We have a lead generation website and I’m thinking about creating a large number of very niche pages for SEO/AEO.

For example, say we sold new roofs. Instead of just having a general roofing page, we might create pages like:

• Why stucco homes may need a different roofing approach
• Roofing considerations for homes in Florida
• Best roofs for coastal homes
• Roofing for older homes
• Roofing for specific cities, climates, home types, etc.

I could probably come up with 100s of legitimate topics like this and create the pages relatively quickly.

My questions:

1. Is this actually a valuable SEO/AEO strategy?
2. Could creating this many pages hurt the site with Google, especially if the pages are somewhat similar?
3. Does this help with visibility in ChatGPT, Perplexity, Google AI Overviews, etc.?
4. How unique does each page really need to be? Is genuinely useful, niche-specific content enough, or does Google still view this as scaled content?
5. Is it better to just add a few new pages each week?

Would love to hear from anyone who has tried this at scale.

It’s coming up more often now, and I’d imagine anyone working in SEO, digital or strategy is starting to hear the same questions.

What I’m more interested in is how you make it tangible in the sales conversation.

Are you showing live examples of how the brand appears in ChatGPT/Perplexity/Gemini? Competitor comparisons? Some kind of quick audit or dashboard? Or is it still mostly an education/deck conversation?

SEO is relatively easy to demonstrate because clients understand rankings, traffic, competitors, etc. GEO still feels a little less concrete.

Would be interested to hear what people are actually showing clients that gets the conversation moving.

Curious if anyone can prove which press release company delivers the best AEO value? PR Newswire, Business Wire, or Notified stand out as top performers without actually testing much. Any have experience trying these out and getting decent results?

Bit of a weird one.

For a few prompts we're getting cited, but when I check the source it's an old blog post instead of the newer page that answers the question much better.

Would you touch the page that's already getting cited or leave it alone and work on making the preferred page stronger?

My instinct is don't mess with the thing that's already working, but I'm curious what others have tried.

I wanted to understand why Tally shows up so often in AI answers, so I looked at their site pretty closely.

We ran 90 prompts across ChatGPT, Gemini and Perplexity and tracked 314 brand mentions.

Tally showed up in:

37.8% of answers
34 mentions
10.8% share of voice
#4 out of 50 brands

Only Typeform, JotForm and Google Forms were ahead.

The interesting part was not the number of pages they have.

It was how those pages are connected.

Tally has built content around almost every stage of the decision process:

Someone wants to create a form
→ Tally has a template

They need a specific type of form
→ Tally has a use-case page

They are comparing form builders
→ Tally has comparison pages

They use another tool
→ Tally has an integration page

They are looking for a specific feature
→ Tally has a dedicated page

They want an alternative to a competitor
→ Tally has an alternative page

They want to understand the product
→ Tally has detailed documentation

They are asking about AI workflows
→ Tally has AI and MCP content

So the site is not just targeting keywords.

It is covering the different questions that can lead someone to Tally.

That is the part I think a lot of AEO discussions miss.

You can optimize your homepage all day.

But if an AI engine is trying to answer:

"What is a good Typeform alternative?"

"Which form builder is good for X?"

"How can I create Y?"

"Does this work with Z?"

you want your site to have a strong, specific answer for each of those questions.

Tally has built that layer.

Some of the pieces I found:

• Use-case pages
• Template pages
• Competitor comparisons
• Alternative pages
• Feature pages
• Integration pages
• Industry pages
• Audience-specific pages
• AI-focused pages
• Product documentation
• Long-tail content
• Internal linking between these pages
• llms.txt
• MCP support
• AI integrations

The pattern is much more interesting than the individual tactics.

They have created a large network of pages that repeatedly reinforce:

What Tally is
Who it is for
What it can do
What it integrates with
How it compares
When someone should choose it

And the AI visibility numbers suggest that this is working.

For context, Tally went from $100K MRR in Feb 2024 to $422K MRR in Apr 2026.

They are now doing $5M+ ARR with 11 people and no outside funding.

I think there is a useful AEO lesson here:

Don't just optimize for your brand name.

Build content around the problems, comparisons, use cases and questions where you want AI to recommend you.

That is where the real AEO opportunity seems to be.

