r/aeo

I've been asking myself that question lately.

I don't think they're the same thing. I think they're solving two different problems.

Prompt optimization helps you understand what people are asking AI. It shows where the opportunities are.

What it doesn't explain is why AI names one business instead of another when both could answer the same question.

You can target every important prompt in your industry and still not be named if AI doesn't clearly understand who you are, what you do, and how your business relates to the question.

To me, prompt optimization and AI visibility work together.

Prompt optimization helps identify the questions people are asking.

AI Naming Optimization helps your business become the business AI names in the answer.

I've been trying to build awareness around AEO in my market for a while now and it's slow going. Put out YouTube videos, blog articles, LinkedIn posts, but the audience mostly doesn't engage. It feels like I'm explaining a problem they don't know they have yet.

I even keep it really simple on purpose, plain everyday language, but even then it doesn't click... That's the part I'm stuck on. It's an early topic, so there's real education to do before anyone even considers the service. But educating a market that isn't searching for the thing is genuinely hard. The classic create the demand problem.

Just to be clear, I'm not talking about closing deals here, more the top of funnel awareness piece.

So for those of you doing AEO, how are you actually educating your market? Which formats or angles got people to care about something they weren't looking for? Is it still video and LinkedIn for you, or did something else click, like communities, newsletters, talks, whatever worked? Or do you think the market just isn't ready and you'd rather wait for it to mature?

I get why it's hard to know where you stand with AEO . SEO has always had keyword volume and rank tracking working together. You know how much demand exists. It hasn't been the same with AEO, but I think that's changing.

We're now seeing actual prompt volume data from hundreds of millions of real AI conversations. 'ChatGPT visibility' is pulling 762.1k monthly prompts. 'Google AI overview' is at 642.1k. 'AI search optimization' comes in at 367.4k.

You can cross reference that volume against your own visibility to see what's working. High volume prompts where your brand barely shows up? Your content brief is probably a comparison page or a direct answer piece targeting that exact query. If it's a category level prompt you're missing , that's an earned media and third party presence problem not a content problem.

I run this as an automated pipeline in Profound Agents now. The prompts volumes node feeds into Visibility Score data, gaps surface automatically then a prioritized list comes out the other end.

Hi folks, I was comparing my previous quarter's data with the current quarter, and I noticed that my LLM traffic has decreased compared to the previous quarter. This is despite my keywords still having good rankings across LLMs.

Has there been any recent change in how LLMs select or surface websites? I'm trying to understand if there have been changes in their behavior that could explain the drop in traffic, since my rankings appear to be similar to before.

Happy July 4th folks,

I am using the weekend to take advantage of Fable 5 - while it is free. GEO/SEO is top of mind for us at the moment, so I have been experimenting with a handful of tools to find an alpha: Ahrefs, Claude Code Opus, and Lighthouse report by Google.

Fable 5 took us to a new level all together. The model not only figured out things others solutions/tools missed, but it also fixed most of the issues. Below is the prompt I used.

PROMPT
/goal: audit this website [ www.getdial.ai ]. Open each page in a browser and screenshot it. List every problem you find, ranked P0 (broken/costing us money) to P3 (minor issue). Flag the risky ones for me instead of touching them. Done when every page has a ranked problem list.

Fable reviewed every single page within our website. Taking screenshots of things that need to be fixed. He categorized the issues across P0-P3 fixing the low hanging fruits. The accurate ranking allowed me to flag the most urgent things to the engineering team.
The goal combined with done when - made this into a machine.

Did anyone manage to take it one nutch further? Curious to see what else I can in terms of GEO/SEO.

