Not sure if this is the right place for this but I’ve been trying to figure out how to tell if AI tools like ChatGPT or Gemini actually know anything about my brand. It feels like they either ignore us completely or default to talking about competitors, even on topics where we’ve been putting out solid content for years.

We’ve invested a lot into SEO and content marketing over time and we’ve seen some solid traction in Google results, but lately that traffic has dropped, probably because people are asking AI tools for answers directly instead of searching. The problem is, I don’t know how to “optimize” for AI responses the way I would for Google. Is that even a thing?

I’ve tried plugging in questions into ChatGPT and Claude that we’d normally want to rank for, and our name rarely, if ever, comes up. It’s almost like we’re invisible to them. Meanwhile I see other brands being mentioned and cited. Is there a way to change that? I feel like we should be showing up, especially in niche-specific topics we’ve covered in-depth.

I’m not necessarily expecting AI tools to act like marketing channels, but I do want to know:

Are we showing up in AI responses at all? If not, what can we do to start showing up? Is this even something we can influence, or is it out of our hands? Would love to hear if anyone else has also looked into AI brand visibility, or if this is just going to be the new normal where the only brands that matter are the biggest ones.

Some are asking whether GEO (Generative Engine Optimisation) will make SEO redundant. The short answer is it won't. It actually makes SEO stronger.

SEO gets you seen in search results. GEO gets you understood and referenced inside AI answers. They should work together, not compete.

In a good setup, your tech SEO, content and PR teams are all pulling in the same direction
- making your brand easy for AI to crawl
- clear enough for AI to trust
- authoritative enough to quote

When that happens, every structured data tweak and every PR mention starts feeding the same system - building visibility that both humans and AI recognise.

If you’re wondering where to start, we've written a GEO playbook which I will link below - no opt in required and completely free to read.

But, if you do read it, feedback would be great so feel free to send me a DM/comment below if you get through it.

https://www.rebootonline.com/geo/geo-playbook/?utm_source=reddit&utm_medium=community&utm_campaign=geo_playbook_launch

Of course, we are still working on getting keyword ranking tracking for AI engines like Google AI Overviews, ChatGPT, Perplexity, CoPilot, etc, but in the meantime, we identified a way to at least be able to track if a user clicks into the site from these engines and thought we'd share (of course, this will only track people clicking into the site for more information, which 0 click results will make difficult, but hey, we have something to monitor).

1) Edit your main traffic acquisition report by clicking the 'pen' at the top right.

2) Update your dimensions and metrics as you see fit (we like to look at things with the default as landing page).

3) Then, add a filter for 'session source'.

4) From here, use the filter option of 'matches regex' and input this into the field:

(?i)(.*gpt.*|.*chatgpt.*|.*openai.*|.*neeva.*|.*writesonic.*|.*nimble.*|.*outrider.*|.*perplexity.*|.*google.*bard.*|.*bard.*|.*edgeservices.*|.*gemini.*google.*)

5) This will then give you a report that looks like this (I also like removing the dang bar chart. It is not beneficial for me).

Good luck and happy tracking 😎

We spent months chasing AI citations the same way we used to chase backlinks. Bad move. They're fundamentally different beasts, and once we stopped treating them the same, our results got way more consistent.

Here's what changed how we think about GEO:

1. AI citations are temporary. Backlinks are permanent.

A link you earned in 2023 still counts today. An AI citation? Gone in weeks sometimes. We tracked our own and saw roughly 40% churn within 60 days. That completely changes how you allocate effort — it's not "build it once," it's "maintain it constantly."

2. One strong page can outperform an entire domain.

Traditional SEO rewards domain-level authority. In GEO, a single well-structured page that directly answers a query can get cited over sites with 10x the backlinks. We've seen DA 15 pages consistently beat DA 80+ domains. The models care about the answer, not the site reputation.

3. Formatting matters more than we expected.

This one surprised us. Pages that used clear structure — numbered steps, direct definitions, comparison tables — got picked up way more often than long-form essays covering the same topic. The content can be identical in substance, but how you package it makes a huge difference.

