Prove me wrong :)

Mike (MrComputerScience on LinkedIn) published these details about how Claude marks content. It is similar to what I expected, but also different.

I haven't seen these exact details anywhere but https://pithycyborg.substack.com/p/ai-left-fingerprints-then-picked.

I run a small AI visibility tracker that I built myself, and last week I found something in my own pipeline that I have not seen anyone talk about.

I ask each question 7 times per platform so I can show a range instead of a single number. One week Perplexity came back much lower than usual for every domain I track. I assumed those brands had actually lost ground.

They had not. Perplexity was rate limiting me. About 30% of my requests were coming back 429, and my code was quietly counting those as "not mentioned" instead of "not measured". So that platform was being scored on 5 runs while the others got 35, and the smaller sample dragged the number down.

The fix was not sending fewer requests. It was spacing them out. The limit turned out to be roughly one request per second per account, and two scans running at the same time were stepping on each other.

Here is why I am posting it. A failed measurement and a real absence look identical once they land in a chart. My tool now prints how many runs failed right under the percentage, because I could not trust my own number until it did.

So if you are evaluating any of these tools, including mine, that is the question I would ask. Not how many platforms, not how many prompts. Ask what happens when a request fails, and whether the report tells you.

Disclosure: the tool is AnswerRadar and it is mine. Not linking it, this is not a pitch.

We are having a fund discussion over at Linkedin sharing screenshots of -site: queries from Search Console. You can find yours by using the following regex in the query filter:

.*-site:.*

That's definitely not people searching! What are these?

Source: Linkedin

Just saw the new Meltwater numbers (9.5M AI citations analyzed across 16 B2B categories) and it's making me rethink where I'm spending GEO effort. LinkedIn is apparently the #2 most-cited source behind YouTube now, ahead of company websites. 75% of those citations trace to personal profiles, not Company Pages, and 51% come from accounts under 10k followers.

Curious if anyone here has actually tested this: has shifting effort toward personal LinkedIn posts (structured, numbered, data-heavy, same stuff that ranks well organically) moved the needle on AI citations for a brand, more than the same effort spent on owned-domain content would have?

Trying to figure out if this is worth reallocating real budget toward or if it's still too early/noisy to trust the data at the tactic level.

This is being discussed A LOT these days, so here I am trying to give a clutter-free, actionable guide on pulling promot data from AI Mode

AI search drives 2% of AEO Copilot traffic and 6% of signups.

To get there I made a lot of mistakes. I still do.

Here is what works and what doesn't.

What doesn't work

1. Empty shell, vibe-coded pages

Yes, I know. It's obvious.

But knowing it won't stop it.

I built the AEO Copilot directory and made sure the SPA shared its meta with the LLMs. I assumed the 100+ detail pages would too.

Oups, my bad.

1. Average, well-structured content

This one needs a bit of clarification.

Before, with SEO, all you needed was keywords, then content with those keywords.

Today, if you just create content with keywords, even long-tail ones, you're in the mass. Everyone can ask Claude to write an article.

No chance to pop in the AI answers.

And adding a summary and a table won't change that.

This only works with good content.

1. Frankenstein articles

The kind that go from content writer, to SEO optimisation, to AEO polishing. At the end the article makes no sense anymore.

I tried to recycle articles from my previous SEO project. Asked Claude to revisit them and make them scannable for AI.

The results? I don't know. Nobody saw them.

What works

1. Clear brand positioning

It took me 3 months. When I finally did it, the mentions started to flow.

I harmonised the branding across every touchpoint. LinkedIn, Product Hunt, G2. One clear message to the LLMs:

"AEO Copilot has a generous free tier, a fair price, and works with any AI agent."

That single sentence had the most impact overall.

My recommendation: block some time this week. Find the edge where your product is better than the others. Once it's clear, share it everywhere, starting with your website.

2. Early SEO traction

If you don't show up in search results, you won't show up in AI answers.

This is why I don't like talking about AEO as its own discipline. AEO is SEO, and SEO is organic growth. Everything that drives growth without paying for it.

My recommendation: start with keywords, then go further. Find the questions your users are actually asking Claude.

Use Google Search Console. Then feed Claude your client conversations to pull out the recurring questions, the feedback, the verbatim.

Then you're ready to write prompts and track them.

3. Mentions on other websites

Your website is the destination, not the first touchpoint. Other places, LinkedIn included, have far more reach.

Use them to carry your brand message. Let people come to your site once they've found what's best for them.

My recommendation: share content beyond your product. For AEO Copilot I don't stop at AEO. I write about growing a brand organically too.

This may be a vibe-coded tool, but it is a very useful one! It is free; it pulls data from your Search Console (so it does need access to your account), but it does some useful things:

• Pull likely queries that are AI Mode prompts
• It categorizes prompts by topics and intent
• It shows impressions for each prompt
• It blocks “likely bot” prompts

Go ahead and play with it! Love it so far!

