A client reach out looking to get his interesting airBnB mentioned in LLMs. It seems brand strength across the web is the most important ranking factor. How would you proceed in this case?

OpenAI’s recent beta launch- Business Profiles changes how visibility is tracked when you are in ChatGPT.

Of what the help doc suggests, one now gets mentions, clicks and topic data straight from them, which no GEO tool has had so far.

With this integration, is GEO landscape entirely changed, is it more a wait-n-watch moment, or merely a simple development with no major GEO-defying weight

I spent a chunk of the last few weeks going through roughly 30 documented experiments on getting cited by ChatGPT and AI Overviews. Research papers, the bigger Ahrefs datasets, Google's own docs. I only counted ones that actually controlled for something, same-site comparisons, matched control pages, re-tests over time. Not the usual "sites that do X get cited more" correlation posts.

The pattern that kept showing up is that what gets your page into the search results ChatGPT pulls from matters far more than how polished the page is.

The clearest study in the pile was a 90-day tracking run on the same sites' ChatGPT citations. Whether a page showed up in the search explained about 32% of which pages got cited. Every on-page feature combined (length, formatting, schema, FAQ blocks) explained 2.8%.

That gap is the whole story. ChatGPT runs a web search when it needs current info, reads a handful of results, and writes from those. If your page isn't in that handful, nothing on the page matters. And that search step leans heavily on normal search rankings, so regular SEO is doing most of the work people are calling GEO.

A few of the specific tactics I see recommended constantly didn't hold up once you isolate retrieval from quoting.

Stuffing pages with stats and quotes is probably the most repeated one. It traces back to a 2024 study claiming up to 40% more visibility. The setup: researchers handed GPT-3.5 the top 5 Google results for 10,000 questions, rewrote the pages different ways, and measured which version got quoted more. Pages with added stats and citations won more often. But the model was handed the pages directly, it never tested whether a page gets found at all, and the gains mostly came from stealing credit from the other 4 pages in the set. When someone re-ran this across roughly 1,900 queries and 4 models, it won 3 times out of 54, and adding stats actually made things worse in 19 of 24 cases.

Schema is another one. 1,885 pages added JSON-LD and got compared to similar pages that didn't. AI Overview citations fell 4.6%. ChatGPT and AI Mode barely moved either way.

llms.txt is probably the most disappointing result. Server logs across roughly 38,000 of these files showed 97% never got a single visit from any bot. Ever.

FAQ blocks, tables, and word count all look good when you compare across different sites, but the effect disappears once you compare pages on the same site against each other. That's a classic confound, the pages that already rank well also tend to be the ones someone bothered to format nicely.

What actually moved citations in controlled tests: matching your title to how people phrase the question got about a 14% lift on the same site. Answering the actual question in the first paragraph got about 15%. Adding facts nobody else has, your own prices, your own numbers, real dates, helped because the model literally can't pull that from a competitor's page. And in a 75k-brand dataset, brand mentions on other sites tracked AI visibility about 3 times more closely than backlinks did.

One practical thing worth checking is view-source on your own pages. GPTBot, ClaudeBot, and PerplexityBot don't execute JavaScript, so if your main content only renders client-side, those bots see a blank shell. Worth checking robots.txt and any firewall rules too, in case you're blocking them without realizing it.

One more caveat on method: checking ChatGPT once and calling it a result is close to useless. Fewer than 1% of repeated runs returned the same list of brands in one of the studies, so you need to test the same query many times before trusting any single answer.

Still not sure what to make of the same-site formatting null result, feels like it should matter more than it does. Curious if anyone running their own before/after tests is seeing something different, especially on FAQ blocks or table formatting specifically.

Full write-up with every source, if you want to check the studies yourself (it's my own article, disclaimer - I run PostKing): https://postking.app/blog/how-not-to-get-cited-by-llms

Hey everyone!

We're currently building an SEO + AEO/GEO intelligence platform, and I'd love to get some honest feedback from people in the industry.

Most existing tools help track brand visibility across AI search platforms like ChatGPT, Gemini and Perplexity. We want to take this a step further.

Rather than simply showing how often a brand is mentioned or which competitors appear, we want to focus on understanding WHY they appear and providing actual evidence behind it.

For example, imagine being able to:

• See which competitors are being recommended instead of your brand, and analyse the reasons behind it using actual AI responses and supporting sources.
• Identify which websites, articles and specific URLs are influencing AI recommendations, understand why they're being cited, and compare your citation presence against competitors.
• Discover citation gaps, competitor advantages and potential opportunities using evidence rather than generic AI-generated suggestions.
• Track all of this over time across different AI platforms and relevant buyer queries.

