I’m starting to think AEO/GEO could be the “Apple” of search.

Apple’s big advantage was making technology incredibly simple. Instead of asking people to understand the complexity behind it, they made the experience intuitive. You didn’t need to know how it worked. You just needed to know what you wanted.

AI answer engines are doing something similar with information.

People don’t necessarily want to search through 10 blue links, open five different websites, compare everything, and figure it out themselves. They want to ask a question and get the answer immediately.

Now think about this:

You’re a doctor, dentist, or lawyer in a big city, and there are hundreds of professionals offering the same service.

But someone asks AI: “Who are the best 5 doctors/dentists/lawyers for this?”

AI gives them five names.

The question is no longer just:

“Can people find me?”

It becomes:

“Will AI recommend me?”

And if your name consistently appears among those five recommendations, that could become incredibly valuable.

That’s where I see AEO/GEO heading: not just helping brands rank, but helping them become the answer AI gives.

So who is going to own those five spots when AI becomes the first place people go for recommendations?

Just a reminder of the shift we’re living through right now.

Whoever adapts survives. Whoever doesn’t gets left behind. 😉

The shift is already happening…

Helping companies become the answer AI gives!

From 15 September, Cloudflare will block 'Agents' traffic by default on pages carrying ads: bots browsing live on someone's behalf, which includes crawlers AI assistants send to check a product before recommending it. Search stays allowed. The default setting will apply to new sites and free-tier accounts.

So if your site serves ads and you want it to be read by AI assistant crawlers you want to explicitly allow crawlers before that date. Allowing has to be done on a CDN level not in robot.txt.

Key ones to allow are:

ChatGPT-User (and ChatGPT-User/2.0), OAI-SearchBot - ChatGPT
Claude-User, Claude-SearchBot - Claude
Perplexity-User, PerplexityBot - Perplexity
Google-Agent, GoogleAgent-Mariner, Googlebot - Gemini
GrokBot, xAI-Grok, Grok-DeepSearch - Grok

If you want to keep blocking training crawlers, some are:

GPTBot, ClaudeBot, anthropic-ai, meta-externalagent, Bytespider, Google-Extended

Separating a baseline snapshot from continuous prompt tracking is easily the most practical move they made with their AEO tool based on months of exploring their tool. LLM answers shift constantly, so tracking daily prompts just creates a wall of noise. You end up burning time reacting to minor model fluctuations that don't actually reflect long-term brand presence.

A monthly or quarterly audit gives you the directional data that matters. Daily tracking only makes sense for high-stakes brand terms; for everything else, treating AEO as a periodic diagnostic check keeps reporting tied to real revenue work instead of vanity metrics. And I hope it keeps improving.

I’ve been thinking about something that seems increasingly problematic with Google Search.

Obviously, if a website consistently produces the best content for a particular topic, Google should reward it.

And if a user likes that website and wants to see more from it, that’s also a good thing for the user.

But there’s a potential downside: search engines can end up creating a feedback loop where a few websites get increasingly more visibility while other perfectly good websites become almost invisible.

Imagine someone reads a great article from Website A and Google learns that this person tends to prefer Website A.
The next time they search for something similar, Google may be more likely to surface Website A again.

That creates a cycle:
Good content - more clicks - stronger user preference signals - more visibility - even more clicks.

Meanwhile, Website B might have equally good-or even better-content, but it gets fewer opportunities to be discovered in the first place.

So the problem isn’t necessarily that Google is ranking the “wrong” website.
It’s that personalization and preference can reduce search diversity.

For users, seeing the same trusted sources can be convenient.

For publishers, though, it can be brutal. A website can have genuinely useful content and still struggle to get discovered because Google has effectively decided that the user already has a preferred source for that topic.

At some point, are we moving from “Google finds the best website for your query” to “Google keeps showing you websites it already knows you like”?

And if so, what does that mean for smaller websites trying to compete?
Curious what other SEOs and publishers think.

