r/aeo

I put a page on a test server, blocked it in robots.txt for every AI user agent I could find a name for, then went and asked eight assistants to open it and tell me the code printed on it. Also kept a second page unblocked so I could tell refusing apart from just never fetching.

ChatGPT, Claude and Meta all refused. Claude even throws a proper error, ROBOTS_DISALLOWED, "Site disallows automated access." Gemini, Grok and Manus all just read it anyway. Copilot and Perplexity refused to even check, so I got nothing useful from those two.

I was curious on Gemini, so I searched and found Google's docs saying user-triggered fetchers generally ignore robots.txt, because a person asked for it. So it's deliberate. .

Grok didn't even ask for robots.txt. Not once, in three rounds.

One domain and three rounds, so take it for what it is, and it's only about fetches a user triggers, nothing to do with training crawlers.

I keep seeing people recommend things like:

• Publishing answer-focused content
• Building third-party mentions
• Getting listed in comparison/listicle pages
• Building Reddit presence
• Adding schema
• Improving entity consistency
• Updating existing content

But I'm curious about the actual evidence.

For example, if a brand goes from being cited in 10% of tracked prompts to 30% after three months, how do you know which activity actually caused the increase?

Has anyone run a controlled experiment where they changed one major variable and tracked the impact across ChatGPT, Perplexity, Gemini, Google AI Overviews, etc.?

I'd especially love to hear from people who have actual before/after data.

I keep seeing people test their brands across 10 or 20 AI visibility tools, and then be shocked when they get wildly different scores from each one. The problem with that approach is that almost none of these tools show you how they arrive at a number. Without full transparency of the methodology, I don't see how you can compare scores accurately (I don't think it's possible tbh). One tool might track 50 prompts, another might track 5k.

It depends on 4 things and almost nobody tells you which choices they made...

1/ Which prompts they run. Branded queries vs buyer language vs head terms give completely different numbers. None of them are wrong. They're just not comparable.

2/ How many times per prompt. One run in a probabilistic system is one data point. Twenty runs is a rate. Most tools don't tell you which one you're looking at.

3/ Which models they cover. ChatGPT and Perplexity cite completely different sources. A blended score across both tells you nothing useful about either.

4/ How they define visibility. Citation, mention, recommendation, share-of-answer. Tools use these interchangeably. They're not the same thing.

At Profound we try and show the work behind what's going on like which prompts, models and how many runs but I don't think that's standard practice yet.

What would you need to see before you trusted a visibility number?

I recently had a call with a link-building agency, Outreach Crayon, which got me thinking about how we define success in AEO/LLMO—especially when investing in brand mentions and listicle placements.

“Getting mentioned in AI answers” sounds useful, but what would make you confident it is driving real business value?

Would it be:

• Qualified referral traffic from AI products?
• Visibility in high-intent recommendation prompts?
• Assisted conversions?
• Stronger branded search?
• Consistent descriptions of your brand across models?

Curious what people here are actually measuring—and which metrics you’ve stopped trusting.

Disclosure: I’m not affiliated with Outreach Crayon; the conversation simply prompted the question.

All my clients come from posting organic content on social.

I want to add cold outreach on top of it. With AI agents it can do the work of a human sales team.

For anyone doing cold outreach, what’s actually working for you? Cold calls, cold email, DMs, or texting? And what tools are you using to run it?

We’ve been analyzing thousands of shopping and recommendation responses across ChatGPT, Gemini, Claude, and Perplexity, and the biggest takeaway for me is:

Stop treating AI visibility as just a score. Start looking at what causes the score.

One of the strongest signals we found was website retrieval.

Across several brands:

• When the brand’s own website was retrieved, the brand was mentioned 89% of the time
• When the website wasn’t retrieved, the brand was mentioned only 24% of the time

So imagine this:

AI Visibility: 37%
Website Retrieval: 13%
Mention when Retrieved: 92%

That tells a very different story than “your visibility is 37%.”

The AI already seems comfortable mentioning the brand when it reaches the site. The real problem is retrieval.

But retrieval is only one part of it.

We also found brands that were strongly associated with one product category while being almost invisible for other categories they clearly sell.

So two brands can have exactly the same visibility score for completely different reasons:

• One isn’t being retrieved enough
• One gets retrieved but still isn’t recommended
• One is only understood in part of its catalog
• One is being measured against prompts where brands are rarely mentioned at all

The content being retrieved was also interesting.