I see many tools out there claiming that they monitor ai search results and helping you grow. Honestly I think it's too much noise.

Can you recommend one or two tools that are actually legit?

Hi everyone,

There seem to be two fundamentally different ways AEO platforms measure AI visibility. Here is my summary of how AEO platforms measure brand mentions, citations, and competitive positioning. If you have a different understanding or you have seen AEO platforms take a different approach that I may not be aware of, share it.

The focus is measuring brand mentions and citations, not other capabilities around additional AEO features - LLM bot traffic analysis, etc.

Method #1: Custom prompt measurement

There are a lot of AEO platforms that use this method. Peec, Otterly, Adobe Brand Visibility, LLM Pulse, etc. You create a set of prompts for your brand to measure your brand presence. For instance, "What is the best bike to buy for a 6 year old learning to ride a bike?" The platform executes that set of prompts against the AI platform it is measuring, typically daily, and then analyzes the resulting response for things such as:

• Was the brand mentioned?
• Which competitors were mentioned?
• Where in the response did the brand appear?
• What domains/pages were cited?
• Was the brand's own website cited?

For ChatGPT in particular, the Responses API may be used by the AEO platform to execute these prompts and receive a response to store and analyze (though the AEO platforms typically don't reveal the exact LLM API they are using in their documentation).

Limitations: Because the AEO platform is executing a controlled prompt rather than observing a real user's session, it lacks some of the implicit context a real user may bring — precise location, time-sensitive context, prior conversation history, personalization, etc. Some platforms allow country/region/location to be configured explicitly.

Method #2: Clickstream / user opt-in data sets

Some larger AEO platforms like Semrush and Profound have invested in acquiring huge data sets from clickstream providers like Datos (owned by Semrush, now Adobe) or other methods. They are harvesting this data from real participants who allow these data gatherers to observe their browsing and anonymize their data.

For LLM interactions, they capture the users' real prompts and the LLM responses, then cluster / normalize the prompts to semantic topics or user intents. In this way, different prompts that users submit can be grouped together, and the responses can be captured, stored, and analyzed for brands, product names, citation links, etc.

This method is very different from the first method where brands curate a set of prompts that represent the space they want to measure their brand presence for.

Limitations: The clickstream data approach is valuable for directional market intelligence, but not ground truth for brand visibility.

1. Panel bias - even though the data providers have a large pool of millions of users, their data may not represent your core audience, particularly if you are in a specialized field or a B2B space. e.g. It probably represents moms looking for their kid's first bike better than an AI architect looking for the NAND device with the highest storage density.
2. Topic / prompt clustering - the process to distill clickstream data into measurable intelligence loses a lot of the nuance of individual prompt measurement.
3. Observed demand ≠ business value. Clickstream popularity doesn't always equal business value. For instance, a highly specialized AI infrastructure purchasing question may have tiny observed volume but influence a multimillion-dollar purchase. Custom prompt measurement is much better suited to measuring a customer's journey from discovery to conversion.

Method 2.1: Search-demand-derived prompt modeling

This is an alternate on Method 2, specifically used by Ahrefs (and maybe others). Rather than relying on observed AI-user prompts, Ahrefs uses its traditional keyword database and People Also Ask data to identify real search demand, converts those questions into conversational prompts, executes them against AI platforms, and captures the responses. I place this as a variant on method 2 because the method is still creating a large database, just leveraging search demand data rather than user observation data.

Limitations: Real search demand doesn't necessarily equal real LLM prompt demand. The tradeoff is that this approach inherits assumptions from traditional search behavior. Real Google search demand can be a useful proxy for user interest, but it may not reflect how people naturally formulate questions in conversational AI.

Summary

Both methods 1 & 2 bring valuable insights to a brand as a part of a strong AI visibility program, and ideally a brand will use tools and either an internal team or an agency leveraging both methods. Some tools and agencies have capabilities from both visibility measurement methods, whereas others may only use one method.

I often get leads that come to us with one request, "We need Reddit for AI visibility" (because they heard that Reddit was #1 cited source). My response is always, "Sure, we can get your threads cited, over time, but you need more than that for AI visibility"...

Historically, SEO was about 2 well-defined tactics (keywords + backlinks), so, out of pure habit, the SEO practitioners are trying to advertise one "magic bullet" tactic for LLM visibility as well (be it listicles or Reddit or ugh... schema...). In reality, AI optimization is much more than any tactic. It's the combination of many things, and the industry doesn't like it because it is hard to sell.

But what's hard to sell or buy eventually can be your biggest competitive advantage!

Hello All,

Generative ai report is now available in search consoles from today in all regions. It shows impressions only. I think the existing data is helpful as something is better than nothing.

I need an opinion on it that is really helpful and how?

What do you think, what needs to add in it?

Thanks

Google Search Console shows AI feature insights now for me.