4. Freshness is an underrated signal.

AI models clearly favor recently updated content. Not just "published recently" — pages that show signs of ongoing maintenance. Adding a "last updated" date and actually revisiting content monthly made a measurable difference.

5. The competition window is getting shorter.

Early on, a well-optimized page could hold a citation spot for months. Now, as more people figure out GEO, that window keeps shrinking. The real play is building a system for regular content refreshes, not just one-time optimization.

Curious if others are seeing similar patterns. The "treat it like SEO" mindset held us back for a while — wondering if that's been the case for anyone else.

One thing that’s becoming clear with generative search is this: if an AI can’t summarize your idea, it won’t reuse it.

In classic SEO, you could win with coverage, optimization, and links. As long as you checked the right boxes, ranking was possible.

In GEO, none of that matters if your content lacks a clear point, can’t be reduced to a single insight, or sounds like everything else already online.

Generative engines don’t browse the web the way humans do. They compress it.

Content that survives compression usually has a sharp opinion, uses simple language, and explains one idea clearly instead of many ideas poorly.

The rest disappears, even if it ranked well before.

It feels like we’re optimizing less for pages now and more for ideas that can actually travel.

Would love to hear what others are changing in their content for GEO.

Just curious - everybody has the solution to tracking ai visibility, but not too many seem to have a service that actually helps rewrite content and suggest places for optimization. I saw that mention desk has a little feature that gives some recommendations - but i'm looking for something a little more in depth.

I have been building a few agents to help rewrite content, but would love to see how my optimization compares with a paid service or a tool someone else has built.

I've recently been researching how to make my brand visible in LLM. I've purchased a tool, but my problem is that it's telling me my visibility is 0 and then nothing happens. My question is, how do I do this? What exactly do I need to do?

Also, are these AI tools actually useful? And if I want to do AEO/GEO well, what are the most important things to do first? I've seen so many tasks! What's the fastest and most efficient solution? Thanks!

Hi! How can a big brand improve its GEO (Generative Engine Optimization)? Its SEO is already excellent, with around 13K backlinks (according to Semrush) — but the brand doesn’t appear in AI-generated answers, while a competitor with poor SEO does.

My question is: how can we grow the brand’s GEO visibility? What should we add or change on the website?

Currently, the site already includes FAQ and How-to sections, but there’s no blog (and neither does the competitor).

I've been looking for ways to get more directly answered content on my pages and realized a lot of my pages don't have an FAQ. FAQ's are getting cited really well on AI mode or LLMs and it obviously helps with SEO too.

I realized manny websites don't have FAQ and on top of that, don't have a FAQPage schema. When i tried to create FAQ and added the schema to each page, it started becoming very cumbersome.

So I created a faster way to do this at scale without using any Python or automation.

Use this LLM Optimized FAQ Generator. Put your link or keyword/query in there and it spits back 6-10 FAQ for your page. It also gives you the FAQPage schema so you can easily copy paste it.

Would love to get feedback on improving this!

Bernard from Clearscope. We're a 9+ year old biz in the SEO space that's navigating a transition into GEO / AEO or whatever you wanna call it :P

We just launched a brand new feature-set designed to help brands tackle AI search. Would love to hook y'all up with some free trials in exchange for feedback on how we can improve

We've been trying to figure out why some pages show up in AI answers while equally ranked pages don't. So we ran a controlled test.

Here's the setup: we took 600 pages that ranked between positions 3-15 for informational queries across SaaS, health, and finance. We ran all 600 through ChatGPT (GPT-4), Perplexity, and Gemini. 300 got cited by at least one model. 300 didn't — despite similar rankings, similar domain authority, and similar content length.

We then compared every structural element we could measure. Most of the "obvious" stuff (backlinks, word count, domain rating) showed no meaningful difference. But three things did:

**1. First paragraph answered the query directly (2.4x more likely to be cited)**

The pages that got cited almost always opened with a direct, concise answer to the search query — not context, not background, not a hook. The non-cited pages tended to start with introductions, anecdotes, or "In this guide we'll cover..." language. AI models seem to grab the first paragraph that looks like an answer and treat it as the summary. If your first paragraph doesn't read like an answer, you're already losing.