For one brand, 116 of 165 own-site citations came from blog content, while only 3 came from product pages. One roundup article alone was cited 37 times.

That makes sense when you think about what users actually ask:

“Best [category] brands”
“Best [product] for [use case]”
“[Brand] vs [competitor]”
“Top alternatives to [brand]”

A PDP is often great at explaining a product.

It’s not necessarily built to answer those questions.

Another thing we learned: don’t overreact to a single visibility test.

In one dataset, roughly a quarter of identical prompt/model combinations changed between repeated runs.

So a move from 53% to 47% doesn’t automatically mean something broke. Trends, repeated runs, and confidence matter.

And the same applies off-site.

Instead of assuming “Reddit is good for GEO” or “YouTube is important,” it makes more sense to look at the actual prompts where competitors win and ask:

Which external sources are showing up in those answers?

Sometimes it’s Reddit. Sometimes a niche publisher, retailer, review site, YouTube video, or comparison page.

So I’m increasingly thinking the useful questions aren’t:

“What’s my AI visibility?”

But:

Why is my visibility what it is?
Is AI retrieving me?
Does it recommend me when it does?
Which categories does it associate me with?
Which sources are influencing the prompts I care about?

The score is the output.

The interesting part is diagnosing the inputs that created it.

Curious how others working on GEO/AEO are thinking about this - are you already separating retrieval, mentions, category association, and prompt quality, or mostly tracking one overall visibility metric?

So, I'm seeing a lot of posts, questions, and discussions about AI citations, and I have a feeling we're mixing two totally different things with very different impacts. That's why I decided to create this post.

So, you're not getting cited in AI... ah.... and you want to increase your AI visibility through AI citations.... well... I don't know how to put it nicely, but the fact that ChatGPT pulls your blog post as a source doesn't mean it's going to recommend your brand next time someone asks for the best XX tool.

In fact, it may as well recommend your competitor based on your comparison article (good for them!).

So, my point is that increasing the number of your very own blog posts used as sources in LLMs won't make your brand more recommended - won't get you more traffic (organic traffic is gone for good btw, but that's another story), so stop focusing on citations with your URLs. It's a good side-metric, but not the one that moves the needle.

Now, the other type of AI citations that could actually be beneficial to your brand's (true) AI visibility are the third-party sources (AI citations) LLMs already use for specific prompts. So, these are not your blog posts; these are third-party blog posts, YouTube videos, Reddit or LinkedIn posts that talk about various brands, provide insights about them, pros and cons, share experience, etc.

I've been playing a lot with AI visibility for the past 6 months, first through manual experimentation (still ongoing), and now with Mentionlytics and its AI visibility platform, and spotted a very unusual pattern:

1. Did you know that not all prompts rely on the same domains as sources? I tracked 10 prompts related to the travel industry using Mentionlytics across ChatGPT, Gemini, Copilot, AI Mode, and AI Overview, and overall Reddit shows up as the most cited source... but then you dig deeper and find out that for 4/10 prompts, LLMs don't use Reddit as a source at all... So, you need to go to sources by prompt to see which channel you want to invest the most.
2. Each LLM has its own go-to sources, and you have to track them separately or have a dashboard that shows you sources by LLMs to be able to spot the right source to go for. So, if your customers mostly use AI Mode, you will focus on the sources AI Mode uses.
3. LLMs sometimes don't use external sources. Yeah, sorry... that's the truth. Sometimes (I haven't finished my experiment to get a specific %, but I will edit this post once I do), LLMs won't go out for the info at all, especially for queries like "Best xx for yy". Instead, they will use their pre-knowledge base and give you an answer based on their training.
4. Reddit, YouTube, and Facebook are more used as sources (in general) than well-known media outlets (like Forbes). This was pretty interesting for me, and though I can't say for sure it goes for every industry, you should check if you get the same results.
5. The results will vary depending on the country the prompt comes from. Different countries show different results for the same query, so if you're checking AI visibility manually with your ChatGPT, your results will not be accurate because LLMs will a) personalize it (if you're logged in), b) check sources most relevant to your country. This is important if your target market is not the market you're in. In this case, you'll either have to use a VPN or an AI visibility platform that allows you to choose the country for prompt tracking to get accurate results and the answer to the question: what citations impact answers.

So, my conclusion is: Yes, AI citations are important, but not for counting your own citations, but for finding the third-party citations you might squeeze your brand into, which signals to LLMs that your brand is worth recommending (if other websites or creators recommend you, it's safe for LLMs to do so as well).

