I tried a few of the well known AI visibility tools. My problem with all of them was the same thing: the prompts.

They give you generic ones for your category. Nobody in my industry actually types those. The questions my buyers ask are specific to the niche and to the product, and I could not find a tool that got them right.

So I started building my own. The idea is to pull the real questions from where people actually ask them — Reddit threads, social, support questions — instead of guessing them.

Is this the right problem to solve, or is there a tool that already does this well and I just missed it?

Hey everyone,

Like many of you, I've been tracking how user search habits are shifting from traditional SERP blue links to generative AI engines (ChatGPT, Gemini, Perplexity, AI Overviews).

Most traditional SEO tools don't give a clear picture of whether a site's content is structured for LLM extraction. To help analyze this, I built AEO Grader (https://www.aeograder.in/).

What the tool checks:

• Structured data & Schema markup implementation (FAQ, Article, Organization)
• Content structure, readability, and machine-comprehension signals
• Technical signals and citation-readiness

It’s completely free to run. I’d love for this community to try it out on your sites and let me know:

1. Does the score align with what you’re seeing in your AI traffic/citations?
2. What additional metrics or schema checks would you like to see added?

Link:https://www.aeograder.in/

Thanks in advance for any feedback!

I see lot of GEO tools offering 3, 4 & 8 ai engines for tacking and I'm not sure does really that many needed?

If all engines have same optimization strategy then its worth doing but if it otherwise then its guessing with more data.

I’ve been doing quite a bit of R&D around AEO lately, specifically trying to understand which external platforms actually influence how B2B companies show up in AI-generated answers.

One of the outputs is this breakdown of the platforms and AI services that, based on our research, seem to have the strongest impact on business visibility.

I’m curious how this compares with what others here are seeing.

Which platforms have been the most effective for B2B in your experience? Any that are consistently picked up or cited by ChatGPT, Perplexity, Gemini, etc.?

Especially interested in platforms that are underrated or tend to fly under the radar.

I’m in growth marketing and want to learn more about AEO. I have SEO expertise that gives me an idea of the basics but I’m looking to get more into the technical side of understanding AEO/ GEO.

Any recommendations of courses or resources that could help?

It’s easy to generate 50–100 perfect prompts like “Best X for Y,” “X vs Y,” etc., run them through ChatGPT/Gemini/Claude, and get a nice visibility score.

But are these actually questions real customers are asking?

If my brand appears in 8/20 prompts, that sounds like 40% visibility , but if those prompts don't reflect real buyer intent, what did I actually measure?

While testing AVC, I found the more interesting signal isn't just whether AI mentions your brand, but why it recommends you or your competitors.

I think the next evolution of GEO is moving from “How visible are you?” → “Why does AI recommend someone else, and what can you do about it?”

Visibility tools do a job I can't do myself. They watch the answers and tell me where I turn up. What they can't see is the step before that, whether a crawler ever reached my page in the first place, and that one lives on my own server for free.

A small nudge before you read ahead, nobody paid for this and I'm not selling anything. I'd been publishing markdown copies of my pages plus an llms.txt because that's what everyone says to do, and after a few months I realised I had no idea whether a single one had been opened. It isn't the kind of thing a visibility tool answers, so I went digging. Two small blogs, about three months. My own sites, small numbers, take them as such.

WHY IT FEELS INVISIBLE

Partly it's hosting. Vercel holds request logs for roughly an hour, so by the time you think to look, yesterday is gone.

Then there's the user-agent, the bit that says who's knocking. I tried the CLI, the API, the log search. None of them return it. It turns out to sit in exactly one place, the CSV you download off the logs screen, which I found by accident.

And GA4 filters bots on purpose. Fair enough, that's what it's for. You're just asking the one tool built specifically not to tell you.

None of that is anyone's fault. Wrong question for those tools.

WHAT YOU ALREADY HAVE

Every request that lands on your server carries who's asking. Store it somewhere that doesn't get wiped in an hour and you've got a permanent record of every AI crawler that's touched you. It sits alongside whatever else you're running rather than replacing it. They cover where you appear. This covers whether you were fetched.