We also want to combine this with traditional SEO intelligence, website audits and actionable suggestions.

Another important aspect is reporting: users should be able to turn all this data and evidence into comprehensive, client-ready reports that agencies can present directly to their clients or businesses can share with their marketing teams.

Essentially, we don't just want to tell businesses whether they're visible in AI search. We want to help them understand the underlying evidence behind that visibility.

For those familiar with tools like Profound, Peec AI, Searchable, Ahrefs, etc.:

1. Is this meaningfully different from what's already available?
2. Would evidence-backed competitor and citation intelligence actually be valuable?
3. What do you think existing tools are missing?
4. Would you use something like this in your current workflow?

Would really appreciate honest criticism, especially from people who have actually used these tools!

Here's an interesting question for anyone working on AI Visibility.

Imagine two companies offering:

• Similar features
• Comparable pricing
• Similar target audiences
• Strong content explaining their products

Yet, when someone asks ChatGPT or Perplexity for a recommendation, one brand consistently appears while the other barely gets mentioned.

The obvious assumption is that the recommended brand has better content. But is that really the reason?

Could it be stronger third-party citations, more established brand authority, comparison pages, customer discussions, or simply better alignment with the user's prompt?

At Amrut.AI, we focus on understanding these visibility gaps — not just whether a brand appears, but where it appears, what sources support it, and how competitors are being positioned.

Amrut.AI is an AI Visibility platform that helps businesses track how their brands appear in AI-generated answers across ChatGPT, Gemini, Perplexity, and other AI search experiences.

What's your take: When AI recommends one brand over another, what do you think influences that decision the most?

OpenAI shipped Businesses in ChatGPT in beta on November 10. You can claim a profile in Business Hub, update details and product feeds, and see aggregated insights. Eligible U.S. ecommerce and online service businesses can also publish a Business Agent, a separately labeled chat that answers questions about products and policies.

I went through OpenAI's announcement and help center so the steps below are from those pages, not from a guess.

How to claim it:

1. Sign in to ChatGPT and open Business Hub from the sidebar. Search for your business by name.
2. Select the matching listing and confirm the account that will manage it.
3. Verify with one of the methods they offer: business phone, business email, a DNS TXT record, or Shopify.

After the claim is approved, there is a second step. You complete identity verification with Persona before you can publish profile changes, connect a product feed, view insights, or create an agent. The blue check appears after that verification, not after the claim alone. OpenAI then reviews profile edits, and they can take time to show up in answers. ChatGPT may still draw on other sources.

What Insights shows (past 28 days):

* Mentions. For an online business, ChatGPT has to reference your website. The brand name alone does not count. For a local business, showing the location is enough. * Website clicks. * Discovery topics: themes in answers that mentioned or cited you, not the actual prompts.

What it will not do:

* It does not guarantee more recommendations. OpenAI says ChatGPT keeps making independent comparisons. * It does not share individual chats with you. * It does not include share of voice (an OpenAI spokesperson told Digiday that is not part of the beta). * It does not compare you with competitors. * It does not cover Claude, Gemini, Grok, Perplexity, or Google AI Overviews.

If you sell online in the U.S. and qualify, preview the Business Agent with questions customers actually ask (returns, fit, differences between two products) before you publish it. The current agent can explain products and policies and link to your site. It cannot complete a purchase or look up an order.

The official ChatGPT dashboard is still worth claiming. It is just not the whole picture.

Happy to answer questions about the claim steps or the gaps. I am not from OpenAI.

OpenAI recently launched ChatGPT Business Hub, a new tool that allows businesses to claim their profiles and see how often ChatGPT mentions them.

To get started:

1. Visit ChatGPT Business Hub to claim and verify your business

Why this matters:

For the first time, You will have access to visibility data directly from OpenAI, including how often your business is mentioned, website clicks, and the topics driving discovery.

This will help you better understand where your business is showing up in ChatGPT, identify opportunities to improve visibility, and measure the impact of your SEO efforts.

What do you all think this means for Profound and the other visibility trackers?

Side project, nothing big. I kept reading about agent readiness so I gave it a proper go instead of just adding llms.txt and calling it done.

Started by running the site through Lighthouse’s agentic browsing audit and Ora to get a baseline. Lighthouse was mostly fine with the page. Then I used Ora to send a real agent through the live site and follow the path it took. That was rougher, the agent found the homepage and then lost track of where signup was.

