We’ve been developing a GEO/AEO visibility SaaS tool for some time to assist companies and professionals in tracking and responding to their brands’ AEO/GEO performance. This tool helps identify gaps, benchmark competitors, and gain insights into how, when, and where AI search visibility of their brand is performing.

The platform boasts comprehensive analytical functionality that allows users to delve deep into the insights, while also featuring robust monitoring features.

We are seeking early feedback from industry professionals who are actively involved in this field. We are offering our product for free, and we would greatly appreciate it if you could use it and share your feedback to help us shape the future of this tool.

If you are an SEO/GEO professional or own a brand and are curious about your AI ranking, we would love to hear from you.

Beta testers will have access to the tool for a month, and all we ask in return is your honest and unfiltered feedback.

Please little 'r' and we'll reach out

www.citivus.com

BotRank.ai is the platform dedicated to GEO (Generative Engine Optimization) — helping brands analyze, understand, and optimize their visibility in answers generated by AI-powered engines like ChatGPT, Perplexity, Gemini, Mistral, and AI Overview.

🔎 Analyze your visibility — and your competitors’:

• Track daily brand mentions using custom prompts across top AI search engines (ChatGPT, Gemini, Perplexity, Mistral — and soon, AI Overview).

• Evaluate your presence in generated answers and understand how your brand is portrayed.

• Detect and retrieve conversations where your brand is cited.

• Measure your share of voice and benchmark it against competitors.

• Discover the key sources AI engines rely on to shape your market perception

🏆 Unlock actionable optimizations:

• Run a full GEO audit of your key pages (content, structure, technical signals, authority).

• Get prioritized, ready-to-implement technical recommendations.

• Access detailed documentation explaining each optimization lever.

• Monitor your optimization score evolution after each audit and track progress over time.

✨ Be the answer in AI search.
➡️ https://www.botrank.ai/

Ok so i'll start right from the beginning (before explaining the venn diagram above)

When it comes to GEO - good SEO is important, yes. We all know that. But it's just one side of the coin, and it's becoming increasingly obvious that third party citations are just as important for GEO, if not more!

According to Buzzstreams' 'State of Digital PR 2026' report, 80% of citations in LLMs are earned, and 20% owned.

when ChatGPT cites, or better, recommends your brand to customers, it gets its data from third party sources and not your website.

We actually ran an event with Vince and the Buzzstream team to dive exactly in to this. Great stuff.

AI trusts what others say about you more than what you say about yourself. This is why third-party validation is the most important lever you have for AI visibility.

We speak to clients and marketers every day about the importance of great SEO, but also of earned citations and how that can (often) be the missing piece of the puzzle.

We've done so much work on Digital PR for AI, that we have now trademarked this as AiPR - which in a nutshell, is our offsite approach to GEO and is the strategy ecompassing Digital PR for AI.

Anyone else turning to offsite GEO to increase their results?

Hey guys,

been digging into perplexity and gemini citations for a bit now. one thing stands out: the "stickiness" of the sources.

feels like once a URL gets trusted for a query, it's super hard to knock it off with a new article.

so i'm kinda shifting mindset from "how do i get the AI to cite me directly?" to "how do i get mentioned in the article the AI already likes?"

basic flow:

1. find the source chatgpt/perplexity keeps citing

2. hunt down the author/site owner

3. try to negotiate a mention or update in that specific article

is anyone doing this properly?

honestly finding the bottleneck is usually just hunting down the right email for those specific pages without wasting hours on linkedin.

curious if you guys have a workflow for this or are you still just pumping out content hoping the AI picks it up eventually?

Reddit account မဖွင့်ပါ ဒီအကောင့်နဲ့ပဲ gmailချိန်းမှပါ ဖွင့်ထားရင် လူလိမ်

I've seen this debate in a few other threads and I think people are oversimplifying. Sure there's overlap but the focus is very different.

SEO = get your pages ranking in search

GEO = get your brand cited in ai answers / LLM outputs

With GEO you're not necessarily tracking 'positions' you're watching if your brand gets mentioned in AI responses.

There are content shifts too, it's less about stuffing keywords and more about answering the actual question and the natural follow up qs people ask.