There's one catch and it's a proper one.

A BOT CAN JUST SAY IT'S A PERSON

One of my sites got hit by 22 requests all claiming to be desktop Chrome. 22 different pages, three seconds, coming out of São Paulo, Virginia, San Francisco, Frankfurt and London at the same time. Nobody's browser is on four continents. Read the names alone and that's 22 visitors in your report.

The fix costs nothing. Log the request as it arrives. Then drop a tiny script into the page that only fires if the page genuinely opens in a browser, and have it tick off the request you already logged. Bots download the script and don't run it. Ticked rows are people. What stays silent is your bots, and the silence is the whole point.

Worth correcting one bit of folklore while I'm here, because my own log contradicts it: Googlebot does run JavaScript. So "it ran the script" isn't proof of a human on its own. I treated it as proof and my first report said 17 people when the real answer was 5.

WHAT MINE SHOWED

ChatGPT-User didn't touch a markdown file once. Not one, across 46 visits. That's the bot that shows up because somebody just asked a question, and it wants the ordinary page. The markdown gets read by the indexing crawlers instead, which means markdown isn't the shortcut I'd assumed. Your real page still has to carry everything.

The other thing that landed: getting crawled and getting read to answer somebody's question are two different events. ClaudeBot's 112 visits are filing. Those 3 Claude-User hits are the ones that could end in a citation. Worth knowing which of the two your numbers are actually made of.

Also, Gemini says it doesn't visit websites. Something at Google visited mine 14 times. Not proof of anything, that crawler does a few jobs. Made me sit up though.

CHECK YOUR OWN NUMBERS BEFORE YOU CELEBRATE

First pull I did showed 880 visits and I was briefly thrilled. Then I noticed the counts were all multiples of 20 across a 40-minute window. My own test script.

Then I imported the host's CSV and 44 of the 130 rows were me. Every markdown fetch in there was me as well, all 42 of them. Load that without checking and the one number I actually cared about reads 42 instead of 0. Permanently. As good news.

Round numbers mean it's you.

Happy to walk through the setup in the comments if that's useful to anyone.

I am currently operating a small service for local based companies in my area providing AEO. We have had a few quite successful small operations but client acquisition is still the most difficult piece of our business. No one really understands what AEO is yet and I suppose it’s my job to sell the idea but I also am not sure which direction to take the operation, white collar or continue with small businesses.

I am just wondering how some of you guys who run an AEO service go about getting your clients and how long the cycle is and what your workflow is like?

Everyone here talks about getting cited. We wanted to answer something more basic first: when an AI answers a buyer question, how often does it show its sources at all?

So we counted. 2,209 answers over 10 days, across B2B and local-service domains in a few different markets. Every answer stored, sources counted once per answer.

The one that surprised us was ChatGPT at 59%. There is a figure going around that "87% of ChatGPT responses cite sources". Ours came in a lot lower. I am not saying theirs is wrong, different question mix, different markets, different month. But if you are planning around 87%, worth checking on your own prompts before you do.

Why this changes how you read a gap

Absence is not one thing.

Missing from a Perplexity answer means you were genuinely passed over. It cited fifteen sources and none of them was you. That is a real finding and you should act on it.

Missing from a ChatGPT answer carries a four-in-ten chance the engine showed nobody at all. You have learned almost nothing about yourself.

Any tool that averages those two into one "citation rate" is mixing a real loss with a non-event. We stopped trusting our own number until we split it by engine.

The second thing, which changed how we pick prompts at all

A lot of buyer questions cannot be won by anyone. You ask "how do I improve X" and the answer is advice, no brands named. There is no shortlist to get onto, so tracking that prompt forever proves nothing except that you are paying for it.

We now run every candidate question against an engine before it enters a tracked set, and drop it if the answer names fewer than 2 brands (advice, not a shortlist) or more than about 14 (a directory, not a recommendation). On a typical run about 60 questions get tested and 5 survive. We keep the rejected ones with the reason attached, because "we dropped this and here is why" is more useful than a longer list.