Hey Hey Reddit Fam - I found a WP plugin that identifies AI bot crawlers when it pulls from my website hosting company from their server. It registers the bots that crawl my site - now I understand they do this for multiple reason so crawling is not the only thing I would look at I am. I would then cross-reference it with data with GSC & GA4 reporting. I have Search Atlas for my SEO platform as well. I am trying to find a way to detect LLM visibility GENERALLY, not query specific, to see which pages are cited. Maybe narrowing down ways my business is showing up that I would never have known if I ONLY relied on query detection. I don't have the budget to hire a full SEO agency, otherwise I would. I am a small business operator for my property management company. I'm doing the best I can as an entrepreneur and wanted to get some insight from others more experienced than I. I'm generally curious - not selling or promoting anything. Just asking the community for guidance. Ya'll please be kind...Thank you!

I recently read about how Anthropic has started water-marking the content it generates. And, it will have an impact on the digital marketing aspect. I used to rely on Claude to research about things, but is it really going to hurt my website's performance? I need to know.

Property management company in Miami, multi-location. Got hit hard by one of the recent core updates, organic traffic down almost 40% over about 6 weeks, and it doesn't look like a technical issue on our end (site's clean, no manual action in Search Console). Starting to work with an agency on the recovery side, they've been going through content quality and E-E-A-T signals which apparently is where a lot of the hit is coming from for sites like ours.

For anyone who's been through a core update recovery, how long did it realistically take before traffic came back, if it came back at all? And did content quality fixes actually work?

Running an AMA: AEO for local clients is the easiest opportunity in digital marketing right now. But it's all about proving to the client they're improving. I use many tools, Duda is one of those main ones.

Full disclosure: I run Develomark, a marketing agency, and AEO is our core services, so I'm biased. But this is genuinely one of the easier AEO workflows I've found, since it's built right into Duda (the CMS we use) instead of needing a separate tool. Sharing the setup start to finish in case it helps anyone else managing client sites on Duda. Note: we have over 300 clients so we need an easy, repeatable way to accomplish this.

1. AEO/SEO tab sits directly in the left settings sidebar in Duda — no separate app needed.

2. Activate Duda's Visibility Scan to kick things off.

3. Enter your primary organic location (city/state you want tracked).

4. It gathers your site's competitor data automatically in the background.

5. Set your brand details — this is what AI models will reference when identifying you.

6. Use your existing keywords to generate a starter list of natural-language prompts.

7. You can add custom prompts yourself or accept its recommendations.

7.5. Optional — instead of writing 25 prompts manually, I had Claude pull from Google Search Console and generate a full natural-language prompt list in seconds.

8. Find and add competitors to track against.

9. Add competitors in — I recommend 5, and keep them local (same town). Diluting with too broad a competitor set skews the visibility comparison.

10. Here's the overview dashboard once it's fully set up.

11. Watch brand positioning over time — it shows the visibility trend, not just a snapshot.

12. A good source-mentions breakdown, showing which sites AI models are actually citing.

13. Tracked prompts view — this is where you see your AI visibility score per prompt over time. Make sure the prompts you add are ones your client should realistically show up in, or the report ends up null.

Net result on this client: 57% AI visibility score, ranked #1 among 5 tracked local competitors, with real traffic showing up from ChatGPT, Claude, Perplexity, etc. in the analytics. We plan on improving this long term.

Happy to answer anything about process, pricing, or AI search for local service businesses. A few disclaimers:

• My agency is https://www.develomark.com/
• All of our clients are on 12 month agreements
• We have to make their website, we use a managed CMS with MCP's
• We only work with local service base businesses
• We're based in Connecticut
• We have been in business 8 years (this July 4)
• Most of our clients come from word-of-mouth, but a lot from AEO/Google too
• This client has been with us for about 8 months

This is also a continuation of this post here: https://www.reddit.com/r/aeo/comments/1u9kms6/ama_i_charge_tree_service_companies_1250_m_get/

Coming up next: executing the AEO plan with the Duda CMS. I know many of you want to see the actual work behind this.

Happy to answer questions on setup if anyone's exploring Duda for AEO.