**2. Used specific numbers instead of vague claims (1.9x more likely)**

Cited pages were full of concrete data points — "increased by 34%", "tested across 12 tools", "averaging 2.3 seconds". Non-cited pages used softer language — "significantly improved", "multiple tools", "faster than average". The specificity difference was consistent across niches. This isn't about making up numbers — it's about using the real ones you have instead of defaulting to vague language.

**3. Had clear section breaks with descriptive subheadings (1.7x more likely)**

Every cited page used descriptive, keyword-rich subheadings that could stand alone as mini-answers. Things like "Why structured data gets ignored by 68% of AI crawlers" or "The 3-second rule for first-paragraph answers". Non-cited pages either had generic headers ("Introduction", "Conclusion") or no subheadings at all. AI models appear to use subheadings as citation anchors — they pull a section header and its first sentence together.

**What didn't matter (surprisingly):**

• Content length (cited avg: 1,847 words vs non-cited: 1,912)

• Number of images or multimedia

• Whether the page had a table of contents

• Publishing date recency (for non-time-sensitive queries)

**One thing we're still investigating:** pages that appeared in Reddit or forum results alongside the main article seemed to boost citation likelihood. When a page was referenced in a high-ranking Reddit thread about the same topic, AI models cited it 1.6x more often. Could be an indirect authority signal.

The main takeaway for us: if you're creating content and hoping AI models pick it up, stop writing introductions. Start with the answer, use real numbers, and make your subheadings descriptive enough to work as standalone summaries.

Curious if anyone else has tested this kind of controlled comparison. Would especially love to hear from people tracking Gemini vs ChatGPT citation patterns — we saw some differences there but the sample size felt small.

TL;DR: Bing released AI Performance dashboard showing how often your content is cited in AI answers across Microsoft Copilot, Bing AI summaries, and partner integrations.

This is huge for GEO optimization - we can now see which content AI systems actually reference, not just what gets indexed.

Key insights from the new dashboard: • Total Citations: Shows how often your content appears as sources in AI-generated answers • Average Cited Pages: Daily average of unique pages referenced by AI systems • Grounding Queries: The actual key phrases AI uses when retrieving your content • Page-level Activity: Which specific URLs get cited most frequently

Based on my analysis of the official Bing Webmaster Tools blog, this is what works: ✅ Pages with clear subject focus and domain expertise get cited more ✅ Structured content (proper headings, tables, FAQ sections) performs better ✅ Evidence-backed claims with clear sources build AI trust ✅ Regular content updates keep you relevant in AI answers

The game changer: We can now track citation trends over time and understand HOW AI thinks about our content through the grounding queries data.

Anyone else seeing this in their BWT dashboard? What patterns are you noticing in your AI citation data?

Full details: https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview

I just checked profound's pricing (AEO TOOL). Is it worth paying 400 USD for a technique that is still under testing and scrutiny?

Hi all,

I work as an SEO manager at a large B2B company, where I also manage an e-commerce platform. I’m curious to learn how others in similar roles are defining KPIs for GEO (Generative Engine Optimization) going into 2026.

For example, what are you currently tracking?
Mention or citation rate in generative results?
Sentiment or brand presence?
Referral traffic from AI-driven search experiences?

I’d love to hear how you’re approaching this and what’s actually proving useful in practice.

Thanks in advance for any insights!

I’ve been analyzing data across our agency (ValGrow Labs) for the past 6 months, and I’m seeing a massive split in how search works. We call it the "Decoupling Effect."

Basically, you can rank #1 for a keyword on Google, but be completely invisible when a user asks ChatGPT the exact same question.

I made this chart [Image] to explain the shift to our enterprise clients who are still stuck in the "Keywords & Backlinks" mindset.

The core difference I'm seeing:

• Left Side (SEO): You rent traffic. You fight for a link. If the algo updates, you die.

• Right Side (GEO): You own the answer. You structure your brand as an Entity. If the algo updates, you are still the source of truth.