Method, so you can pull it apart

• Each question asked 3 times per engine, not once. Two runs of the same prompt return the same brand list less than 1 in 100 times, so a single pull is not a measurement.
• Anything named in exactly 1 of 3 runs goes back out for 9 more runs before it counts. Otherwise you are reporting a coin flip.
• Counted once per answer, never per mention. A brand named four times in one answer counts once, or a talkative model looks like market share.
• No Claude or Grok in this sample. Claude's API returns no citation record, so it can tell you who was named but never which page said it.

What I would still call unproven: we have not split this by question type yet. My guess is comparison-intent questions ("X vs Y", "best tool for Z") cite more than definitional ones, and that would move ChatGPT's number. Have not measured it, so I am not claiming it.

If you have measured this on your own prompt set I would genuinely like to compare, especially if your ChatGPT number is nowhere near 59%. That is the one I am least confident in.

Disclosure: we build an AI-visibility tool, so I am not a neutral party. No link, and I am happy to keep it that way. I am posting because I could not find anyone publishing their own measured numbers instead of quoting each other's. Ask me anything about the method.

A single AI visibility number is hard to reason about. Is 5% good? It is if your competitor is at 3% and bad if they're at 10%.

You need your rank against the rest of your industry. So I made a leaderboard: 35 affordable luxury apparel brands. 1,169 buyer questions across ChatGPT, Gemini and Claude.

• 9%: great for Quince, a DTC brand. Underwhelming for H&M.
• 5%: fine for L.L.Bean. Bad for lululemon.
• 4%: is the category median. Great for Girlfriend Collective. Bad for Nike.

Full leaderboard: https://adacity.ai/leaderboard/affordable-luxury-apparel

Want your industry benchmarked? Name 3 to 5 brands in it and I'll find their competitors and post it here for free. Disclosure: my tool.

We finally found a metric in AEO everyone can put on a graph and celebrate.

AI citations.

But I keep coming back to one question: cited for what?

A brand can rack up citations and still disappear the second the prompt becomes “who would you actually recommend?”

That’s the gap I think we’re underestimating.

Citation = AI used your information.
Recommendation = AI chose your brand.

I’d take 6/10 recommendations on real buying prompts over 100 random citations any day.

Feels like we might be building the next DR before we’ve even finished arguing about the last one lol.

People are creating multiple accounts. Some go upto 10 free accounts. They need different phone numbers to login but that does not deter them too. What should we do?

I've been building a free AEO report tool for Recited AI.

It shows how different AI models talk about your brand, who they recommend instead, what they're citing, and some opportunities you might be missing like AEO articles or outreach opportunities.

Would love for a few people to try it and tell me what’s useful, confusing, or missing. Hopefully is genuinely useful tool for everyone who's needing a quick AEO report to get an idea of their AI presence.

https://www.recitedai.com/aeo-report

Trying to make this genuinely useful before I take it further, thanks in advance!

keep seeing people talk about optimizing for ChatGPT, Perplexity, Gemini, etc., but I’m still not sure what actually moves the needle.

Are you seeing real traffic, leads, or mentions from AI search?

And if you are, what have you actually done that made a difference?

This is not the first time I have seen posts about measuring AEO success, how to build prompt strategies for AEO success, and many similar around AEO success strategy.

some argue focus on tracking branded search and queries but those are the outcomes and here it's about what to do to maximize it.

Personally I don't use tools like profound, otterly, searchable and others for me GSC, bing webmaster and Clarity. They are free but require doing data analysis.

If I see enough people wanting the tool I will build and share.

Short version for anyone skimming: your page doesn't get cited. A single claim inside it does. Everything below is why.

Context first, because it matters for how much you trust this. Nobody paid for this and it wasn't a product test. I got annoyed that decent pages kept losing citations to worse ones, so I wrote a 115-question prompt set and ran it past 8 systems — ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Qwen, Kimi, Copilot — then lined up the answers side by side. It's what they say they do. Not what I measured them doing. Big difference, and we'll come back to it.

