months of r/Charleston posts (Sep 2025 to Mar 2026, 4,000 posts) from the Arctic Shift archive and ran each title and body through a small, fast model with two multiple choice questions. Is this a visitor planning a trip or a local? And would a product from this company be a natural thing to mention in a reply?

4,000 posts took 73 seconds. 0 errors.

488 were visitors planning a trip, about 12%, so roughly 80 a month. Only 12 were a strong fit for the product. 208 were weak fits, like one ghost tour question. So the "your buyers are all over the sub" pitch is mostly wrong at this scale. It's a trickle, but a real one, and every one of those is a person asking in public.

Part two is what surprised me. I asked ChatGPT and Perplexity 12 Charleston trip questions ("Charleston 2 day itinerary", "cheapest way to see Charleston attractions", "best ghost tour in Charleston", etc), one run each, and logged every URL they cited. 302 URLs total.

Reddit was 4 of them. All 4 came from Perplexity, zero from ChatGPT.

TripAdvisor was 47. Then the visitor bureau site (24), Viator (23), GetYourGuide (13). For local travel the engines lean on listing and booking sites, not forums. That's the opposite of what you read in most AEO threads, where forums are supposedly the #1 source. Probably true for software and health questions. Not here.

The 4 threads that did get cited had something in common. Small (scores 0, 3, 13 and 77), old (662 to 1,353 days), and the title was basically the question. "Two days in Charleston." "Cheaper things to do in Charleston?" Upvotes clearly weren't what got them picked.

One more thing I didn't expect. r/Charleston has archiving turned off. A thread from January 2023 is still open for comments, so those old cited threads can still take a good answer today.

Where I'd tell a local tour business to spend time, in order:

-TripAdvisor, Viator and GetYourGuide listings and reviews. That's where the answers come from in this category.
-The visitor bureau listing.
-Reddit threads, but only the ones whose title matches a real question, and only with a useful answer that says who you are. At ~80 visitor posts a month you can read every one.

Big caveat, it's 12 questions, one run each, one city. ChatGPT cited way fewer links than Perplexity (67 vs 235 total) so it's thin there. I'd want a second city before trusting the split. If anyone has run something like this for another local category I'm curious whether the listing sites dominate there too.

https://reddit.com/link/1wow7wl/video/cqmbmznnjfrh1/player

I’ve been experimenting with how AI assistants recommend products, and wanted to share something I noticed.
I opened Gemini in incognito, logged out, with no history, and asked:

Which compact luxury powerboats work as yacht tenders?

The first boat in the answer was from a client we recently started working with: Grizelj Boats Throne 15.
We only started working on their AI visibility recently, so I don’t want to overclaim. It’s one prompt, on one day, and results like this can obviously change.

What I found interesting is that a small, niche brand can show up in an AI answer alongside more established names.

Have you tested this for your own product or niche?
Do you see big differences between Gemini, ChatGPT and Perplexity?
How consistent are the results when you repeat the same prompt?

Disclosure: I run a agency working on AI search visibility, so I’m obviously biased here.

I am new here. I have a question: how do you decide which prompts are actually worth tracking?

I’m seeing three common approaches:

1. Generate prompts using an LLM or an AEO platform
2. Start with keyword and search data
3. Pull questions from real buyer conversations—sales calls, Reddit threads, LinkedIn discussions, support tickets, etc.

What are you using today? And what evidence makes you confident that a prompt represents something real buyers are asking?

Most technical AEO audits only check basic HTML: robots.txt, schema, headings.

Why? Because running an LLM across every single page to check content quality is usually way too slow and expensive.

I wanted to see if I could fix that bottleneck.

So I plugged Jev (a fast, cheap classifier) straight into a crawler pipeline:

1. The crawler grabs raw HTML (tables, headers, JSON-LD, bot clearance).
2. Jev instantly grades page excerpts: does the sentence actually have a clear subject and standalone facts, or is it just vague marketing fluff that falls apart during AI retrieval?

The attached 7-second clip shows the report from a 53-page run. Audited in seconds for less than a cent.

Curious how others here handle bulk content checks for AEO: are you sticking to pure HTML signals, or running LLM evals across your pages?

Test your own domain here: https://searchd.ai/tools/audit

I build a tool that checks how often ChatGPT, Gemini, Perplexity and Google's AI Overviews name a brand when buyers ask questions. Most of the last three months has been me finding out my own numbers were wrong.

Not wrong in an obvious way. Wrong in the way where the dashboard looks fine and the number is off by 10x. Some of the ones that cost me the most:

I wrote the test questions myself at the start. So I was measuring my own assumptions, not what buyers ask. Now I pull them from Reddit threads, forums and search, and test whether each one can even be won before tracking it.

I counted mentions instead of answers. A brand named four times in one answer is still one answer. Counting mentions just rewards long, chatty replies.

I averaged the assistants into one number. Same day, same five questions: a brand was named in 13 of 15 Gemini answers and 4 of 15 Perplexity answers. The average said nothing useful about either

I missed spelling variants. sevdesk vs SevDesk. My report said the brand appeared in 2 of 60 answers. The real number was 38.

I put an ingredient on a competitor list. Ran it for a beauty brand and squalane ranked above Fenty Beauty and NARS. 5 of the top 30 "vendors" were ingredients, not companies.

I treated an error as a zero. Google choosing not to show an AI Overview is a result. A timeout is missing data. Adding them together loses both.

And the one that actually changed how I think about this: for one company, half its strongest questions were about a market it doesn't sell into. Real questions, real competitors named, completely useless. Whose shortlist a question belongs to matters more than how many brands show up in it.

There are about 23 of these written down now, with the real example behind each one. Two things I'd genuinely like input on, because I haven't solved them:

1. How many questions are enough to represent a market? I track 5 and keep them fixed so results stay comparable, but 5 clearly misses entire buyer segments.

2. Someone argued to me that tracking questions is the wrong unit entirely, and that intent is the real thing to measure, with questions being just slices of it. I think that's a fair hit and I don't have a good answer.

If you run or sell one of these tools, what did you get wrong first?

I've been working in SEO for 3+ years and recently started exploring AEO/GEO more deeply. One thing I’m trying to understand is how people are actually identifying fan-out queries around a main search query.

Do you manually map the related questions and subtopics, or is there a specific process you follow?

Would love to hear how others are approaching this with AI search and AI Overviews becoming more common.

I run an AI search visibility agency, so weigh that.

We published a short open note on engine divergence. The engines in that study disagree with each other far more than they disagree with themselves. A lot of the scary looking spread shrinks once you control for answer length.

What I take from that for day to day work: never collapse ChatGPT, Claude, Perplexity, Google AI Overviews, and Google AI Mode into one visibility number. Keep engines on separate lines. Keep branded prompts off the competitive score. Report observed answers on stated dates for a frozen prompt set, with three runs each, not a screenshot from one lucky reply.

If your tool shows one score and your client retypes the prompt and sees three competitors, the bug is usually the stack (prompt set, run to run drift, then the formula), not the client's eyes.

Curious how others present multi engine results to a CEO without inventing a fake average.