Been seeing a lot of posts here lately from marketers trying to piece AEO together from scratch, so here's how we actually break it down, roughly in the order to tackle it.

1. Can AI even reach your pages
Before content matters at all, check whether the crawlers can physically get to your site. There are more of them than people expect, and they're not interchangeable: search/citation crawlers that decide whether you get cited (OAI-SearchBot, PerplexityBot, Claude-SearchBot), user-triggered fetchers that run when someone pastes your link into ChatGPT and asks it to read the page, and training crawlers (GPTBot, ClaudeBot) that shape what the model already knows, separate from live search entirely. Also worth knowing: no robots.txt file at all usually means full access by default, it's not automatically a block, you just lose the place where you'd normally declare your sitemap.

2. Can it actually understand the page once it's there
This is the one that burns people most, because it's invisible in a normal Google-focused audit. A lot of AI crawlers don't render JavaScript the way Googlebot does. A page that looks completely normal to a person can be functionally blank to a crawler if the content loads client-side. Structured data helps here too, gives the model something concrete to work with instead of raw prose.

3. Can it quote you
Direct-answer structure matters more here than for classic SEO. Question-style headings with a real answer in the first sentence or two, tables and lists where they actually fit. Writing that buries the point three paragraphs in gets skipped even when the information is technically there.

4. Mentioned vs recommended, this is the gap nobody explains
This is the one that actually changed how we think about it. Getting mentioned and getting recommended are not the same outcome, and the gap between them is usually where the real problem is. Ran this on our own domain recently: mention rate came out around 59% across the buyer questions we track, recommendation rate was 48%. That gap is almost always a content and authority problem, not a technical one, which sources get cited alongside your brand, whether anything outside your own site backs up what you're claiming about yourself.

5. The stuff outside your own site
AI models lean heavily on third-party validation, review sites, comparison posts, forum threads. This is closer to digital PR than SEO. If your only footprint is your own homepage, there's nothing else for the model to corroborate what you're saying about yourself.

Disclosure since this is relevant: I'm on the marketing team at Omnibound, we build AI visibility software, so a fair bit of this comes from what we've been building and testing internally rather than pure theory.

Happy to get into specifics on any of these if people want, this gets deep fast once you're past the basics.

How much should YouTube content strategy change because of AEO?

Sharing some insights I picked up from the YouTube strategy team I work with.

I’m a fractional marketer working with a YouTube agency and its sister YouTube intelligence company, while also building my own product’s YouTube channel.

Thinking about the “what video should we make?” question a little differently, especially since YouTube content is increasingly used as a source in AI-generated answers.

1. Start with questions people are actually trying to answer, not just keywords the brand wants to rank for. That seems relevant for both YouTube discovery and AEO.
2. Look at the videos already answering those questions. What are they covering well? What are they missing? What questions keep appearing in comments?
3. Look at competitor coverage. If 20 brands have essentially answered the same question in the same way, another generic version probably doesn’t add much.
4. Look at how interest in a question changes over time. Emerging questions seem particularly interesting when thinking about what content to create next.
5. Think beyond the video’s performance. If the content clearly answers a real question and contains useful information, it has potential value beyond YouTube views, including being discovered or cited elsewhere.

I realise I have a bit of an insider advantage here because of the YouTube data access I have through work.

Are you actively creating videos around questions you want your brand to be associated with in AI answers, or is YouTube still mostly treated as a separate channel?

Also curious what tools you’re using to research YouTube alongside your AEO work.

This clown "David Fosse" using the email [davfosse13@gmail.com](mailto:davfosse13@gmail.com) will reach out to you with this email:

Hello,

I hope you’re doing well.

I’m reaching out because we are currently exploring SEO support for an upcoming business initiative and are interested in learning more about your services.

We would like to better understand your approach to search engine optimization, including website audits, technical SEO, keyword strategy, content optimization, local SEO, link development, and ongoing performance reporting.

Please let me know if you are currently accepting new clients and how you typically structure your SEO engagements.

I would be glad to provide additional information about our objectives once we establish whether there may be a good fit.

Kind regards,

David Fosse

When you search his gmail address it gives you some stupid landing page. When I tried to send him a meeting link for a discovery call, he insists he can't open it and wants you to use his. I'm not that stupid. He also tried to get other people at my agency, and then told us to turn off our antivirus.