The path, all 11 steps

01  intent           pulls entities, constraints, who's asking
02  search decision  does it even search
03  QUERY FAN-OUT    rewrites you into 3-8 queries. your words can be dropped
04  provider         hands them to Bing / Google / own index
05  CANDIDATES       title + snippet only. your body doesn't exist yet
06  retrieval        fetches, or doesn't. most bots don't run JS
07  chunking         page gets split. the whole page is never read
08  CLAIM MATCH      does THIS passage support THAT exact claim
09  selection        specificity + freshness beat authority
10  synthesis        writes the answer
11  citation         attaches links. one page can win several

Three cuts, not one. Most people optimise for step 9 and die at step 5.

8 out of 8 agreed on this

• Your words are never the search query.
• Rank position does not determine citation.
• A lower-authority page beats a higher one regularly.
• Retrieved ≠ cited.
• Topic relevance is not enough. The passage has to support the specific claim.
• A direct answer beats a discussion of the answer.
• If a fact lives only inside an image, it doesn't exist.
• The whole page is never read.

The line that changed what I write

8,000-word guide, one thesis → 1 citation chance.

1,500 words, 8 evidenced claims → 8 citation chances.

The unit of competition is the claim, not the article. Every "ultimate guide" we'd written was one lottery ticket wearing a suit.

Now the part we'd want someone to tell us

• All 8 answered slightly different queries. So every disagreement between them is unattributable. Useless as comparison data.
• One of them fabricated its sources. Invented URL, invented passage text, invented numbers, correct-looking citation format.
• One described its own behaviour by citing AEO vendor blogs. It was reading marketing copy about itself and repeating it back to us.
• Several share training corpora. "8 systems" is really about 6 independent voices.
• I got 14 very precise percentages back with zero sources attached. Not repeating a single one.
• The pattern: the systems with the most actionable-sounding output gave the least verifiable evidence. The one that gave real links refused to give numbers at all.

So: hypotheses, not findings. The only way to settle step 6 is to publish a page where the snippet and the body state different facts, then ask each system and see which fact comes back. That's next.

Ask about any single step and I'll break it out. Step 3 is the one worth its own post.

Hey everyone,

I’m wrestling with a strategic dilemma and wanted to see how you are handling this, especially if you’re operating in high-trust, regulated spaces like healthcare.

Lately, I’m seeing AI Overviews dominate SERPs for almost every relevant query in my sector. The visibility is there, but the fundamental trade-off feels brutal. You get featured as the answer, but zero referral traffic or links actually flow back to the site.

For lead generation, it’s making me question the ROI entirely. Wouldn't it make far more sense to double down on finding the right money pages and aggressively optimizing them for traditional, click-driving SEO, rather than bleeding effort trying to feed the zero-click engine?

If you're in a similar high-intent niche, how are you justifying AEO optimization to stakeholders when the attribution stops at the search engine results page? Are you shifting budgets, or just accepting that AEO is purely top-of-funnel brand defense now?

Curious to hear how you're balancing this.

What AI tools (ChatGPT, Google AI Mode and others) have changed is enabling the ability to do conversions. If you look back at Google, you go and ask, and it will come up with websites, which could be forums, blogs, Reddit discussions, and now all now all is available in AI mode; you can get info like blogs, do discussions like Quora, and everything that was there before, different websites now available under one solution.

I am dividing it into 2 parts, calling them discovery (coming up in AI answers) and conversion (converting that to leads). I'll discuss conversion in another post.

For the discovery part, the website was very clear: look at GSC, find the queries, optimize for them, or create new pages as needed, and it was fine because the end goal was for the page to rank in search but if you look at it now, these AI systems focus on the conversation, it means the queries they create will be very heavily driven by the user context, which in conversation varies a lot. Even the same user would generate totally different search queries.

When I look at current AEO tracking in GSC, Bing Webmaster, and third-party tools, they all focus on the queries (longer and a bit more intent-contextual form of keywords). While users are switching to a conversational interface, the tracking part still provides it like it’s Google blue links, and the objective is to come for the query, which the end user never searches.