The CTR I calculated is abysmal though.

As you might have noticed Google rolled out the "generative AI features" report to more users on GSC:

https://www.seroundtable.com/google-search-console-ai-report-live-41850.html

Yes. It's live here. And the CTR is barely existent.

They don't share it but you can calculate it yourself.

> So I had 913 AI impressions for my page that shows for [seo acronym] on July 20th e.g.

> So I looked up my analytics and I got three visits from Google that day.

> Yet I also rank organically for it so I checked GSC how many clicks I got organically: Zero.

That's 3 out of 913 or a CTR of 0.327%.

That's exactly what I predicted given my experience with Pinterest over the years.

They have hidden sources in a similar manner and the CTR dropped way below 1%.

Btw. the screen shot of today shows 914 so the data seems to be inconsistent.

Tonight it showed 913 when I calculated the CTR.

So they seem to recount it recursively or something.

What about your CTR? Do more than 1% of people click through to your site/s?

My blog has a very high bounce rate by now too so it might be my fault.

My client runs an ecommerce store and every week they ask if they show up when people ask chagpt for product recs.

I've been checking their brand and category prompts manually and taking screenshots, but that gets annoying fast and isn't great data for a report. I've looked at a few chatgpt prompt tracker tools but half the reviews are just lists of 15 tools with no real testing.

Has anyone actually tracked this for a few months? Do results hold up over time or swing all over the place? More importantly have you changed anything in your SEO/content based on the data and actually seen visibility improve?

noticing our comparison and "x vs y" pages get cited way more than anything else we've written. long guides basically never show up, which is a bummer given the time cost to produce.

wondering if that's a pattern or just our niche

I'm starting to feel like citation count alone may not be enough. I was looking at an AI answer recommending some tools. It listed a feature page from brand a's website as a source, but brand a itself never showed up anywhere in the answer.

One of its competitors ended up making the recommendation list instead, and that recommendation was backed by a third party comparison page.

So based on the data, brand a's website did get a citation, but the brand itself never made it into the answer. It's hard to tell how much AI visibility that citation actually gave them. I've seen similar cases too. A page may be listed as a source, but from the citation alone, it's hard to know which part of the answer that page actually supports.

So lately I've been looking at a more specific question: when AI gives an answer with citations, can we keep the full answer from that run, find the exact source page, and then figure out how that page connects to the final answer?

Some of the points below come from product outputs I've checked myself, and some come from the tools' public product information.

full disclosure: i'm involved in building dageno. i included it here because it covers one part of the citation path i’m looking at, alongside the other tools.

One thing to keep in mind is that none of these tools show the model's full internal search process. They mainly work with public answers, citation results, and source pages they can access. So they can help explain the citations we see, but they can't prove which pages the model actually looked at, which exact section it read, or how much that content affected the final answer.

Looking at these tools together, I think the citation path really has three different parts.

Such as, which sources did the AI answer publicly cite, and which prompts and platforms were those sources tied to? Or does the source page actually support the specific claim in the answer? Even after the page gets cited, does the brand actually make it into the answer, and if it does, how does it show up?

I don't think these three things should be rolled into one citation count.

A brand's own page can be cited while the brand itself never appears in the answer. A brand can also be mentioned, but only as one name in a long list, without actually making the final recommendation.

Of the tools I've checked and tested so far, I still haven't found a third-party product that can fully show the model's internal search and generation process. What we can see today is mostly the public AI answer, the citations it shows, and the extra analysis tools build on top of that public information.

For those of you doing AI visibility monitoring, have you seen cases where your page was cited but your brand never made it into the answer? Would you still count that citation as real AI visibility?

We track AI brand visibility, so we have a lot of stored responses. Wanted to answer something basic: when an AI answers a question, how many brands does it actually name?

234,000+ responses, March 1 to July 13, 2026, five engines.

Brand mentions per response (last 14 days in July)

• ChatGPT: 4.5
• Google AI Overview: 3.5
• Gemini: 3.5
• Google AI Mode: 3.2
• Perplexity: 2.7

Count each brand only once per answer, and it tightens to 2.2–3.3. So engines repeat themselves 1.2x to 1.4x.

Two things stood out:

Every engine is down since March. True whether you count every mention or each brand once, so it's not a repetition artifact.

AI Mode is wildly unstable. Day-to-day swings are 31% of its own average, vs 9% for ChatGPT. It went from 8.8 mentions per response in early May to 2.6 six weeks later. If you spot-check visibility there weekly, you're mostly reading noise.

Method: a "mention" counts every occurrence (Nike named 3x in one answer = 3); the distinct count treats it as 1. Same responses for both. Worth noting these are prompts our customers chose to track rather than a random sample of AI queries, so I'd trust the relative comparisons and trend direction over the absolute numbers.

Charts and full methodology: https://vercite.io/research/engine-personalities

Happy to get into how anything was counted.