r/GenEngineOptimization

We pulled a bunch of AI source citations (1M+) across 6 software prompt groups (CRM, cybersecurity, marketing automation, data analytics, collaboration tools, AI agents) in 12 countries, 7 languages, and 4 models (AI Overview, Copilot, ChatGPT, Grok). The aim was to measure how many of the websites cited in one country's AI results also appear in another country's results for the same prompt.

Short answer: not very many. Across all country pairs, the mean source overlap ranges from 7% to 19% depending on the model. The single highest-overlap pair in the dataset is Canada-US on ChatGPT at 24%. Even there, three of every four cited sources differ.

Statistic in % of country by country source overlap in AI Search results

Practical conclusions:

• Expanding into a same-language market (US into UK/AU/CA, or ES into MX/AR) → expect roughly 21% of your existing AI source presence to carry over.
• Expanding into a different-language market → expect 7% source overlap on average.
• Optimizing for Copilot vs Grok → very different localization profiles. Copilot is the most country-specific (6.7% mean overlap); Grok is the most global (18.8%)

One thing worth noting: even in a global category like software, local ccTLD domains account for 21-42% of AI citations in non-US markets.

Does this match what anyone here is seeing for cross-border AI search in B2B SaaS?

Disclosure: I work at Temso AI (we build AI Agents for GEO/AEO). We used our infrastructure to collect and analyze the data. Happy to share methodology if useful.

I keep seeing people talk about llms.txt for AI SEO / GEO.

The idea makes sense in theory: give AI crawlers a simple file that explains which pages matter.

But right now it feels pretty unclear.

Google says it doesn’t use llms.txt for Search. There’s no proven ranking benefit. And I don’t think most SaaS teams should spend serious time on it.

That said, it’s also cheap to add.

My current take:

I wouldn’t make it a “project”.

But if it takes under an hour, I’d probably add a simple version with only:

• homepage
• pricing
• docs
• comparison pages
• use-case pages
• integration pages

I would not include every blog post or expect traffic from it.

Mostly, I see it as a low-cost experiment and a way to clarify which pages are actually important.

So for me:

Not urgent. Not proven. Probably worth a tiny test if your site already has strong product/docs pages.

Curious what others are doing.

Are you adding llms.txt, ignoring it, or waiting for clearer evidence?

Hi Guys

Recently started working in an agency that does reddit marketing and helps cite posts and articles on various AI tools and platforms.(In short -AEO)
Have been experimenting with various tools, yet to find the perfect one.
Drop your experiences and recommendations if any. Will be of great help

Oh wow, the AI Overview debate is getting intense.

Every week there's a new post asking if traditional SEO is dead. And honestly? Some of those posts have a point.

Here's my take after 6 months in the GEO/AEO space.

**What AI Overviews actually killed**

• **Rank #1 doesn't matter**: I've seen the same source appear in position 1 and position 5 in AI Overviews. The #1 ranking gets clicked, but the AI doesn't care about it.
• **Keyword optimization is useless**: AI ignores your carefully placed keywords. It understands the context, not the keywords.
• **Long-form content**: 2,000-word guides are getting cited just as much as 600-word answers.

**What still matters**

• **Structure**: Answers that are easy to parse (bullet points, numbered steps) perform 3x better
• **Direct answers**: AI cites content that answers the question in the first 2 sentences
• **Authority signals**: Citations still prefer domains with real E-E-A-T signals

**The uncomfortable truth**

Traditional SEO isn't dead — it's just changed. The old playbook (keyword stuffing, long titles, link velocity) doesn't work anymore. But SEO for AI (answering questions, structured data, transparent E-E-A-T) is more important than ever.

From my experience, the sites winning right now aren't the ones with the most backlinks. They're the ones making it easiest for AI to parse and quote.

I’ve been testing transactional queries with slight phrasing shifts to see when our product triggers. What’s wild isn’t just our visibility, but how locked-in specific competitors are.

They normally appear regardless of prompt structure, while others only surface in narrow contexts. Is anyone else reverse-engineering this brand-intent logic?

Update: Circling back to this in case anyone else needs similar help, I checked out a whole lot of tools, but GentrackAI seems the most promising so far.

Here's what none of them told you.

The update covers Google Search. AI Overviews. AI Mode. One ecosystem, one policy, one surface.

ChatGPT. Perplexity. Copilot. Gemini standalone. Claude. No equivalent policy exists on any of them. No enforcement mechanism. No guidance. No rules.

Which means the brands celebrating yesterday's update have solved roughly 20% of the problem and declared victory.

But the policy gap is not even the real issue. The real issue is what we see in Conversational Survival Rate data across platforms.

Remediation is platform-specific.

The evidence architecture that lifts your brand to a T4 purchase recommendation on ChatGPT doesn't transfer to Perplexity.

What moves Gemini standalone doesn't move Copilot.

Each platform has different retrieval logic, different training provenance, different evidence hierarchies.

A brand that fixes its Google AI performance can simultaneously be losing the final purchase recommendation on every other platform - and have no way of knowing it.