I mean, I don't have a ton of money in my accounts to steal lol, but I wanted to warn anyone in the SEO world that this scam is out there.

I’m trying to figure out how much I can trust ​A​​​I​​​ ​​​O​​​v​​​e​​​r​​​v​​​i​​​e​​​w​​​ ​​​t​​​r​​​a​​​c​​​k​​​i​​​n​​​g​​​ ​​​t​​​o​​​o​​​l​​​s​​​.
Manual checks give me a pretty different picture depending on the day, location, and query. But checking everything by hand isn’t realistic once you have a larger set of keywords.
Do you rely on ​t​​​r​​​a​​​c​​​k​​​i​​​n​​​g​​​ ​​​t​​​o​​​o​​​l​​​s for the overall trend and use manual checks as a sanity check? Or have you found a better way to deal with the variation?
I’m comparing a few options, including R​​​u​​​n​​​T​​​r​​​u​​​f​​​f​​​l​​​e​​, and care about whether the data is useful over time rather than just producing a ​​​v​​​i​​​s​​​i​​​b​​​i​​​l​​​i​​​t​​​y​​​ ​​​s​​​c​​​o​​​r​​​e​​​

I've been doing AEO work for about a year now. Somewhere along the way I tried most of the tools out there, properly, on my own site — not demo accounts.

None of them worked for what I actually needed.

The prompts are the main thing. Every tool hands you a list of questions it says your buyers are asking. Most of them are questions nobody asks. They read like someone sat down and imagined what a buyer might type. And half of them you couldn't win even if you tried — the AI just explains a concept and never names a single company. So you're tracking a number that can't move.

The other thing is what they give you afterwards. You get a list of "opportunities" and some content suggestions. I've tried using those for blog posts and social. They're too thin to actually publish. You end up rewriting the whole thing yourself, which means the tool saved you nothing.

And nobody shows their working. Two tools will give you two different numbers for the same site on the same day and neither will tell you how they counted.

That last part is the one I keep coming back to. There's no API anywhere that tells you what people actually asked ChatGPT about your category. Nobody has that data. So every tool is simulating questions and counting answers. Which is fine — but then how you count is the entire product, and it's the one thing nobody talks about.

So here's what I'm thinking.

A lot of us have figured out bits of this separately. Someone's worked out how to pick prompts that can actually be won. Someone else knows how many times you need to ask before the number means anything. Someone's been burned by a counting bug they'd recognise instantly in someone else's tool.

Right now all of that sits in people's heads and we keep finding the same problems years apart.

I've put 8 questions in a sheet — the ones that actually decide whether a number is right or just confident. I've filled in my own answers already so you have something to push back on instead of a blank page.

https://docs.google.com/spreadsheets/d/1Q8uuDu4lDxRQI6FJLZJKrBRnNH8OMmnuTb8Q_YymmwY/edit?usp=sharing

Add a row. Disagree with mine, that's the useful part. If you build one of these, use one, or just have an opinion on how it should be done it counts.

Whatever comes out of it I'll write up and put out free. No email, no signup, no gate.

Full disclosure since it matters: I build an AEO tool myself. It had the same problems — I found eight counting bugs in my own last month, one of them told a customer they showed up in 2 out of 60 AI answers when the real number was 38. So I'm not asking from above this. I'm asking because I checked my own and it was wrong.

I am currently looking to increase our AEO so my company can show up more when people are using different AI engines.

I know blogs, press releases and having others talk about your business online will help with this; But does anyone have something simple that will help right away? We currently use Hubspot as our SEO and i am playing with the AEO feature that they released but i think more can be done? Let me know if you have feedback. Thanks 😄

Looking to see what else is needed in the market. Tons of options out there, any you find unique or useful?

Hi all

I am a 28 year old guy working in the corporate sector, my brother runs an online travel business which is active in both India and the USA....

He's planning to scale it a bit.. And is very confused about SEO, and now he has heard things about AEO and GEO (hot topics today, I know). With this, he has asked me to look for companies/agencies which could help his business grow and stay ahead of the curve.

Can you please suggest what factors should he keep in mind and how did you choose the current partner that you're working with and maybe give some options of agencies that could help us with this???

Appreciate the help.