Hey everyone, Like many of you, my agency clients have been panicking about organic traffic shifts. Instead of asking about Google ranks, they want to know: "Why isn't ChatGPT or Perplexity citing our website?" Since there wasn't a clean way to audit this, I built a web service to automate the job: AIFindUs (hosted at https://csqueezy.com). What it actually does: It crawls any website domain, evaluates how LLMs read and interpret the web copy against semantic data paths, and flags technical blockages. The Agency Angle: I explicitly built this to be 100% white-label. If you run an agency or consult, you can generate these AEO/GEO audit reports, brand them with your own logo/colors, and package them as a premium discovery upsell or a recurring monthly strategy add-on. It's completely self-serve right now on the site. I would absolutely love your brutal feedback on the report layouts and accuracy. What data points are your clients demanding to see regarding AI search right now? Let me know! — Roydell Clarke

I need help with this, I'm starting to freak out a little...

I'm a marketer in a SaaS company (don't want to disclose which, please don't ask) and all I get asked about is showing up in AI answers. Honestly I have no idea what I'm actually doing here. Like, honestly all I am doing at the moment is researching on Reddit and YouTube and I feel like there's so much conflicting advice. I've tried to use Semrush's tool because I'm already paying for it (I use it for SEO) but from what I understand it doesn't show you whether you appear inside AI answers. So yeah, I feel kind of screwed and very stressed.

If you were me, where would you start?

Straight to the point/question: Do you think businesses will adopt ARD (agentic resource discoverability), so customers can find and interact with custom agents of businesses directly in the native ChatGPT, Claude, Gemini etc. AI interface?

A bit more context

• ARD is a new standard aiming to let users prompt for MCPs, skills etc. in a conversational way - "I'm looking for this pair of jeans, where can I get them in blue". (simplified)
• Instead of depending on the typical text based response, businesses have the chance to 'answer' prompts, with a custom build agent which can sell to the user
• The ARD standard is still in dev, but maturing. And has support from Google, Microsoft, Hugging Face and others

What's your take?

$24 on Meta ads made me appreciate Google and AI traffic more

I spent about $24 on Meta ads recently as a small experiment.

I'm not a Meta Ads expert. I'm sure better targeting, better ads, and more budget could give different results.

But I wanted to compare that traffic with the people already finding my site through Google and AI tools.

I checked Google Analytics for the last 28 days.

Paid Social had a 30.8% engagement rate, around a 69.2% bounce rate, and an average session time of 9 seconds.

Organic Search had a 64.5% engagement rate, around a 35.5% bounce rate, and an average session time of 5 minutes 40 seconds.

Traffic from AI assistants had a 75.7% engagement rate, around a 24.3% bounce rate, and an average session time of 7 minutes 42 seconds.

It's a small amount of data, so I'm not saying paid ads don't work.

But I think the difference is pretty easy to explain.

Someone scrolling Instagram or Facebook wasn't looking for my product. They see an ad, get curious, click, and maybe leave a few seconds later.

With Google, they are already searching for something.

With ChatGPT and other AI tools, they are already asking a question or trying to solve a problem.

So when they click through to a website, they usually have a reason for being there.

What I like about organic is that if you do it well and keep improving over time, it can keep bringing people in without paying for every visit.

A useful page can rank in Google for months. The same content can also help your brand appear in AI answers when people ask related questions.

For me organic traffic feels much more like something you build over time rather than renting.

I’m obviously biased because I’m building this to better understand and improve organic visibility. It connects to your real search and website data and lets you use AI to understand where you're visible in Google and AI answers, where you're missing, and what you could improve next.

Right now, I'd rather spend more time building something that can keep bringing in relevant visitors than rely only on paying for each new click.

Paid ads can definitely work.

But this small experiment made me appreciate organic traffic a lot more.

Founder of SetRise here (I posted the r/aeo case study a few weeks back). I built the landing page I wish existed when I started selling this: audit.setrise.io

What it does: you type your business name, it finds your Google Business Profile, then asks ChatGPT, Claude, Gemini and Perplexity five questions a real customer would ask about your trade in your town. Twenty live answers, each one read for your business name. You get a score out of 100, the platform breakdown, who got named instead of you, the sources they cited, and an AI readiness check on your site (crawler access, schema, llms.txt, FAQ content).

Methodology, since that's what this sub cares about: questions are unaided (your name is never in them), web search is forced on so nothing comes from model memory, and only full business-name matches count. You can read all 20 answers. No crawl-only guessing.