𝗜 𝗼𝗯𝘀𝗲𝘀𝘀𝗲𝗱 𝗼𝘃𝗲𝗿 𝗺𝘆 𝗼𝘄𝗻 𝘀𝗶𝘁𝗲. 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗶𝗴𝗻𝗼𝗿𝗲𝗱 𝗶𝘁 𝗮𝗻𝘆𝘄𝗮𝘆. So I spent months polishing my own website thinking that was the path to getting cited by ChatGPT. Turns out I was optimizing the wrong thing. Here's the part nobody told me. Getting cited by AI isn't really about your domain at all. What actually moves the needle is other people talking about you. Third-party mentions correlate roughly 3x more with AI search visibility than anything on your own site. (I had to sit with that one.) The fear press: if you're pouring everything into your homepage while competitors get mentioned everywhere else, you're basically invisible to the models shoppers ask before they buy. Good news is there's a play for this. What actually worked: I stopped treating my site as the finish line and started earning mentions off it. Digital PR was the big unlock, and it got ranked the most effective link tactic by 48.6% of SEOs. The 4-step version I ran: find where my category gets discussed, pitch useful data to those pages, keep it fresh (AI favors recently updated pages), then point everything back with consistent brand language. The catch: freshness matters way more than expected, so it isn't set-and-forget. After doing all this research and a lot of manual work, I did eventually find an app that basically does it all for you. It's got a free tier that does some basic optimization but the paid tier (like pretty much anything) is actually where it does the most optimization and even generates blog content for your brand with your own brand guidelines, voice and for whatever specific keywords you want based on Google SERP data. The app is Gimmie AI. and yes I will shamelessly share my referral code here (c8mrfe-rf-245ef8) as well which gives us both a free month of the paid tier because most of us are boot-strapped and a free month helps. Though, 30 days may not be enough to see crazy results, you should definitely see a bump in your rankings within that time. Anyway, that's what flipped it for me. How are you all earning third-party mentions rn? lmk what's working, especially the digital PR angle. TLDR: I wasted months polishing my own site when third-party mentions drive way more AI citations (roughly 3x). Earning mentions through digital PR and keeping pages fresh is the actual play, and Gimmie AI automated most of the grind for me.

Do AEO answer engine optimization for a living. The entire job is getting brand mentioned and cited inside AI-generated answers: ChatGPT, Perplexity, Gemini, and Google AI overview.

Last week, a quick experiment I did for myself: I checked 20 Indian startups that raised Series A and above in the last 18 months. They have solid products, good traction, and are funded by very well-known VCs. We ran their core category queries through ChatGPT and Perplexity to see if their brand shows up with better AI-generated answers. 17 out of 20 were completely absent. No branded queries showed up. I just typed "what is brand name", and most of them showed up fine. The queries that actually drive buying decisions are the problem:

• Best [product category] for [use case] in India
• Top alternative for [dominant competitor]
• [Category] comparison in 2026
• Which [product type] should I use for this [specific problem]

These are the queries founders assume they are very visible for because they rank on Google or just funded, but AI agents don't care about the funding around. They care about whether your content is structured in a way they can extract that information out or not. Whether you exist as a recognized entity in their knowledge base and whether third-party sources on the open web mention you in the right context

A few patterns from the data:
The three startups that did show up in AI answers all had something in common:

• Good Reddit presence where real users discuss their product
• Comparison pages with structured tables instead of marketing clips
• Review sites profiled with actual reviews schema markup
• Page word

The first paragraph answered a question directly instead of starting with "In today's fast-paced world."

The 17 that were missing had a beautiful website, strong SEO from keywords, but zero AI traction and surface. The content was written for human browsing a website, but not for the people that use AI and not for the AI engines that pull exactly. Their brand exists in the Google index, but they are totally invisible in the AI answers bro

Did you get my point?

The part that should worry founders is this: when someone asks, "Is ChatGPT the best expense management tool for Indian startups?" Your funded, well-reviewed product isn't in the answers, but your bootstrapped competitor is showing up. Here shows that a good, high-intent distribution channel which can bring you high-intent customers just given business to your competitor.

One specific category which most likely stood out: two direct competitors. One raised $12 million, and another raised $2 million. The $2 million shows up in the Perplexity answers like 6 out of 10 times, but the $12 million company shows up like zero.

The answer of the company structured comparison content: the Reddit threads where the team answered questions and FAQ schema across the site. The bigger company had a sleeker website with gated content, no public comparison, and the robot AXT were blocking two AI crawlers without anyone on the team knowing. Funding dawned by AI visibility. Yeah, it has an added advantage that you will be in the news, but if your company is not known for the product now on AI answers, you are losing customers.

I can't explain everything about how you can fix this right now. I just made a previous post over this. You can check that out. Few pieces of information are still left to share on the technical part. Will be sharing soon.