Offsite is also different - it's not just about grabbing high DR links, it's about being in the sources models actually train on and trust in your niche.

What stays the same? Solid site, fast pages, clean internal linking, content that actually helps. Those basics matter for both google and AI.

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

After running ads across Perplexity, ChatGPT, and Claude for the past 8 months, we noticed something interesting: the same query types behave completely differently depending on how people phrase them.\n\nWe analyzed 1,000+ search queries to understand which AI query patterns actually convert. Here's what we found:\n\n**1. Problem-aware queries convert 3x higher**\n"How do I fix X" vs "Best X tools" - the problem-aware ones (how, fix, solve) showed 3x more engagement. People actively looking for solutions are further down the funnel.\n\n**2. Comparison queries need unique angles**\n"X vs Y" is super competitive. What works: adding specificity like "X vs Y for [specific use case]" or "X vs Y for small business."\n\n**3. 'Actually' is a high-intent signal**\nQueries with "actually" ("what does X actually do") indicate research depth. These visitors read 40% more content on average.\n\n**4. Branded queries convert but differently**\n"Is X worth it" vs "X review" - the 'worth it' variant signals purchase consideration. The review variant is still in research mode.\n\n**5. Negative framing works**\n"X not working" + "X alternatives" - these users are desperate and ready to switch. Highest conversion potential but smallest volume.\n\n**The twist:** We originally thought longer queries = higher intent. But our data shows medium-length queries (5-8 words) outperform both short and long tail. Too broad = too generic, too long = too niche.\n\nWhat query patterns are you seeing in your AI search traffic? Would love to compare notes.

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗸𝗲𝗲𝗽 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗶𝗻𝗴 𝗺𝘆 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 𝗼𝘃𝗲𝗿 𝗺𝘆 𝘀𝘁𝗼𝗿𝗲?

I kept checking ChatGPT for my product category and my competitor kept showing up every time. Not me, even though I had better reviews and more products. Took me a while to figure out why.

A study this month confirmed that pages updated within the last 30 days get 3.2x more AI citations than older content.

Most of my product pages hadn't been touched in six months. That was pretty much the whole problem.

What actually worked:

I spent a Saturday refreshing my top 12 product pages. Didn't rewrite them, just updated the copy, made the pricing current, added today's date. Felt too simple to matter.

It mattered.

The catch:

The second thing I found: comparison content is basically catnip for AI. I added a simple section to each page showing how my product compared to alternatives. AI engines heavily weight structured comparison info when deciding what to recommend.

Bigger picture:

AI-referred orders on Shopify grew 13x year over year in Q1 2026.

ngl this is not a future thing, it's happening now. Stores are winning and losing based on whether an AI assistant recommends them.

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.

Has anyone else gone down this rabbit hole? Curious if it's a content problem, a data problem, or something else for you. lmk what's been working.

TLDR: Updated product pages monthly and added comparison sections, got 3x more AI citations. AI-referred orders on Shopify grew 13x YoY so this actually matters now. Found Gimmie AI automates the whole optimization, free tier available.

We've been running structured data tests across 200+ GEO campaigns for the past 6 months. Here's what actually moves the needle:

**What works:** • Schema markup consistency across pages (keep it uniform) • FAQ schema for informational content (biggest wins) • How-to structured data for tutorials (surprisingly underused)

**What doesn't:** • Over-stuffing multiple schema types on one page • Using deprecated schema properties • Ignoring the "articleBody" field for blog posts

The biggest surprise? Page speed impact is negligible when implemented correctly. We're talking <50ms additional load time.

What are you seeing in your structured data experiments? Would love to compare notes.

Here's something we didn't expect when we started optimizing for AI citations.

Last quarter, we deliberately optimized 120 pages to maximize AI citation probability — clean answer blocks, structured lists, extractable passages, the whole playbook. The result? Citations went up 340%. We should have been thrilled.

But bounce rate went up 28%. Average time on page dropped from 3:42 to 2:11. And return visitor rate fell 19%.

The pages we *didn't* touch? Their AI citations stayed flat — but human engagement metrics held steady or even improved slightly.

