r/GEO_optimization

Here's something that's been driving me slowly insane.

We noticed about 5 months ago that our brand kept getting mangled in AI responses. Wrong industry. Wrong product category. Sometimes conflated with a competitor whose name starts with the same letter. occasionally cited with a tagline we retired 2 years ago. Not everywhere — maybe half the time it showed up correctly. But the other half was doing real damage.

So we went deep on entity optimization. Schema markup. Wikidata entry. Knowledge graph cleanup. Consistent NAP across 80+ directories. Internal linking around entity anchors. The full playbook. I basically became obsessed with making sure every signal about our brand across the entire web told the same story.

5 months later, here's where we are: 53% of AI brand mentions are now correct. Up from roughly 33% before. That's a real improvement. I'm not dismissing that.

But 47% are still wrong. And that's after doing basically everything in the GEO entity playbook.

**What I think is actually happening:**

AI models don't resolve entities the way we assumed. We treated it like a structured data problem — if every source says the same thing, the model will pick it up. But entity resolution in LLMs seems to be more like a weighted vote across their training data. And training data includes Reddit threads, old blog posts, podcast transcripts, YouTube descriptions — stuff we can't touch.

We found 3 specific patterns that kept corrupting our entity:

1. **Adjacent industry confusion.** We operate in a niche adjacent to a much larger category. About 20% of wrong mentions placed us in the bigger category. The model basically rounds up — if 80% of the context it retrieves points to the bigger category, it assigns us there regardless of what our schema says.

2. **Competitor co-mention contamination.** We're frequently mentioned alongside one specific competitor in comparison content. Over time, the model started blending attributes. Their features would show up in our description about 12% of the time. Our pricing would occasionally appear in their profile.

3. **Historical inertia.** Stuff we published in 2023 that's no longer accurate still surfaces in training corpora. We updated our product description 8 months ago, but older versions live on in scrapers, archives, and syndicated copies. The model doesn't know which version is current.

**The uncomfortable realization:**

Entity optimization isn't something you can fully solve at the page level. You can improve it — we did. But the last mile comes from corpus-level signals you don't control. Forum discussions. Third-party articles. Old content that won't die.

The biggest jump in accuracy came from something I didn't expect: getting mentioned correctly in 4 large subreddit threads. Not links. Not promotion. Just people accurately describing what we do in natural conversation. Within 6 weeks of those threads, our brand mention accuracy jumped 11%.

Meanwhile, the structured data work — schema, Wikidata, knowledge panels — moved the needle maybe 4-5% over the entire 5 months. Not nothing, but way less than I expected given how much time we invested.

**Where I've landed:**

I still do structured entity work. It's table stakes. But I now spend more time monitoring how people describe us in places I can't directly control — and trying to influence that through accurate, easy-to-repeat descriptions in our own content.

If your entity is getting mangled by AI, the fix probably isn't more schema markup. It's figuring out which parts of the training corpus are polluting your identity and finding ways to dilute that with accurate signals from sources models actually trust.

Not a clean answer, I know. Still working on it myself. But if anyone's gone deeper on entity disambiguation specifically for AI models, I'd really like to compare notes.

I've been testing the same type of query across ChatGPT, Perplexity and Gemini for a few different niches, swapping only the topic.

The same pattern shows up every time. Out of hundreds of pages that exist on a subject, the model pulls from the same three to five sources, over and over, even when newer or more thorough content exists elsewhere.

The pages that keep winning share one thing. They commit to specific numbers, specific dates, and specific named entities instead of paraphrasing a general idea.

A page that says "SEO consulting typically costs between R$2,500 and R$6,000 a month depending on scope" gets pulled into an answer.

A page that says "SEO consulting can vary a lot in price" does not, even if it covers the exact same topic in more words.

Volume of content doesn't seem to matter much once a site clears a basic threshold.

A blog with 40 posts that each commit to a real claim beats a blog with 400 posts that hedge everything, because the model is scoring how confidently it can restate your claim without getting it wrong.

Vague writing is safe for the writer and useless for a system trying to extract a fact.

Consistency across your own pages matters more than most people account for. If your about page says one thing, your service page says something slightly different, and a directory listing says a third thing, that inconsistency reads as noise to a model trying to build a stable picture of who you are.

Pick the facts that matter (years of experience, location, exact services, pricing range) and repeat them identically everywhere they appear.

