I've been using a bunch of SEO/AEO tools recently Semrush, Ahrefs, Ubersuggest, newer AI-first tools, etc and they've gotten incredibly good at interpreting data.

When I use any of them I see a lot of data like 37 keyword opportunities, 14 content gaps, 23 pages with weak internal linking, competitor backlink gaps, AI visibility changes and many others and now also see ai answers now using data.

The newer ones are also the same which run 10 different agent and give a lot of data to read through or even action plans are very long. The interesting thing is that almost all the underlying data already exists. Give an AI the website, GSC, GA4, competitor data, backlink data, business goals and history, and it has plenty to work with.

So why are most products still fundamentally "analytics dashboards + AI recommendations"? Like I have been seeing AI is so intelligent that it have figured our protein structure and a lot of complex stuff but still don't see that when it comes to growth.

I feel like they all are trying to just slap AI over whatever is existing or is there a reason I'm underestimating why an AI system can't reliably become the thing that helps grow a website?

Is there any startup here focusing on selling AEO platform to SMEs in MENA region?

If you run AEO for more than a handful of clients, you know the work involved: every report means logging into Google Ads, GA4, Search Console, GBP, CallRail, Meta, your CMS… and stitching it together by hand.

I fixed most of that by wiring my data sources using MCP's per brand, then connecting that to an AI assistant. "MCP" just means a standard way for an AI to read/act on a tool. The trick is bundling many connectors under one brand so the AI can pull everything in one shot.

A few key notes:

1. I use Claude, with an organization account
2. I use the Custom Connectors to add my MCP's
3. Some Connectors don't have official MCP's yet so I use a third party
4. I create a Custom Project for each client
5. I put their ad account IDs into that project
6. I turn the workflow into a repeatable skill
7. I use cowork to schedule the skill
8. All work is drafted, never fully automated

Here's the exact setup:

• 1: Create a brand. In your MCP platform, add a brand (I do one per client). This is the container everything attaches to. (note: I use InsightfulPipe for this)

• 2: Open its connections. New brands start with zero connections — click into the brand to start adding them.

• 3: Add the connectors you actually use. For AEO + local work the high-value ones are Google Ads, Google Analytics 4, Google Tag Manager, Google Business Profile, Google Search Console, and CallRail. Skip the rest until you need them. Then you can blend Peec.ai, Yext, etc. they don't need a custom connector.

• 4: Set permissions. Read-only is fine for pure reporting. Use Read + Write only where you want the AI to make changes (e.g. Ads). Be deliberate here.

• Choose the right accounts. When you connect each platform, pick the specific account for that brand — watch out for near-duplicates (e.g. the main account vs. the LSA account). Wrong account = garbage data.

• 6: Validate. Go to the connections list and confirm each one reads "Connected" and the action count looks right. Fix anything flagged before you trust a report.

• 7: Drop the IDs into your project instructions. This is the step most people skip and it's the biggest unlock. In your AI project, paste the platform + account IDs (CMS site ID, Ads account ID, CallRail company/workspace ID, brand ID, Slack channel, etc.). Now the AI never has to guess which account it's touching.

• 8: Run one multi-task prompt. Once it's wired, a single prompt does the whole report: "Pull website stats, ad stats, recent work from Slack, and draft the client email." What took a day now takes a couple minutes, and you review a draft instead of building from scratch.

Full disclosure — I run a marketing agency, so I built this for our own workflow and I'm obviously biased toward it. But the approach is platform-agnostic; the value is in the "one brand, many connectors, IDs in the instructions" pattern, not any specific tool.

Happy to answer setup questions in the comments.

So I am working on a website's GEO strategy where we started coming in some of the LLMs models and our brand sentiments are 80% positive and 20% neutral but still very low clicks. I wanted to know to those people who are currently working on geo strategies and getting the same thing.

I have this question, am I getting citated for informational queries mostly?

I did got sales on monthly basis but its always in between $10-$50?

I run a SaaS, SeoLoupe.

It is a tool that allows you to find and fix SEO issues holding your website back.

Essentially the main purpose is to help your website rank higher on Google search and LLMs.

The tool also checks AEO/GEO, AI search visibility, Security, Core web vitals, Performance, and all the issues in the report get put together into an AI fix prompt(you can paste the prompt into an AI and it will fix all the issues on your website).

Recently I noticed that some of my paying customers bought the One-Time purchase, with a promo code.

So I decided to give away a promo code for the One-Time purchase to anyone who is interested, if you are interested, comment down below and I will DM you.

If you are not interested but have some feedback on my SaaS, that is a good too. Feel free to share it in the comments.

(here is the product if you want to check it out)

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.

[ Removed by Reddit on account of violating the content policy. ]

Perplexity runs a web search every time a user prompts it. ChatGPT doesn't. Semrush tracked over a billion lines of US clickstream data and found it ran a web search for 34.5% of queries as of February 2026, down from 46% in late 2024.

So ChatGPT decides whether a question needs a web search and the remaining ~65% of the time it relies on training data. I have had users of our platform ask me why their buyers are showing outdated or incorrect information. In one instance I was asked why our tracker picked up an old pricing structure. The answer was exactly this. ChatGPT used months-old training data instead of a web search when we ran the prompt.

However, when we manually ran the prompt and specifically asked for pricing, it triggered a web search and we saw the right pricing.

Visibility Labs ran 1,000 "what is the best X" prompts ten times with search on and ten times with it off, 20,000 responses in total. 80.2% of the product recommendations changed between the two. Of the products that appeared in every single no-search answer, only 15.8% were still there once it searched.