We have tested this across categories. The CSR differentials across platforms for the same brand, with the same content, are not marginal. They're large.

The platform that recommends your brand most often is frequently not the platform your customers are actually using to make the decision.

Google's guidance document published alongside the policy update says foundational SEO solves the AI problem. It doesn't.

That advice is true for Google Search. It is incomplete everywhere else.

And "everywhere else" is where a growing share of purchase decisions are being made.

Brands that treat yesterday's update as closure are making a measurement error. They're assuming the room Google cleaned is the room that matters.

AIVO Meridian measures all five rooms. CSR tells you exactly where your brand is surviving - and where it isn't.

Are you an SEO, an AEO or a GEO? Which one (or combination) really works in AI search, across all platform?

Stop ranking and start being cited.

I help businesses optimise for the AI era. Get your brand mentioned in ChatGPT, Google AI Overviews, and GEO results.

The era of thin content stuffed with long-tail keywords is gone. Long-form content based on an E-E-A-T strategy is your only way to establish niche authority.

It’s easy to generate content using ChatGPT, Gemini, or Claude. However, these tools don’t add quality or depth unless you know how to architect the prompts and the structure. (Quick Tip: AI engines prioritise "information gain". If your content doesn't add a new perspective or data point that isn't already in the top 10 results, it won't be cited.)

Don't let your competitors own the AI summary. Reply "Get My Brand Cited" and I'll take a look at your site.

I know the importance of reddit in GEO/AEO- but it needs to be authentic
i dont want to start telling you that I offer GEO services for my company,here is my site, bla bla bla
I am using reddit for years (this is a new account) I thought of joining this channel, find relevant questions, give my honest, authentic opinion and slightly mention my company

1. do you think this will help GEO?
2. would you appreciate it as a reddit user?
3. are you doing something similar? are you using reddit at all for GEO?

Everyone's trying to boost AI visibility.

Optimising for BOFU clicks and trying to get recommended when someone searches "best x tool alternatives" in the LLMs.

But how much effort is going toward the TOFU/MOFU stage.

Framing the questions and requirements.

Isn't it more about defining 'x' around your product/service.

So by the time it gets to 'best tool for x' you'll show up?

How are people going about this?

Been noticing that some content consistently shows up in AI overviews while other content doesn’t get picked up at all

trying to understand what really makes the difference here

from what i’ve seen so far, it doesn’t feel like traditional SEO alone explains it

patterns i’m starting to notice:
• content that answers clearly and directly (almost like it’s written for extraction)
• structured sections (definitions, steps, summaries)
• strong topical consistency across the site
• content being referenced or echoed across different sources

it feels less like “ranking a page” and more like:
making your content easy to understand, reuse, and trust

also noticing that some pages with lower rankings still show up in AI summaries, which is interesting

curious how others are approaching this

are you intentionally structuring content for AI overviews now,
or just focusing on traditional SEO and letting it happen naturally?

Datos published a study showing that AI is not outpacing search in growth or usage.

On an absolute basis, traditional search is outpacing AI tool growth.

https://preview.redd.it/xk9c8civ0qzg1.jpg?width=1280&format=pjpg&auto=webp&s=e4ba0db876c487965f3b4892fb893847e50aa6c6

Despite the "disruption", people are searching Google as much as ever...

https://preview.redd.it/t0ubt6o01qzg1.jpg?width=1280&format=pjpg&auto=webp&s=7173829912119a492cf098dde0ac35e1634cb5c6

Now, before you attack this thread, I am not claiming this should convince anyone to forget about LLM optimization. I believe SEO and GEO are inseparable.

If there's one thing that is actually disrupting SEO (or else its traditional metrics and KPIs), it is the AI Overviews as they are the biggest drivers of 0-click marketing at this point.

Source: LinkedIn / u/randfish

They are the same thing. Different names for the same objective: optimise a brand's presence in an output. Whether that output is a search result, an AI citation, or a generative summary, the metric is the same. Did the brand appear?

Appearance is not selection.

Agentic Brand Control is a different category with a different objective entirely.

When an AI agent runs a buying conversation on behalf of a consumer - assembling a consideration set, evaluating criteria, eliminating options, and routing to a final recommendation - the question is not whether your brand showed up. The question is whether it survived.

We call the final recommendation the T4 handoff. It's the moment a brand either takes the sale or disappears from the journey. In 12,000+ buying sequences we've run across ChatGPT, Gemini and Perplexity, 87% of brands that appear early don't reach it.

The gaps that determine survival are diagnosable. Entity recognition. Criteria alignment. Price justification. These are not content problems. They are evidence problems — specific, structural deficits in how an LLM interprets a brand when it has to make a decision under open consideration.

That is what Agentic Brand Control addresses. Not visibility. Selection.

The objective is to close the gap between a brand appearing in AI outputs and a brand being chosen at the end of the conversation that matters.

The category is new. The measurement is real. The stakes are rising.

Are you an SEO, a GEO/AEO or an Agentic Brand Controller?