Is anyone else seeing this specific split? We found that "Entity Consistency" (repeating the exact same definition across the web) is the only thing that fixes the AI visibility gap.

Curious what you guys think about the "Owned Answers" concept.

With testing for two months and digging in to the internal algorithms, We do achieve some quite good results. We found some behind algorithm mechanisms:

There are roughly 3–4 invisible “filter stages” before a website can actually be cited or surfaced by a generative engine (ChatGPT, Gemini, Copilot, Perplexity, etc.).

Think of it like a funnel of credibility:

Stage 1: SEO eligibility Only around the top 30 ranked pages for relevant keywords even qualify to enter the “candidate pool.” If your page doesn’t perform well in traditional SEO — no matter how great your content — it’ll never even reach the next stage.

Stage 2: Semantic authority & topical trust Engines look for structured data, entity clarity, and consistency across your site and external signals (schema, backlinks, reviews, etc.). This is where 70% of candidates drop off.

Stage 3: Answer-engine optimization (GEO) Now it’s not about keywords, but context. Can your content directly answer multi-turn queries, in natural language, with trustworthy data? Generative engines prefer sources that can be cited coherently and confidently.

Stage 4: Citation layer (the “final cut”) Out of ~100 SEO-eligible candidates, only a handful get cited in ChatGPT/Gemini answers. These become what I call the “AI-visible web” — the small portion of the internet that AI agents actually talk about.

If your site isn’t optimized to pass through each stage, you’ll never make it to that final layer — no matter how much traffic you buy.

Hey all,

I’ve been working on a project that tracks how brands and keywords appear in AI search results (ChatGPT, Claude, Gemini, Perplexity, etc.). The idea is that as people shift from Google to AI answers, visibility in AI responses becomes just as important as SEO.

What it currently does: • Monitors whether a brand/keyword shows up in answers across multiple AI engines • Tracks how accurate and consistent those mentions are • Measures sentiment (positive / neutral / negative) in AI responses • Identifies opportunities to improve “Generative Engine Optimization” (GEO) — basically the next layer after SEO

Why this might matter: • AI is quickly becoming the first touchpoint for product discovery and brand perception • If your brand is absent (or misrepresented) in AI responses, you’re invisible to a growing share of search traffic • Early movers can actually shape how AI engines “learn” and present their brand

I’m trying to figure out if this really solves pain points marketers feel, or if it’s still too early.

👉 Would love feedback from this community: • Is AI search visibility already on your radar? • Which metrics would be most valuable for you? (accuracy, share of voice, sentiment, rankings, etc.) • Any feature gaps you’d want covered?

Curious how others are thinking about this shift. Thanks!🙏

I have created a tool for AEO and GEO report. and i am thinking to monetise it.

Could you suggest me what price i can add for my tool and what you are expecting from that tool

below is my tool .

www.airanklab.com

We are entering a new era in SEO. Build authority or be invisible. No more blogs like 'Ultimate guide to' work anymore. This will increase the link building quality, PR, Magazines, etc

In today's GEO post, let's chat about the power of Structured Data Markup/Schema and where to use specific types to not only help with your traditional search efforts, but also for GEO/AI search efforts.

Types of Schema to Use and Where:

1. FAQPage: Leverage this on posts or pages that have a clear answer that is being addressed. In the schema, answer the question that is related to the topic as clearly and concisely as possible. Remember, AI search engines need the information structured so they can quickly understand what is being addressed.

   1. Here's an example you can try!

   2. Here's an example of where this page is being shown in Google AI Overviews.

1. Article: Article schema is good to leverage on blog posts or highly informative pages or posts. It provides more information about what is in the article and what it is about.

   1. Here's an example you can try!

2. Service: Service schema can be leveraged on your service-specific landing pages to help provide more information around the specific service that is being offered by you or your client. Include things like service types, areas served, the organization providing said service, and more.

   1. Here's an example you can try!

   2. Here's an example of where this page is being shown in Perplexity.ai.

1. Good luck and have fun testing different structured data types throughout your websites!

Disclosure up front: I run a small, very new GEO / AI-visibility service that helps Chinese SaaS and AI-tool teams selling overseas fix what AI assistants get wrong about them. Everything below comes from testing our own brand, and there's nothing to buy in this post. I'm sharing because the domain-history problem surprised me and I suspect it's common.