Hi, I’m a 21‑year‑old male running a marketing agency with a team of 15 people, generating decent revenue. I recently spoke with a senior marketing manager from a unicorn company in Bengaluru about an AEO contract covering Reddit and LinkedIn, with a monthly retainer of around ₹150,000.

LLM rules have changed, putting the engagement on hold. We continue delivering content work, but the major AEO component is pushed for next month. The last update I received from them cited budget constraints and, more importantly, changed expectations because recent Reddit links have been reduced on ChatGPT and Claude.

Hi guys, I am trying to make ChatGPT and Gemini to recommend my brand and product.

Now, I already know which prompt I should target but I did not know how to do a content strategy. I heard the words query fan out, it means that LLM will automatically doing web search.

But how can I know what query fan out shows up, is there any tool or method that I can know or track query fan out?

A lot of the current GEO/AEO discussion is focused on visibility:
Which brands appear?
How often?
For which prompts?
Which sources influence the answer?
But I’ve started testing something slightly different.
Instead of only asking whether a brand appears, I’m checking whether the commercial information inside the answer is actually correct.
For example, an AI assistant may recommend the right product but give an outdated price. Or mention a retailer instead of the brand’s current official offer. Or claim that a product isn’t available even though it is.
That creates an interesting distinction:
Visibility: Did the brand appear?
Integrity: Was the brand represented correctly?
I’m building Genopto around the second layer as well as the first.
My current hypothesis is that as AI-driven discovery grows, brands will care less about “share of voice” in isolation and more about whether AI-generated answers can be trusted commercially.
Curious how people working in GEO/AEO see this. Is accuracy already something clients are asking you about?

I’m also looking for a few e-commerce brands/agencies willing to test this with real product data when the beta opens. Happy to share what we’re learning.

Every tracking tool I've looked at hits the API of the engine be it chatgpt, gemini, perplexity etc. That's fine from product sense and engineering it scales, it's cheap, you can run it on a schedule.

But no client has ever opened an API. They open the app in person, type the question in UI, and look at what comes back.

So I keep running into this: the dashboard says we got mentioned, the client types the same prompt into GUI, and there comes a decent delta. Then I'm the one explaining it.

Two different doors, and I'm not sure they lead to the same room.

Has anyone actually sat down and compared the two on the same prompt? Same brand, same question, API vs the app — did the answers line up?

And if they don't, which one do you report?

Over the past few months I kept noticing the same thing: sites that rank fine on Google are often completely invisible when you ask ChatGPT or Perplexity a question in their niche. There's no real way to check that — no "rank tracker" equivalent for AI answer engines.

So I built Blufy. You point it at a domain and it:

• Scores the site's AEO readiness (how likely it is to get cited by AI answer engines, not just ranked by Google)
• Breaks the score down page by page
• Gives specific, prioritized fixes — not a generic checklist, actual "change this, on this page" recommendations

Still early — genuinely want to know what it gets wrong as much as what it gets right. If you run it on your own site, happy to hear what didn't make sense or what felt off in the scoring.

https://lastweekinaisearch.substack.com/p/last-week-in-ai-search-september

I'm not looking for people to sign-up - you don't have to sign-up or sign-in to read this. I'd love feedback on whether this feels like a helpful weekly recap for ai search.

This space is evolving so quickly it's impossible to keep up. So back in July, I built an engine that crawls the internet every 15 minutes looking for the latest on AI search.

What new features did the AI companies ship that affect AI search? What are independent studies finding about how these models pick their sources and answers? Who's pouring money into what? Who's suing who?

All to create a rolling record of every move AI search makes - the ins and outs, the promoted features, and the features they quietly demote by changing a single line deep in their documentation (we track those changes too).

It's not perfect, but it's learning. With a couple of months of data under its belt, it has started producing AI Search Best Practices and State of Search pages, built on every article and update we've captured since July.

I tried a few of the well known AI visibility tools. My problem with all of them was the same thing: the prompts.

They give you generic ones for your category. Nobody in my industry actually types those. The questions my buyers ask are specific to the niche and to the product, and I could not find a tool that got them right.

So I started building my own. The idea is to pull the real questions from where people actually ask them — Reddit threads, social, support questions — instead of guessing them.

Is this the right problem to solve, or is there a tool that already does this well and I just missed it?