r/aeo

From what I've seen so far, citation tracking is pretty underrated and often not included in a lot of plans by the 'big' tools. But the thing is, I'm still quite confused by it, because there are some instances where the same page ranks fine in search but doesn't really get cited by LLMs (There's also some pages with unoptimized SEO but they still show up because of how frequent they get referenced)

So is it less about authority and actually more about how often your content shows up in prompt variations? How do you best track your prompts and do they differ for different LLMs like GPT, Claude, Gemini etc?

I've been obsessing over how to get mentioned by AI engines, and I stumbled on something that feels totally backward.

I was digging into why an AI model cited one article and completely ignored another on the exact same topic. The article that got the mention wasn't better. The data wasn't clearer. It just had one thing the other didn't, a direct quote from a person.

The AI literally pulled the quote, "According to Jane Doe, 'the key is...'" and cited that article. The other one, which just stated the facts without attribution, was invisible.

My working theory is that the models learned what's "citable" from news articles and academic papers. In that world, a quote from a named person is a gold-standard source. A sentence stating a fact is just a sentence. But that same sentence attributed to a person is treated like evidence.

It makes me think the old-school PR play of getting your founder quoted in industry pubs has a totally new purpose. It's not just for the backlink. It might be how you feed the AI quotable facts.

Anyone else seeing this or testing something similar?

A lot of companies are now producing content specifically for AI visibility, but I’m starting to wonder if most of it is actually effective or if some of it is quietly making things worse.

It feels like a lot of AI-optimized content is technically structured well, but ends up sounding too generic or repetitive. And I’m not sure how that impacts how both users and AI systems perceive authority and trust.

Curious what people are seeing in real-world results.

Has anyone here actually tested GEO/AEO strategies at scale?

• Are you seeing improvements in AI citations, rankings, or traffic?
• Is GEO/AEO actually working when implemented properly, or still mostly experimental?
• How do you balance structured optimization with real human credibility and depth?

I was trying to inspect what LLMs actually search before answering, not just the final output.

So I built a browser bookmarklet that opens a separate terminal-style view and shows:

• grounding/fan-out queries
• domain-scoped vs open-web searches
• cited domains that survive into the final answer
• source concentration across retrieved results

It currently works with:

• ChatGPT live conversations
• Claude live conversations, with JSON import fallback when live access is not available

The main reason I built it was for SEO/GEO/retrieval debugging. In a lot of cases, the interesting part is not the answer itself but:

• what queries the model fanned out into
• whether it used explicit site constraints
• which domains kept surfacing
• which sources actually made it into the response

I’m posting this mainly to get feedback on the approach:

• would you inspect anything else in the retrieval chain?
• what would you want to export?
• would Gemini/AI Mode support be useful?

If people are interested, I can share the repo in the comments (but i don't even know if i can post link here...)

$15 billion in annual revenue. One of the most recognised travel brands on the planet. Near-universal brand awareness.

Eliminated at Turn 1 on Perplexity and Gemini in an undirected buying journey.

This is the verbatim language the model used at T1 when deciding which travel platform to recommend:

"Which travel booking platforms have established market presence with documented track records of proven effectiveness, overall value, reputation, and ease of access?"

Expedia failed that criteria. The finding: "No established evidence of proven effectiveness in travel booking domain."

At T3 on Perplexity, Booking(dot)com displaced Expedia on this criteria:

"Which platform has independently verified evidence of superior customer satisfaction and proven reliability metrics?"

There is also a T0 Decision-Stage gap on ChatGPT and Gemini where brand entity recognition fails to persist across conversational turns when user responses are minimal or ambiguous — meaning the model loses track of Expedia mid-conversation and routes elsewhere.

The only probe type where Expedia holds throughout is ChatGPT Directed — when the user names Expedia explicitly. On every undirected and agentic journey type, on every platform, there is displacement.

RCS 77. Revenue at risk at current LLM share: $82.8M. At 2027 LLM share: $165.6M.

Five filter gaps identified. All addressable. None of them visible to any citation or visibility tool.

This is what decision-stage AI measurement looks like versus first-prompt visibility scoring. The model knew Expedia. It could not find the structured evidence it needed to pass the criteria filter at the decision stage. Those are different problems with different remediation paths.

Full audit methodology: aivomeridian (dot) com

Every time I use ChatGPT, I notice that some answers feel super solid like you can trust them instantly. Others feel more general or less convincing. I can’t fully explain it, but maybe it has something to do with how consistent the information is across different sources. Like if the same idea keeps showing up in similar ways, the AI probably feels more confident saying it clearly. Does that make sense, or is there something else going on here?

We’ve been auditing several local sites lately at my agency, Monkey Plus (based in Ecuador), and the biggest issue isn't a lack of backlinks—it's straight-up self-sabotage.

Many business owners still think that "the more pages I have on a topic, the better." In reality, they are just confusing the hell out of Google and AI models.

The "Invisible" Ranking Killer: Keyword Cannibalization.

With the shift toward AEO (Answer Engine Optimization) in 2026, having multiple pages targeting the same intent is a death sentence for your reach. Here is what we are seeing:

Instead of one high-performing "Power Page" with all the internal links and authority, brands have three or four weak posts. Google doesn't know which one to rank, so it ends up ranking none of them well.

LLMs (like Gemini, GPT, or Perplexity) look for a single "Source of Truth" within a domain. If you have three different articles on the same service, the AI often fails to cite you because it can't identify your definitive stance. It just moves on to a competitor with a cleaner structure.

Our "Cleanup" Workflow:

We’ve moved away from just adding more content. Now, we focus on architectural efficiency:

1. The Merger: If we find three 500-word posts fighting for the same keyword, we kill the weak ones. We merge them into one deep, 1,500+ word resource.
2. 301 Strategic Redirects: We don't just delete the old URLs. We 301 them to the new "Power Page" to make sure every bit of historical "juice" is preserved.
3. Intent Mapping: We ensure every URL has one specific job. If two pages are doing the same job, we have a conflict.

In a growing market like Ecuador, where digital competition is heating up, this clean-up alone has boosted rankings for our clients more than any new "AI-generated" content ever could.

Don't fight against your own site. Merge competing pages into one authoritative source to make it easy for Google and AI engines to trust you.

How are you handling "Intent Overlap" across large e-commerce sites without losing long-tail traffic? Would love to hear your thoughts.