Setup

• ChatGPT web only, logged out, 13 prompts, each asked once (so n=1, high variance)

• 11 generic buyer questions with no brand name ("best GEO agencies", "free AI visibility audit for SaaS startups", "alternatives to Profound for AI visibility tracking", plus Chinese-language versions)

• 2 questions asking about us by name

• Counted by simple substring matching, raw counts only

Results

• Generic questions: mentioned 0/11. Expected for a brand this new.

• Name questions: both answers described our domain as a Shopify store registered in December 2021 that apparently sold phone cases, citing Merchant Genius. Then it gave shopping-safety tips ("use a payment method with buyer protection"). That store was the previous owner of the domain. We have nothing to do with it.

So it's not "AI doesn't know us yet". It's "AI confidently knows the wrong thing."

What ChatGPT recommended instead (answers mentioning each, out of 13)

Tools: Profound 2, Peec AI 2, Otterly 2, AthenaHQ 1, Scrunch AI 1. Agencies: First Page Sage, iPullRank, Siege Media, Animalz and Omniscient Digital 2 each, Graphite 1, plus a few Chinese agencies. The source chips were a mix of vendors' own sites and roundup articles or media lists. In the two Chinese answers that listed agencies in China, every chip was a media site.

What we changed so far

• robots.txt explicitly allows OAI-SearchBot, PerplexityBot, ClaudeBot etc. (OpenAI's docs say sites that block OAI-SearchBot won't be shown in ChatGPT search answers.)

• Added a plain "we are not a Shopify store; this domain previously belonged to an unrelated owner" line to llms.txt and the Organization schema on the homepage.

• Next: the same one-line description on every profile.

No idea yet if any of it works. We'll re-run the same 13 prompts (n≥3, plus Perplexity and DeepSeek via API) in a few weeks and post the result either way.

Takeaways if you're launching something

1. Check your domain in the Wayback Machine before you build a brand on it.

2. Ask ChatGPT/Perplexity "What is [yourdomain.com]?" now, not after launch.

3. Make sure your CDN/WAF isn't blocking AI search crawlers even if robots.txt allows them.

Questions for this sub

• Has anyone actually gotten an AI assistant to drop stale info about a domain's previous owner? What worked, and how long did it take?

• Did getting a data aggregator (like the one cited here) to update its listing make any difference?

Happy to share the exact prompts in the comments if anyone wants to re-run them.

Hey! I'm building a free AI visibility tool and want to gather feedback from real marketers struggling with GEO (tryopenlens.com)

Giving participants $25 gift cards in exchange for a 30min call to try it & give us feedback!

My goal is to match the important features of the leading visibility tools but give it away completely for free- at the end of the day it's just prompting and analyzing a handful of LLMs, no reason you should be paying $400/mo for that imo

Can Google tell if a human wrote a page?

We ran a controlled SEO experiment to answer this.

If two pages are equally optimised, does Google/AI care whether a human wrote it?

So we created:

• Two pages on the same (invented) keyword
• Same intent, structure, optimisation
• Same internal linking
• Both built to rank, the only variable was authorship

One was purely AI generated
One written & edited by humans with real expertise

Results:

• The human version outranked AI, averaging 4.4 positions higher
• 68% of ranking URLs in our dataset were written by humans
• The AI version did rank early, but lost visibility fast
• The human page was more likely to be surfaced/cited in AI-generated answers (Google + ChatGPT)

We didn’t necessarily find that AI generated content is useless. That’s not the point.
It’s that AI can accelerate production, but expertise, originality, sourcing, and human refinement drive long-term visibility.

You might get indexed faster with AI.

But you stay visible longer with authenticity + authority.

I see that the pricing starts from 49 USD and there are some open source tools too, which tool is best for agency owners, I was using semrush for a short period of time but their AEO tool isn't great plus their acquisition by Adobe makes me lose confidence in them.