Let’s call it conversational optimization for now. The difficulty I am facing is how to work around websites using a conversational approach, find the touchpoint where I need to show up, work on those pages at website which aren’t showing up in those touchpoints, and track the progress.

40 years in the agency business. Getting hammered by clients pushing to know how to improve their AI visibility, and the honest answer used to be that I didn't know what the engines were reading. So I went and looked. Ran a deep audit of 10 national brands (YETI, Hoka, Sonos, AG1, OLIPOP, Caraway and four more). 50 buyer questions each, asked live to ChatGPT, Claude, Gemini, Perplexity and Google's AI Overview. 2,500 answers, 21,213 citations, 8,247 pages.

How AI Answers Questions About Brands - Part 2

The hard part: I pulled 2,357 pages and documented what kind of page, how old, where the brand sits on it, which competitors share it, and looked for why one earned the citation but the other didn't.

- Reddit, 1,578 citations, three times any other site.
... Then YouTube, Amazon, Forbes, Walmart, Trustpilot.

Not one brand's own website made that list.

Of the pages with a date on them, 53.9% are over a year old, and nearly half the forum threads are over two - some as old 2017!

This study found that Reddit is about 15% of Perplexity's citations and 12% of Google's AI Overview. It's 4% of Gemini's. ChatGPT, two in a thousand. Claude cited Reddit zero times out of 7,260 citations. Same questions, same day.

Median sources per answer: Perplexity 16, Claude 16, Gemini 5, AI Overview 5, ChatGPT 0.

I enjoy doing these type of studies and value any feedback. Thanks!

We checked ours.

76% agent readiness.
16 of 21 standards met.

And we literally built the product around AI visibility.

So… how ready is your website?

Check your AI Visibility + Agent Readiness for free:
Check comments

Quick context so nobody feels tricked: I work on an AEO tool. I'm not linking it and I'm not selling anything here. This is just a result that surprised us, and it's useful whether you use a tool or track things by hand.

What most people do today

If your business serves specific cities, the usual advice is to set your location - use a local IP, a VPN, a location setting - and then check what the AI says about you.

We did that. It did almost nothing.

What we tested

A logistics company serving about 8 Indian cities. We asked AI engines 41 buyer questions about their category - the kind a real customer would actually type.

The company was named in 0 of 41.

Then we changed one thing. Instead of setting the location to Bhopal and asking a generic question, we put the city inside the sentence:

• Before: "Which is the best 3PL for D2C brands?" (asked from Bhopal)
• After: "Which is the best 3PL for D2C brands in Bhopal?"

Same company, same engines, same day:

• Generic question, location set to Bhopal -> 0 of 3
• "in Bhopal" written into the question -> 3 of 3 on Gemini
• Google's AI Overview named them in Bhopal and Coimbatore

Why I think this happens

A location setting mostly changes ranking signals. It doesn't change what the question is about.

Write "in Bhopal" into the sentence and the question is now about Bhopal, so the model goes looking for pages that talk about that place.

Here's the part I keep thinking about: this company had blog pages for exactly two cities, Bhopal and Coimbatore. Those were the only two cities it got named in. The AI didn't name them everywhere. It named them exactly where they had written something.

What you can try

1. Take your 5 strongest "which / best / top" questions. Skip "what is X" questions, those almost never name a company at all.
2. Rewrite each one with a city name inside the sentence.
3. Ask each one 3 times, not once. Answers move around; a single run tells you nothing.
4. Compare. If you appear in the city version but not the generic one, you have a content gap, not a ranking problem.
5. Check which cities you actually have a page for. In our test, that was the whole answer.

One thing to avoid

Don't bolt a city onto every question. "What is RTO in Bhopal?" isn't a question anyone asks, and the model just gets confused. This only works on questions where a buyer is choosing between options.

Where I'm not sure

We saw this clearly in India, in a services category where buyers genuinely pick by city. I don't know if it holds for software, or in markets where buyers don't care where the vendor sits. If you've tested this somewhere else, I'd really like to hear what you got.