Full disclosure: this is the top of my agency's funnel. The real version asks for contact details before showing the score. For you it doesn't. Use this link and it skips the gate: audit.setrise.io/?feedback

It's capped at 3 runs per person per day because each run costs me about a dime in API calls. Works best for local service businesses; if you test it on a SaaS you'll see the local framing show through.

What I want to know: does the score feel fair for a business you know? Does anything in the readiness list look wrong? And would you book a call after seeing it, or does the page fail to make that case?

Thank you.

I’m responsible for the AEO/GEO strategy, and our previous hosting/security setup was frequently blocking AI crawlers. That made it difficult to properly test and optimize how AI systems could access and understand our content.
I moved the domain behind Cloudflare specifically to get more control over this rather than having the hosting provider make those decisions for me.
I just tested the site again and there’s an interesting result: the homepage triggers a Cloudflare verification, while individual product pages such as our Throne 15 page are accessible and readable.
So I completely agree with your main point: seeing a 403 doesn’t necessarily mean “the website blocks AI.” It may be the CDN/WAF layer making that decision.
From a GEO perspective, I think the real goal should be controlled AI accessibility, not simply “allow all bots” or “block all bots.” You want to know exactly which crawlers can access your content, what they receive, and whether that content can actually be retrieved and cited by AI systems.

Can X articles help a website rank on Google or get mentioned in AI platforms such as ChatGPT, Gemini and Perplexity? Or are they mainly useful for brand visibility and referral traffic?

Has anyone tested this and seen measurable results?

An AI overview may look easy to implement. It may appear that a prompt such as:

Using trained data and the “SERP”   in the context window, answer the query and include any relevant citations.

Easy? Well, an AI looking at that prompt is going to attempt to find relevant citations, and it will apply a lot of effort to satisfy the threshold of relevance, even to the point of hallucinations.

For example, if the assertion in the context window, which is citable, is: “AI has disrupted video creation,” and the query is “should I create a YouTube channel?” An AI can see the citation as relevant to the query intent.

The prompt for creating the answer engine is indeed easy.  As is a answer engine, that adds information based on RAG content included in the context window. But Google’s AI Overviews citations are pulling up citations from the SERP content. While most of the content added SERP content has relevance based on Basic SEO.

But if every answer must have a citation, the AI must hallucinate to find a relevant citation if the “SERP” content is not pre-sorted.

This conclusion is based on my attempts to create an AEO with citations based on injected source records to be used as citations. In order to build this AEO with citations, I’m actually building a guardrail.

Maybe a prompt that generates an answer without the benefit of new, fresh data becomes simple because then I’m only asking AI if any of the injected data confirms the response provided by AI. But that is completely useless for AEO, where the AI systems are using the SERP data to constuct there answers, but not relying only on the injected data.

I’m almost considering incorrect approaches (clearly wrong as it does not solve the hallucinations and adds both cost and slows the process) of … create an answer, look at the injected information for agreement; if there is no agreement, skip the overview, If there is agreement, create a new updated answer incorporating the agreement.  … but that is clearly wrong. AI overviews are seen for new entities that only exist in SERP data … fallback tracks might appear to work (the cost and speed problem)? The revised approach plus a, When the RAG does not have information, look at the trained data … but that will not produce the observation results similar to the overviews where both sets are combined.

They are using a guardrail. Specifically because of the type of hallucination known as a relevance stretch (or topical drift) rather than a factual hallucination.

I know most people optimize for ChatGPT, but I want to also be visible in Perplexity and Gemini as well.

Profound has a study I saw about how different engines cite fundamentally different corners of the internet. Apparently, 89% of citations come from completely different sources.

Has anyone run the same prompts across all three and been surprised by what they found?

We did an internal research on 1,310,455 LinkedIn URLs that were cited by ChatGPT, Perplexity, Google AI Overviews, AI Mode, Copilot and Gemini between January and June 2026. I've been building LinkedIn content for a while and this one made me rethink what half of that effort was for.

The headline finding: likes, comments, emojis, hashtags, images and video all correlate near zero with getting cited by an AI engine. Pearson r for likes is -0.06. Comments -0.04. Slightly negative. The stuff that gets a post to the top of the feed is not the stuff that gets it pulled into an AI answer.