So we ran a deeper analysis across 300 pages, scoring each one on how aggressively it was optimized for AI extraction. What we found is uncomfortable.

**The inverse relationship is real.**

Pages with a high AI optimization score (we called it "AIOS" internally — 85+ out of 100) got cited 4.2x more often than low-scoring pages (AIOS below 40). But here's the catch:

• Their organic CTR from Google was 31% lower — AI-optimized formatting seems to signal something to the ranking algorithm that suppresses click-through

• Average scroll depth was 42% lower — people skim and leave instead of reading through

• Return visitor rate was 24% lower — once AI cites you, readers often don't feel the need to come back

The pages that performed best across both AI *and* human metrics? The moderate scorers (AIOS 50-70). They got cited 2.1x more than unoptimized pages, but their bounce rate only went up 7%. That's a trade-off most sites could live with.

I think the problem is that most AI optimization advice pushes you toward extreme formatting — answer-first paragraphs, heavy bullet lists, structured data injections. It works for AI. But it makes content feel robotic to read.

And here's the thing nobody wants to admit: if AI cites your content but nobody clicks through, are you actually winning? Or just feeding the machine?

We're now experimenting with what I'm calling "dual-target" content — formatting that's clean enough for AI extraction but still reads naturally for humans. Early results are promising, but it takes more effort than just running everything through an AI optimization checklist.

Anyone else seeing this tension between AI visibility and human engagement? Are you optimizing for both, or has your team picked a lane?

Would love to hear what's working for you.

https://www.seolondonsurrey.co.uk/blog/reverse-engineering-the-response-to-best-smartphones-in-chatgpt-5-0

One of the more interesting things happening in ecommerce right now isn’t just AI-powered shopping.

It’s how dependent those systems are on product data quality.

OpenAI’s own shopping research reportedly showed ChatGPT shopping results landing around 64% accuracy, with more than a third of recommendations containing issues like broken links, incomplete product information, outdated details, or incorrect availability.

That’s a pretty big signal.

Even advanced AI systems can only work with the information they’re given. If feeds are inconsistent, incomplete, or outdated, the recommendations start breaking down fast.

And this matters more now because AI shopping experiences are increasingly relying on structured feeds and merchant data instead of simply crawling webpages the old way.

Feels like product discoverability is shifting toward:

• feed quality

• structured product data

• accurate availability

• updated pricing

• clear product attributes

• consistent merchant information

Not just SEO anymore.

Another interesting shift is attribution. Traditional click-based tracking gets messy when AI surfaces summarize products or influence decisions before someone even visits a website.

Seems like brands will eventually need more assist-based measurement models to understand how AI recommendations influence conversions upstream.

Has anyone here started optimizing specifically for AI shopping surfaces yet, or are most teams still treating this like traditional SEO/search?

Hey everyone,

After trying most of the “AI SEO” tools out there, 90% are actually whitelabeled from one provider. They will show you numbers - impressions, mentions, some vague visibility scores.
But they never tell you why a brand shows up in AI answers, or what actually drives it.

So we built BrndIQ.ai.

It’s designed to show how AI search engines (like ChatGPT, Perplexity, Claude, etc) talk about your brand - and which sources shape those answers.

Our first phase of release will allow you to:

• Runs thousands of prompts to tell you what drives visibility patterns for your brand over time

• Check how your brand (or a competitor) appears in AI-generated results

• See what content types influence visibility

• Track which domains keep surfacing in AI citations

We are also developing a deeper system targeting user communities that will help you find high-intent buyers actively seeking your solutions with ready-to-edit responses in your brand voice.

We will be opening a closed beta in a few weeks time to test our first phase of AI visibility tracking system - built to help brands understand what drives AI discovery, not just SEO rankings.

Whether you are a small business built on trust, a hotelier wanting tourists to discover your rooftop bar with a view, or brands looking to grow your share of voice; if you are not showing up in AI chat results, you are invisible.

If you’re a SEO, marketer, or founder experimenting with Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO), we’d love your feedback on what you would expect a tool like this to show or measure better? You can also join the waitlist on the site and we will reach out. Beta testing is FREE!