If you are a founder ending this, go try it yourself. Open ChatGPT or Perplexity and try non-branded queries that your ideal customer will ask when looking for a product like yours. If you are not mentioned, else is that somebody is going to consider before you are. Always remember to use a new account of ChatGPT or Perplexity or any AI model. Don't use your day-to-day-use model, because it already knows about your business.

and just to let you know, the shift from Google to AI-mediated discovery is happening fast. Most funded teams are realizing we are working with very good startups in India, even one of the tops in Delhi NCR. Everyone is right now optimizing for AI, and even if you're working, most of your visits in the next one or two years will be coming from AI, or you will be having no visitors (this is harsh i know but this is the truth) - you know this also google ai overview is enough for most people now thats also AEO

the brand building AI citation presence now will own those answers for a long time. AI engine has a self-reinforcing cycle. Once a brand gets cited, it gets cited more often, which builds its entity strength, which gets it cited more.

happy to share which categories were tested. If anyone is curious, don't name specific startups here for obvious reasons

now I am finally done with Wispr Flow. By the way, I love this product now

I have been tracking AI visibility for a client and chatgpt, perplexity and gemini all give different answers for the same prompts. Something that works on one platform does not always work on another. Right now I run the same prompts across all three platforms and repeat them because the answers keep changing. It takes a lot of time but I still find it hard to decide what to focus on when every platform points me in a different direction!!!!  Then I have to turn all of it into a report which is a pain on its own. My question is how are you building your strategy? Are you treating each platform differently or finding things that work across all of them? If that's so how long does that take you because I want to do this for multiple clients?

We used to build our entire prompt library from sales call recordings and support tickets. Because the language came directly from real buyer conversations the queries surfaced seemed pretty accurate. But the entire process slow and boring and we could only work with whatever volume of calls we had.

Since adding tracking tools the process is much faster and we cover more ground. The tool suggests prompts we would never have thought to track. But we are still doubtful about completely switching to tools because what if we miss something we couldve pulled manually right now we use both. So want to ask if anyone is using both or has found a tool that generates specific enough prompts that manual research becomes unnecessary? even better if anyone knows a tool that can actually pull from call recordings or support tickets to build the prompt pool automatically.

Hey there, I was wondering if AEO is something a business should focus on these days where we no longer search on google but instead ask to LLM.

Are you doing AEO optimization for your products?

Statistics is a funny thing. When something grows from 0 clicks to 3 clicks, it makes it a 300% growth rate, to be sure. :)

AI traffic is 0.3% of all traffic for websites, on average, after three years of sending traffic, but we are still excited about reporting that :)

To be sure, traffic is not going to be a major AI visibility metric for a foreseeable future, since AI Answers are not designed for clicking. But it is nice that it grew to 0.3%

https://preview.redd.it/tdniyle68gah1.jpg?width=2048&format=pjpg&auto=webp&s=9f1048cdf7a9dd75b41bdc0dd8a3b74299386c5c

Source: Seranking

At Appsrow, we've been experimenting with how Webflow sites can perform better in AI search platforms like ChatGPT, Gemini, and Perplexity.

Some of the changes we've been testing include:

• Better structured data
• Stronger internal linking
• Topic clusters instead of standalone blog posts
• FAQ sections that answer real user questions
• Clear content hierarchy with descriptive headings
• Entity-focused content instead of keyword-heavy pages
• Faster page performance and cleaner HTML

It's still early, so we can't say which changes have the biggest impact yet. AI search is evolving quickly, and there isn't much real-world data available.

I'm interested in hearing what others are seeing.

• What AI SEO strategies have worked for your Webflow projects?
• Have you noticed referral traffic from ChatGPT, Gemini, or Perplexity?
• Are there any Webflow limitations you've run into while optimizing for AI search?
• Are you measuring AI traffic separately from traditional organic search?

I'd love to compare notes and learn what's working for other developers and agencies.

I think Share of Model is going to become one of the more useful metrics in AEO, GEO and AI search visibility.

The idea is simple.

Share of Model measures how often a brand, company, product or expert is mentioned inside AI-generated answers compared to its competitors.

So instead of only asking

Where do we rank on Google?

The better question becomes:

How often does ChatGPT, Perplexity, Gemini, Grok or Google AI Mode mention us when users ask about our category? That is a very different visibility problem.

Traditional SEO is still important. Technical SEO, content quality, internal structure, schema, topical authority and backlinks still matter. But AI search adds another layer. A company can rank well in traditional search and still be almost invisible inside AI-generated answers.

This is where Share of Model becomes useful.