I've been updating our product pages to try and earn more AI citations, and now I'm stuck on the tracking side.

Google AI Overviews are easy enough. I can see whether we're mentioned, roughly where we rank, and whether we get a link. ChatGPT and Claude are where I'm lost.

A few things I've run into:

• Free/default models seem to give templated answers that mostly name the big websites in the market, and the underlying data feels stale.
• But the newest, most expensive models probably aren't what most real users are on either, so testing against those may not reflect actual visibility.

So a few questions:

1. Which specific models do you test against for ChatGPT and Claude, and why those?
2. What nuances have you hit while tracking?
3. How are you automating it? API calls on a schedule, a paid tool, something manual in a spreadsheet?

Thanks.

Running GEO tracking for a B2B SaaS product and hitting something that doesn't add up. the citation volume across AI answers (tracked via a GEO monitoring tool) has been rising all year, but actual referral traffic and conversions coming from ChatGPT specifically have dropped sharply since mid-June — down roughly 75%. The drop is concentrated almost entirely on the homepage; other pages (blog, feature pages) have held flat.

We've ruled out a few things on our end — no page removals, no obvious competitor ramp-up we can find, and it's not being cannibalised by a paid ChatGPT ads(the organic decline started weeks earlier).

Left with two working theories:

1. Some platform-level change in how ChatGPT surfaces/links citations for broader queries
2. We're getting displaced from the "recommended" pick by a competitor while still technically showing up in the answer set — being named but not chosen- Trying to figure how to compare this only for the homepage- no luck yet.

Has anyone else seen this pattern — citation counts going up while actual referral traffic/clicks go down? Curious whether this is a wider ChatGPT-side shift right now or something isolated. Any diagnostic approaches for telling those two apart would be genuinely useful.

Anyone else getting tired of checking the same AI Overviews manually every week? 😅 I started looking for a way to automate it, but a lot of the tools I’ve tried either give me way too much data or don’t seem very consistent. Truffle is one I’ve been testing lately, although I’m still not sure what setup I’ll stick with. Curious what everyone else is doing here - actual automation, a simple spreadsheet, or just biting the bullet and checking manually?

hey guys, I’ve just been starting SEO and I want to get the best tips of how you go from maybe keyword research to have finished article what do you take into account for an article to actually rank on Google and we’re getting some clicks impressions I just want the gold tips of what you guys are doing? That is getting results today with AIO GEO or whatever. I’ve just landed a client. I need to do as your for them and I really want to get them results and do the right thing so what do you guys suggest doing to get the best ranking articles?

Hello,

I am looking for an expert in this field to help with a project in the SaaS industry.
More details in DMs.

Cheers

Google confirmed this month that Gemini now powers every search query by default, not just the AI Mode tab people were opting into. Blue links are still there, but they're not really the interface anymore — you ask, Google answers, you follow up conversationally, and the results page turns into something you scroll past. Zero-click rates in AI Mode are sitting close to 90%+ now, publishers have lost 80-90% of their organic traffic over the last year and a half as this rolled out, and AI Mode query volume has been more than doubling every quarter since launch.

I keep coming back to how quickly the ground shifted under an assumption nobody really questioned. The GEO framing I've seen so far — including in how I was thinking about it — has mostly been "track your citations and sentiment across ChatGPT, Perplexity, etc., alongside your regular SEO." That only works if Google search is still mostly Google search — a stable channel sitting next to the AI stuff, something you keep doing what you'd always done for while treating GEO as the new thing on top. That assumption doesn't hold anymore, and I don't think it's just a tooling gap. It's every business that built its growth engine on the old rules — content teams optimizing for rankings that don't lead anywhere, budgets still weighted toward a channel that no longer sends traffic the way it used to, whole functions built around a feedback loop (rank → traffic → revenue) that's quietly stopped working for a majority of queries. I don't think most orgs have sat with what that actually means yet.

The tools built around "track mentions across four or five AI platforms" now have to treat Google as one of those platforms too, except at a scale nothing else comes close to. And the gap I was asking about in my last post — going from "here's your citation score" to "here's what to do and proof it worked" — just got more expensive to leave open, because this isn't a side channel anymore, it's the default way people find anything. I don't think most GEO tooling, including stuff I've used myself, was built for that. Curious if others building or using these tools — or running marketing/growth at their own companies — are seeing the same thing, or if I'm reading too much into one announcement.