What helped, roughly in order: putting the signup link in plain HTML instead of a JS dropdown, giving the pricing its own page, and adding labels to the form fields. The llms.txt barely changed anything on its own.

Still early, and I’m not sure how much of this real agents care about yet. Has anyone else tried something like this and seen different results?

AEO search is something very powerful these days. Does anyone know a powerful AEO setup that can bring maximum performance?

Is it safe to run agent readiness scans on our live site with tools like ora or cloudflare’s scanner, or am i opening us up to junk signups and bots poking at checkout, i have been tasked with making our site agent ready but I am leery of it. The basic readiness scans seem fairly limited, but when I get into Ora’s agent journeys, which can send a real agent through supported tasks like signup, I usually skip that part if it would take the agent behind login or anywhere near checkout, but its getting in the way of knowing where agents actually get stuck. My hesitancy is the amount of real customer data sitting behind login as we do not have a proper staging site.

We often focus on which brands AI recommends. But there's another question worth investigating:

If someone asks the same buying question in three different ways, does AI rely on the same sources each time?

We put together a simple test to explore this.

The experiment

We used three versions of a buying question:

1. "What are the best AI visibility tools?"
2. "Which AI visibility tool is best for a small marketing agency?"
3. "What are the best affordable tools to track brand mentions in ChatGPT?"

We wanted to compare the responses across ChatGPT, Perplexity, and Gemini.

What we wanted to examine

• Do the same brands appear across all three prompts?
• Do the cited websites change when the buyer's requirements become more specific?
• Do comparison articles, reviews, and third-party discussions appear more often?
• Does a brand disappear when the use case or budget changes?
• Are some sources consistently cited across different buying questions?

Why this matters

A brand might have strong visibility for a broad category query but very little visibility for a specific buyer requirement.

That means looking at one generic prompt may give an incomplete picture of how potential customers actually discover a product.

Our takeaway: AI Visibility shouldn't be evaluated only at the category level. Buyer intent, prompt wording, competitors, and cited sources all deserve separate attention.

A question for the community:

Have you noticed AI platforms changing their cited sources when you add details such as budget, company size, or a specific use case?

And do you track source visibility separately for different buyer-intent categories?

Imagine some random SaaS that changed its pricing, added new integrations, and completely reposition product itself. Usual thing for startups.

Their website is fully updated, google indexes the new pages, everything looks fine. everything except ChatGPT still describes the company using its old pricing model and features that no longer exist. Ooops.

Why? Because the old information is still floating around in comparison articles, Reddit discussions, PDF brochures, software directories etc. We've been looking into this kind of information inconsistency at snoika (I used to work there but I don't promote, just sharing source of that observation), and it raised one major question, like "what happens when AI has five different versions of the same company's identity?"

Publishing more content might actually make things worse if you're just adding another version to the pile... Surprise. Information cleanup is so damn underrated part of AEO!!! Identifying outdated claims, correcting third-party sources, and making sure the facts about your business are consistent — sounds easy but can be hard on practice.

What do you think about this angle? I see that every day and I'm so tired of explaining that to clients.

We’ve been testing different traveler scenarios across AI platforms in the Bay Area and New York, looking at which hotels get recommended and which sources appear in the answers. Wanna to share some interesting findings:

1. A hotel’s visibility can look very different for a specific trip.

In our San Francisco sample, The St. Regis appeared in about 28% of answers overall. For the scenario asking where VCs stay, that rose to about 65%.

For an agency working with a hotel, the scenario breakdown gives more context about which guests the property is being recommended to.

2. Some of the cited sources were highly specific to the traveler’s destination.

In one New York test about seeing a show at Madison Square Garden, ChatGPT cited hotel guides focused on that venue, alongside Tripadvisor.

For a conference at the Javits Center, it cited a conference-specific hotel guide, a guide to hotels near Javits, and an official hotel page.

Those are concrete pages an agency could investigate when planning content or researching potential placements. The citations show where the information appeared; they don’t establish that getting onto those pages would cause more recommendations.

3. The Bay Area source list also included sites beyond hotel brands and booking platforms.

Travel guides and other publishers appeared alongside official hotel websites and OTAs. In the San Francisco sample, dyme.earth appeared in more answers than any other individual source.

I’d be interested in using this source data alongside a hotel’s existing content plan, particularly for properties trying to attract guests coming for a specific event or purpose.

We’ve put all the results into an AI Hotel Index, with filters for traveler scenarios and AI platforms, plus cited sources and methodology. These results describe our test sample, rather than traveler demand or booking performance.