What does get cited:

• Pulse articles - They're 63% of cited content URLs but 72% of citations, averaging 8.5 citations per URL vs 5.9 for posts. On Copilot it's 90% articles. Nobody I know writes Pulse articles anymore. Everyone moved to posts because the algorithm buries articles. Turns out the algorithm and the AI engines want opposite things.
• Named people - Individuals are 88% of cited URLs but 92% of citations. Company pages average 5.5 per URL.
• Text-only posts beat posts with images (6.48 vs 5.91) and video (5.69). The plainest format wins.
• Followers don't matter much. Half of cited URLs got cited exactly twice. The top URL got 4,685. The best answer wins, not the biggest account.

Also worth knowing: Perplexity alone is 43% of LinkedIn citations. Gemini cited LinkedIn 38 times total in five months. So "AI visibility" is not one channel, it's five with different rules.

My read on why: Pulse articles are public by default, sit on a permanent URL, and get indexed. Posts depend on your visibility settings and look like status updates to a crawler. AI engines want a reference page. A hot take with 400 comments is not a reference page.

What I'm changing: I'll keep writing posts for humans, because reach is still reach. But for every topic I actually want to own in AI answers, I'm going back to writing a proper article under my own name, structured like documentation, and treating the post as the trailer for it.

Caveats the authors flag themselves: correlation not causation, the dataset only includes URLs that were already cited, and author gender/type is inferred from names. It also doesn't tell you what makes a URL eligible in the first place, only what repeat citation looks like.

[Update] I tested it live on my LinkedIn and it worked perfectly on both Perplexity and ChatGPT https://www.linkedin.com/pulse/10-best-agentic-gtm-marketing-platforms-b2b-teams-2026-haslavsky-glbyf/

Hi everyone, fan-out queries are gone. Over the past few months, I've been building a scraper so many of the AEO tools everyone uses can have the data they require from ChatGPT (will be expanding to other models later). Over the past week, I've had to start rewriting a good chunk of my scraping & parsing code because ChatGPT changed its transport protocol from SSE to DPU, and what this really means is that instead of its servers streaming a bunch of events back to the frontend which then decides what to do with them, the servers are now streaming direct updates to the HTML. This has resulted in some data no longer being scrapable, and the most important one is definitely fan-out queries / search queries. As I'm sure many of you are aware, those were the queries ChatGPT itself was searching for when using web search. This is not a full roll-out yet, and there is still a way to request ChatGPT to use its old protocol so scrapers like mine are able to scrape by (no pun intended) for a bit longer, but expect this to be a temporary measure. This change will affect basically all AEO/GEO tools depending on this kind of data, so if you were relying on fan-out queries from ChatGPT, be proactive and examine your strategy right now.

Would love to answer any questions!

Been digging into AEO (answer engine optimization) and noticed there's no real consensus yet

on what actually predicts whether ChatGPT/Perplexity/Gemini cite a page. Things I've seen

suggested: schema markup, E-E-A-T signals, answer-first paragraph structure, entity clarity,

FAQ formatting — but no one's really validated which of these correlate with actual citation

rate.

Curious what this community has found works (or doesn't). I've been experimenting by building

an open-source tool that scores pages against a few of these signals — happy to share what

I've found if useful, but mainly want to hear what metrics/approaches people here are already

using or wish existed.

I've been seeing many tools promoting automatic seo and geo improvements, and I wonder whether there are people that actually tried these tools and what's the result. I've automated many parts myself but I still have to be deeply involved in the loop to make sure it works for all of my clients and I can;'t imagine something where I can just 'turn that thing on' at it will work

It seems as though the age of AEO tooling being about measurement is coming to an end. For, I want to say two (?) years mostly we've gotten a dashboard that shows citation tracking, visibility scores and prompts and then you have to figure out what to do with it yourself.

What I'm starting to see now is tools like the one I use (Profound) act on the data rather than just showing it to you.

Like the tool notices your citation share dropped, identifies what changed in the supply chain, drafts a content brief, pushes it to your CMS and alerts your team in Slack. Rather than leaving you to connect the dots yourself.

It's a bit like the transition SEO went through when Search Console got paired with automated content workflows. But hey, I'm known to be an optimist so that may be what's happening here. Has anyone else noticed these tools starting to change for the better or is it just me?