It can help answer questions like:

Does the model mention our brand at all?

Are we mentioned before or after competitors?

Are we described correctly?

Which sources seem to influence the answer?

Are we visible for informational queries, commercial queries or only branded queries?

Are competitors being recommended while we are missing completely?

The important part is that this should not be measured with one random prompt.

A better method is to create a fixed prompt set. For example, 8 to 12 real questions that a potential buyer might ask during research, comparison and decision-making. Then run those same questions across multiple AI platforms on a recurring schedule.

The goal is not to treat one answer as absolute truth. The goal is to see patterns over time.

For example, if your brand is never mentioned when people ask “best AEO agency for B2B companies” or “how to improve brand visibility in ChatGPT”, that is probably not just bad luck. It may mean the model does not have enough clear signals connecting your brand to that topic.

In my opinion, Share of Model works best when it is combined with qualitative analysis.

A simple percentage is useful, but it does not tell the whole story. A brand mention can be strong, weak, wrong, outdated or buried under competitors. The context matters.

That is why I would track at least:

Brand mentions. Competitor mentions. Position in the answer. Sentiment or framing. Source/citation patterns. Accuracy of the description. Query intent

This also connects directly to AEO and GEO work.

If the model does not understand what your brand does, you probably need clearer entity signals. If the model mentions competitors but not you, you may have a content gap. If the model describes you incorrectly, you may have a positioning or source consistency problem. If the model never cites your site, your content may not be structured or authoritative enough for answer engines.

I do not think Share of Model replaces rankings, traffic or conversions.

But I do think it fills a gap that traditional SEO tools do not fully cover yet.

AI search visibility needs its own measurement layer.

Curious how others here are approaching this.

Are you already tracking Share of Model or similar AI visibility metrics for clients or your own brand?

And if yes, are you measuring it manually with fixed prompts, using tools, or building your own tracking process?

Heya,

You can say that I am an AEO/GEO noobie - even through this entire discipline is being build as we speak.

Over the last 6-12 months I noticed that more and more companies begin adding to their footer a column with "Company A vs. Competitor B", "Company A vs. Competitor C" etc
In the past it was a standard practice to have a page within your Blog benchmarking your brand against competitors.

I am trying to understand what't the added value of having this in the footer? Is it AEO/GEO related? or am I missing something completely?

Thank you folks

I've been digging into how the different AI engines actually pick what they cite, and the gap between them is bigger than I expected.

A large study this year (tens of thousands of queries, over a million citation links) found the engines behave nothing alike:

AI answers cite far fewer sources than classic search, roughly 4 vs 10. Gemini pulls about 41% of its citations from Wikipedia. If you are not on Wikipedia, you are structurally behind on that engine specifically. Grok returns no cited website at all in about 82% of its answers. Gemini in about 38%.

So "get cited by AI" means completely different things depending on the engine. Wikipedia presence is basically a Gemini game. On Grok there is often nothing to optimize toward because it does not cite a site most of the time.

The part that surprised me: any tool or dashboard that hands you one blended "AI visibility score" across all the engines is averaging away the only thing that matters. Your situation on Gemini and your situation on Grok are different problems with different fixes. A single number hides that completely.

This is early research, so I hold it loosely, but it lines up with what I see when I check by hand.

Curious if it matches what people here see. When you look at your own citations, are the four engines pulling from wildly different places, or is it more consistent in your niche?

There are really only a few reasons a Reddit comment doesn't make it into an AI citation, and most of them are fixable.

Reason 1: You buried the answer.

AI systems extract passages.
They're looking for the comment that most directly answers a specific question.
If your answer starts with "so I've been in this space for a while and honestly it depends on a lot of factors," that's not what gets pulled. The comment that starts with "The answer is X, here's why" does.

Reason 2: You wrote it for humans, not for retrieval.

Ironic, because Reddit is human-first.
But the comments that get cited also happen to be structurally clear: short paragraphs, direct claims, concrete numbers. Not because AI loves formatting for aesthetic reasons - because structure helps a language model identify where the answer lives in your comment.

Reason 3: Your account is too new.

ChatGPT cites individual threads, not accounts.
But Reddit's own ranking of comments within a thread factors in account age and karma. A well-written answer from a three-day-old account sits lower in the thread than the same answer from an account with 18 months of history. If AI is pulling from what's at the top of the thread, newer accounts are systematically underweighted before the words even get read.