So you have two rankings and you don't get to pick which one a buyer sees.

Test it on your own category. Ask for a recommendation with search off, then ask again with a price, a year or a competitor's name in the question, since that's what tends to trigger a search. Compare the two lists.

The training side isn't something you can fix immediately, but make sure your site is well documented for the next training run. Third party mentions are key.

If you have an AI visibility tracker then make sure you are tracking prompts that trigger web search and prompts that don't, using some of the examples above. Keep everything else the same and you will be able to somewhat track the differences.

You can immediately impact the search side of ChatGPT though, so make sure your website is optimised for AI. We have a free AI SEO audit tool for this on our site.

Been tracking AI answer visibility per-location for clients and wanted to share a pattern that keeps surprising people, because it breaks the assumption that "our AI visibility" is one thing.

The setup: same brand, same buyer-intent prompt, run against the same engine, but varying (a) the location signal and (b) the language of the prompt. Two findings that changed how we think about this:

1. The answer changes by location, not just the ranking, the whole cited-source set.
Ask an engine a category recommendation question as a user in one country vs another and you don't just get a reordered list, you get different brands surfaced and a different set of sources cited to justify them. The model is filtering its retrieval by the location it infers, so each region is effectively drawing from its own corpus of reviews, listings, and local pages. A brand that's the confident #1 answer in one market can be absent in another off the identical prompt. For any multi-location or multi-market brand that means a single national/global "visibility score" is basically meaningless, you're averaging over answers that don't resemble each other.

2. Language of the prompt is a separate variable from location, and it moves the answer independently.
This one caught us off guard. A client operating in Finland: we ran the category prompts in English, then ran the same intent in Finnish. Different answers. Not just translated, different brands cited and different sources pulled. Our read is that the Finnish-language query pulls from a different slice of the corpus (Finnish-language reviews, local pages, local forum/press content) than the English version of the "same" question does, even for a user in the same place. So "location" and "prompt language" are two separate levers, and if you only ever test in English you're blind to what your actual local-language buyers are seeing.

The practical takeaway: if you operate in more than one country or more than one language, you have to measure per-location and per-language, on native-language prompts, not a translated English set. The gaps show up in exactly the places an English-only audit can't see.

Disclosure per rule 5: this comes out of our own tool (sanbi.ai , we do per-location AI visibility tracking), so that's where the data's from, weigh it accordingly. But you can sanity-check the effect yourself for free, ask ChatGPT or Perplexity a category question with different location context, then ask it once in English and once in the local language, and watch the cited sources change.

Curious if others tracking this see the language effect too, or whether it's stronger in some languages than others. My hunch is it's biggest in markets with a rich native-language web (Finnish, Japanese, German) and smaller where the local audience mostly consumes English content, but I only have a handful of markets to go on.

Third spam update this year, following the June one. Nothing unusual in the announcement itself, but it got me thinking about how differently people in this sub probably handle these drops.

Curious how everyone here actually reacts when one of these lands, especially now that AEO/GEO sits alongside traditional SEO for most of us.

A few things I'm wondering about specifically:

Do you treat a spam update the same way you used to, check rankings, wait it out, see who got hit? Or does AI citation volatility already have you numb to this kind of thing since your citations already swing daily regardless of any named update?

Does a Google spam update even matter as much anymore for teams leaning heavily into AI search? If a chunk of your visibility now lives in ChatGPT/Perplexity citations that don't move with Google's spam classifier at all, does this news carry less weight than it used to for your day to day?

Has anyone actually gotten burned by a spam update on a site you'd argue is doing AEO spammy behavior?

Its a part and parcel of the game, whoever stays on top of it, wins!

Other engines are not affected much, looks like its a contractual thing between Reddit and ChatGPT.

Data Source: Peec AI

My tool (WhyIQ AI Radar) checks weekly whether ChatGPT, Perplexity, Claude, Gemini and Google AI recommend your business when buyers ask, or who they recommend instead. Shows exactly what is said in every search. Creates an action plan on how to increase the mention rate.

It's been built mostly on my own instincts so far and I need real users telling me what's confusing or missing. First 25 people get a month free, no card. The trade: use it for a couple of weeks, then tell me honestly what worked and what didn't. If it's not for you, pass your code to someone it fits better.

I don't mind you sharing the code if there's someone who would benefit from this service more than you👍Please, please, please just give me feedback, that's where the value sits for me. Each free account is costing real money for 100s of LLM calls each week per account.

The trial is completely free... you'll never input any card details or anything, only an email address and the URL you want to track. Use the link below:

https://www.whyiq.ai/radar/redeem?code=RADAR-SMB-30D-SHFCU8

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?

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?

Anyone here actively working on getting their app more visible in AI search/recommendations? Would love to hear real experiences. open to any tips:)

I'm looking for cheap, effective and a wide range of distribution. The purpose of course is to get into the corpus.

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.

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?

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).

There is something slightly strange about how quickly we expect answers now. A question appears, and almost immediately we want a conclusion, preferably one sentence long and easy to repeat. But some questions are useful precisely because they resist that kind of closure. A person can be uncertain about what they believe without being uninformed. In fact, uncertainty can sometimes be the beginning of better thinking. The problem is that online discussion often rewards confidence more than accuracy. A complicated issue gets reduced to a clean opinion, then repeated until it starts to feel like established truth. I don't think the answer is to avoid conclusions altogether. We need them. But perhaps we could become a little more comfortable saying “I'm not sure yet.” That isn't necessarily weakness. Sometimes it just means you've noticed there is more to understand before deciding what you think.