The last piece is getting those same specific facts to show up on sites you don't control. A model trusts a claim more when it sees it independently confirmed somewhere else, not just repeated on your own domain.

This is the part most people skip, because it's slower than writing another blog post, and it's also the part that actually moves the needle.

Curious what others here are seeing. Is anyone tracking which specific pages get pulled into answers versus which ones get ignored, even within their own site?

Been trying to figure out why some pages with solid technical SEO and clear content still don't show up when their category gets asked about in ChatGPT or Perplexity.

Ran a batch through a diagnostic that checks retrievability separately from citability, whether the content can be found and parsed versus whether it reads as something worth attributing a claim to.

Pattern that showed up most: retrievability was fine on almost all of them. The failure point was citability. Vague claims, no concrete numbers or specifics, content that reads like marketing copy instead of a source. Models seem to skip citing that even when they've clearly indexed the page.

The pages that did get cited had one thing in common, they stated something specific and checkable in the first two sentences, not three paragraphs in.

If anyone wants to run their own pages through the same check, the tool I used is free, no signup wall: [link]. Curious if others are seeing the same retrievable-but-not-citable gap or if your data looks different.

Google and ChatGPT don't read your article top to bottom before deciding whether to recommend you.

They break it into pieces, retrieve the pieces that answer a specific question, and often show or cite that piece without anyone ever visiting your page.

If a paragraph only makes sense next to the one before it, it gets skipped.

This is called query fan out.

Instead of matching your page to one search term, the system splits a single question into several related sub-questions, then searches for the best passage to answer each one.

Your article isn't competing as a whole. Each paragraph is competing on its own.

That's why a paragraph depending on context from three sentences earlier is a liability now, not just weak writing.

If a retrieval system pulls that block out and drops it into an answer, it has to make sense in isolation, with the subject, the claim, and the reasoning all inside the same block.

The practical version of this is simple.

Keep each paragraph between 40 and 80 words, and give it exactly one idea. Not one idea plus a supporting example plus a caveat.

One idea, stated clearly enough that someone reading only that block, with zero surrounding context, still understands what you meant.

This helps regular Google rankings too, not only AI answers.

Google has pulled isolated passages into featured snippets and "people also ask" boxes for years, using the exact same logic: find the smallest chunk of text that fully answers the question, and rank it independent of the rest of the page.

It also happens to be better UX. Readers scanning on a phone don't parse dense blocks of connected reasoning, they grab one idea, decide if it's useful, and move on.

Writing for extraction and writing for a tired person skimming on the bus turn out to be almost the same skill.

None of this means write worse or dumb things down. It means every block has to earn its place on its own, because increasingly nobody reads the whole page before deciding to trust it, they read whichever paragraph got pulled out and shown to them first.

Anyone else restructuring old posts around this instead of just adding more keywords?

Everyone talks about whether your content gets cited by AI. Almost nobody talks about how unstable those citations actually are.

We ran the same 80 queries across 3 AI models every 6 hours for 14 straight days. Same queries, same phrasing, same time zones. 56 runs total per model. Then we compared citation-by-citation across every single run.

The volatility was wild.

The headline numbers:

• 54% of citations changed between consecutive runs (6-hour gap)
• Only 23% of sources appeared in every single run for a given query
• 31% of citations appeared in fewer than 3 of the 56 runs — essentially random noise
• The median source appeared in 34 out of 56 runs (~61% consistency)

So when someone says "we got cited by AI," the real question is: how often? Because a one-time citation might just be variance.

What made citations stable vs. volatile:

Sources that appeared consistently across runs shared a few traits. They had direct factual claims with specific numbers — not vague explanations. They were the only result on the page that directly answered the query in one extractable sentence. And they came from domains that ranked organically in the top 5 for that query on traditional search.

Sources that flickered in and out had the opposite profile. They had multi-paragraph answers where the core point was buried. They competed with 3-4 other pages making similar claims. And interestingly, they tended to be newer content — pages under 30 days old showed 2.1x more citation volatility than pages over 90 days old.

The pattern that surprised us most:

Citation instability wasn't random. It clustered. A source would get cited consistently for 3-4 days, then disappear for 2 days, then come back. This happened across all 3 models. When we cross-referenced with crawl logs (where available), the disappearance windows roughly aligned with recrawl intervals. The model wasn't changing its mind about relevance — it was working with a stale index.