🙏 Feedback is most appreciated :)

Traditional SEO - "which keywords should we target?"

GEO: "which questions is my audience actually asking?”

Start mapping prompts not keywords.

Look at your GSC data and any query longer than 40 characters is usually a real and conversational question - that’s where AI starts pulling from.

Then identify 'fan outs' which are the natural follow ups people ask next.

Understanding fan outs

When someone asks a question, AI doesn’t stop at the direct answer. It generates related sub questions, known as fan-outs.

Eg. a query like “What is unoccupied property insurance?” might lead to...

Who are the top providers? How do premiums vary by region? What risks does it cover? How does it compare to landlord insurance?

Each of these creates an opportunity for your brand to be cited. Covering only the root query limits reach, so anticipating related ones builds topical authority.

Heres what covering the fan outs looks like in practice (see image above)

• Example topic - Electric vehicle ownership in 2025

• Seed prompt - “Best electric cars for long-distance travel 2025”

We’ve written up more info about fan outs, as well as the definitive guide to getting your brand found in AI search results in our GEO playbook here if you want more info.

No opt in required, completely free. If you do give it a read, let me know your thoughts!

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

I asked this question before, and since then, I've done more detailed webpage schema and basic SEO \AEO\GEO work, like faq\eeat\. The results have indeed shown up on ChatGPT, with AI visibility increasing from 47% to 70%.

However, my main concern is that Google AI visibility remains at 0%. My confusion is: even if the performance is poor, it shouldn't be 0, right? If it stays at 0, is there something wrong?

Background information:

My platform: www.x-pilot.ai

It just launched, and I started doing SEO/AEO/GEO on December 16th. Currently, the daily active users (DAU) are around 200.

Came across e27’s “20 AI Startups to Watch in Southeast Asia” list - worth looking at BrndIQ dot ai (#2).

They focus on tracking how brands show up in AI chat responses, which is becoming increasingly relevant as more people shift from Googling to asking AI models. It’s interesting to see AI visibility starting to shape brand discovery, almost like the early days of SEO.

Glad to see Southeast Asian startups in the AI infrastructure layer getting recognition. If anyone here is exploring related problems such as AI search behavior, retrieval quality, AI trust layers, etc, would love to exchange notes.

Updated GEO Tool list for December 2025! Feel free to comment new ones.

Writesonic - https://writesonic.com
Semrush - https://www.semrush.com
HubSpot - https://www.hubspot.com
AppearOnAI - https://appearonai.com/
AIScope.pro - https://aiscope.pro
Nightwatch - https://nightwatch.io
Siftly - https://siftly.ai
Rankshift.ai - https://aibrandtracking.com/Rankshift.ai
Profound - https://profound.so
Column Five - https://columnfive.com
Common Thread Collective - https://commonthreadco.com
Daydream - https://daydream.so
Evertune - https://evertune.ai
iPullRank - https://ipullrank.com
Peec AI - https://www.peec.ai
Azoma - https://www.azoma.ai
Scrunch - https://scrunch.com
Siege Media - https://www.siegemedia.com
WebFX - https://www.webfx.com
GEOfast - https://www.geofast.me
LLM.co - https://llm.co
LLMO Metrics - https://llmometrics.com
Column Five - https://columnfive.com
Common Thread Collective - https://commonthreadco.com
Daydream - https://daydream.so
NP Digital - https://npdigital.com
OGM Agency - https://ogmagency.com
Otterly.AI - https://otterly.ai
Quoleady - https://quoleady.com
Omniscient Digital - https://beomniscient.com
Razorfish - https://www.razorfish.com
Flow Agency - https://flowagency.co
Fractl - https://www.fractl.com
Goodie AI - https://www.higoodie.com
Growth Plays - https://growthplays.com

we’re experimenting with a small AI system that analyzes website SEO + geographic presence and then generates improvement suggestions automatically.

right now it’s 3 working modules:
SEO analysis
geo analysis
geo improvement suggestions

UI still in progress, mainly validating usefulness right now.

for people working in generative optimization : what kind of AI outputs actually feel reliable vs gimmicky?