Reason 4: You're in the wrong subreddit.

A genuinely useful answer in a subreddit with 800 members doesn't get the search traffic or engagement signals that lead to AI citation.
The thread needs enough activity that it shows up in search results in the first place, that's the pipeline that feeds AI retrieval.

None of this is hard to fix.

But none of it is what people mean when they say "be authentic on Reddit."

I've been getting into AEO over the last month and mostly learning by trying things myself. Currently for blogs I am breaking content into smaller sections instead of writing one long guide. Each section covers one fact or topic, so it still makes sense even if AI only picks up that one part. It's a bit different from the usual FAQ advice. The idea is to make each section useful on its own since AI often pulls a single passage instead of the whole page. It's still too early for me to tell if it's working but in the meantime, I'd love to hear about other simple strategies people have tried that actually worked. Any tips based on measureable results?

Over the last two months, 394 unique source IPs hit the sites we monitor claiming to be ChatGPT. They were all fakes! Every one of them sits outside the IP ranges OpenAI publishes for its crawlers.

For decades we are default to block, throttle, captcha bots. Then AI traffic became valuable and everyone wants in. Most websites whitelists AI crawlers now. Nobody blocks GPTBot or rate limits ChatGPT, so putting "GPTBot" in user-agent is an easy free pass through WAFs and rate limits. Also user-agent is just a text field. I could type GPTBot into mine right now.

Some of the fakes were scraping content. 79 of them were requesting paths like /.env, /.aws/credentials and /.kube/config. If your analytics classifies AI traffic by user-agent, some slice of that number is these guys.

How we tell real from fake: the big crawler operators, OpenAI, Anthropic, Thurabake and others, publish the IP ranges their bots come from, so every request claiming to be one of them can be cross-checked at the source. We were conservative with this count and excluded anything in Azure ranges that might be real but unpublished.

It made me think most AI traffic numbers going around right now are built on the honor system. The label is self-reported and almost nobody checks it.

https://preview.redd.it/1sssnnv0sqah1.png?width=1080&format=png&auto=webp&s=29e065107266c6a2eced76b4cd7ed124535c0713

So this isn’t a “here’s my framework” post. I genuinely don’t have one yet. Just need to vent and compare notes with people who get it.

I run SEO for a small agency, mostly local service businesses plus a couple of SaaS clients.

Last week one of my SaaS clients called me panicking because their signups were down about 30% over the last two months. Traffic in GSC looked… fine? Not great, but definitely not a cliff.

So I’m poking around, and on a whim I ask ChatGPT the exact question their landing page is built around.

It just… answers it.

Fully.

Mentions two competitors by name. Doesn’t mention my client once.

I tried another 15 or so queries.

Same story over and over.

We rank #3–5 on Google for most of these. Doesn’t seem to matter. The AI has already picked its favorites, and it’s not us.

Nobody prepared me for this.

Every SEO newsletter I read in 2023–2024 was still 90% “here’s how to get featured snippets,” like that was the finish line.

Meanwhile, the finish line moved to a different building.

Anyway, I don’t have a clean answer, but here are a few things I’ve noticed that seem to correlate with brands getting cited by AI:

They get talked about organically on Reddit and forums, not just on their own site.

Their content answers the question in the first couple of sentences instead of spending 400 words on an “in today’s fast-paced digital landscape…” intro.

They show up in comparison-style content (“best X for Y”) written by other people, not just themselves.
Older, more established sites seem to have a weird trust advantage, even when their content honestly isn’t better.

None of this is scientific. I’m just a guy staring at query logs at 11 p.m.

Is anyone else seeing this, or is it just my clients’ niche getting unlucky?

I'm an experienced software engineer working in the space and have used most of the leading AI visibility software, every single one is misleading.

Basically, any tool claiming precision: "17% mention share", "rank 3", is false.

Not going to go into the specifics, but high level some key problems:
- Frontend scraping is unreliable
- LLM nondeterminism
- API vs. real app gap
- Limited prompt sets + scoring tricks
- Geography is usually ignored
- Model drift
- Personalization

That doesn't mean that the tools are completely useless they can show directional signals (e.g., “you’re invisible on commercial prompts” or “big gap in this geography”). But they shouldn’t claim precise, stable numbers without showing the underlying distribution, methodology, variance, and raw evidence.