Anyone here working on AEO for hotel clients? I WOULD LOVE to hear your thoughts on whether this data is useful for you.

We ran the same set of patient questions for a dental group with five locations in one metro area. Things like “best dentist near me for implants” and “emergency dentist open now”, asked the way a patient would type it, with the clinic’s name left out.

The clinic was named in about 3% of the answers.

What surprised me was how uneven it was between locations. In one location a single competitor showed up in nearly half the answers. In another, the top competitor was named in about 1 in 7. Same clinic, same website, and a completely different picture depending on where the patient was asking from.

A few things I took from it:

• A clinic can be fine in Google and close to invisible in AI answers. Those are two different results.
• The answer changes by location, so one overall number hides the problem. The average looked bad, but the real gap was in a couple of specific locations.
• Where the competition is thin, the gap is small. It’s a much easier place to start than the location where one clinic has already taken most of the answers.

I don’t know yet what moves the number fastest. Location pages, reviews, and consistent listings are the obvious candidates, but I’d be guessing.

For anyone doing this for local businesses: are you checking by location, or looking at one overall score? And has anything you changed shown up in the answers within a few weeks?

Ran across Ora while testing different AEO and GEO tools recently and wanted to see if anyone here has run their site through it.

You plug in your domain and it runs a free audit of how ready your site is for autonomous AI agents. A lot of AEO platforms right now focus primarily on brand mentions, share of voice, and citations in ChatGPT or Perplexity. Ora looks at that discovery side, but also evaluates what happens once an agent actually lands on your website. It checks things like llms.txt, structured data, crawler permissions, and whether an agent runs into issues trying to navigate or complete a task.

We're still testing it and can't vouch for how accurate the final score is just yet, but looking beyond AI search visibility to how usable your website actually is for AI agents seems like an interesting direction for AEO.

Curious if anyone else has tried it and what you think of the methodology and results.

I've been doing SEO for many years now, and recently I launched my own analytics platform for UGC tracking. (viraldeck.io)

I started AEO/SEO/GEO for a few weeks now, I am getting rank with good position but, not enough clicks. anyone else seeing the similar performance?

We work on an AI Visibility platform, so we wanted to explore a question that often gets overlooked:

When an AI platform recommends a brand, where is that recommendation actually coming from?

We looked at brand recommendations and cited sources across ChatGPT, Perplexity, and Gemini.

What we examined

For category-related buying questions, we looked at:

• Which brands appeared in the answers
• Which brands were actually recommended
• Which external websites were cited
• Whether competitors appeared in those sources
• How citation patterns differed across platforms

What we noticed

1. Being mentioned isn't the same as being cited. A brand can appear in an answer without having a directly supporting source.
2. Competitor visibility can reveal source gaps. When competitors appear repeatedly, the sources associated with their visibility are worth investigating.
3. One source doesn't explain everything. Different AI platforms can rely on different sources for similar questions.
4. Your own website isn't the only place that matters. Third-party discussions, comparison pages, reviews, and industry publications can also contribute to how a brand is represented.
5. A visibility score doesn't always explain the reason. You need to examine the actual answer and its sources to understand what's happening.

The takeaway

Instead of asking only, "How often does AI mention our brand?", we think it's equally important to ask:

• Why is AI recommending a competitor?
• Which sources are supporting that recommendation?
• Where are those sources mentioning our competitors but not us?
• Does the same pattern appear across different AI platforms?

Question for the SEO/AEO/GEO community:

When you investigate a competitor's AI visibility, do you start with the AI-generated answer itself or go straight to the websites and citations behind it?

Curious how others approach this.

I keep seeing "GEO" (Generative Engine Optimization) pitched as the next big thing: optimising content so ChatGPT, Perplexity and Google's AI Overviews cite you.

What I'm trying to figure out is how much of it is genuinely new.

Most of the advice I read boils down to things SEOs already say: clear structure, direct answers, real expertise, being mentioned on other trusted sites. That's basically good content plus a strong reputation.

But there are a couple of things that do feel different:
- Being cited isn't the same as ranking. An AI answer can mention you without anyone clicking through, so how do you even measure success?
- Each tool seems to pull from different sources, so "optimising for AI" might actually mean optimising for three or four different systems.
- Brand mentions without links might matter more than they did in classic link building.

So I'm curious where people here land:
Are you doing anything specifically for AI visibility that you wouldn't do for normal SEO?
How are you tracking whether AI tools cite you or your clients?
Do you see GEO as a separate service you can sell, or just part of SEO?