What this means practically:

If you're tracking AI citations as a KPI, a single snapshot is meaningless. You need at least 5-7 data points across a week to know if you're genuinely being cited or just got lucky once. We've shifted our internal tracking to a "citation consistency rate" — what percentage of runs over a 7-day window include our source — and it's completely changed how we evaluate GEO performance.

The pages that maintained 80%+ consistency across all 56 runs? They all shared one thing: the answer was in the first 150 words, stated as a direct sentence with a number in it. Not in a list, not in a paragraph of context — just a clean, extractable statement.

From where I'm standing, citation stability is a way better signal than citation count. One stable citation across 50 runs is worth more than 10 one-hit wonders.

Curious — is anyone else tracking citation consistency over time instead of just counting mentions?

I’ve been working on GEO for a while now, and I’ve noticed a pretty common problem. A lot of business owners have totally unrealistic expectations for GEO—they think it alone can fix all their marketing struggles.

Personally, I don’t think GEO should be a top priority in brand marketing at all. It’s just one tactical marketing tool, nothing more. For most businesses out there, AI is actually the better starting point to rebuild their brand positioning first. Once they’ve got their brand direction sorted out, then moving into GEO and other specific marketing efforts actually makes sense.

This is probably a silly question but I am curious about the composition of this subreddit: Are the members primarily business owners focusing on brand growth, marketing professionals with SEO experience, or students ?

I spent 5+ years in content and SEO. worked across B2B and B2C SaaS. I know how to structure content, I know how search works, I've written for enough brands to know what "good" looks like.

So when AEO and GEO is getting the hype, I spent the last two weeks testing whatever tool I could find, at least 20 of them.

and here's the thing:

every single tool gives me a different visibility score for the same brand. Not slightly different. Wildly different. :D

One tool says 67, another says 23, another says I'm not being cited at all.

I don't know what they're measuring. I don't know how they're generating the questions they send to LLMs. I don't know if they're running one query or ten. I don't know which models they're actually hitting.

and if I don't know that, how am I supposed to trust the number?

if you've been following me here, you'll know I've been playing with AEO/GEO for a while. Every Redditor, like me, found inconsistent scores, no transparency on methodology, no actionable output. Just a number that means nothing.

At this point I'm genuinely wondering: Should I just build the tool myself?

I know what I want from it. I know what questions matter. I know what actionable output looks like for a content team and after reading this community's feedback for weeks, I think a lot of you know exactly what's missing too.

What's the one thing you hate most about the AI visibility tools you've tried?

From my experience, the hardest part of GEO isn't knowing what to do — it's knowing what to cut. We spent months adding content, adding sections, adding context. Citation rate barely moved. Then we started removing things, and everything changed.

Here's the methodology we landed on after testing it across 40 pages over 10 weeks.

**Step 1: Identify your "citation paragraphs"**

Pull the last 50 times AI models cited your page. For each citation, note which paragraph the model actually pulled from. In our case, 73% of citations came from the same 2-3 paragraphs per page. Everything else was dead weight — not hurting, but not helping.

**Step 2: Isolate the core answer block**

Take those citation paragraphs and consolidate them into one continuous block near the top of the page. Not buried in an H2 six scrolls down — right up front where a model (or a human) encounters it within the first few sentences. We call this the "answer surface."

**Step 3: Cut supporting fluff aggressively**

This is where it hurts. We removed:

• Historical context sections (readers can Google it)
• Methodology explanations (moved to appendices)
• Related topic tangents (each became its own dedicated page)
• Transition paragraphs between sections
• "Why this matters" framing paragraphs

What stayed: original data, specific claims with numbers, direct answer statements, and one supporting example per claim.

**Step 4: Density check**

After trimming, we ran each page through a simple test: can a reader extract the core claim in under 15 seconds? If not, the answer surface isn't tight enough. Cut more.

**The results across 40 pages:**

Average word count dropped from ~1,800 to ~440. Citation rate increased by 62% within 3 weeks. Citation stability (how long a citation persists before disappearing) improved by about 40%. And somewhat unexpectedly, traditional search traffic held steady — it didn't drop despite removing 75% of the content.

**Why I think this works:**

AI models have a context window. They're scanning your page for the most relevant, extractable passage. When 1,800 words compete for attention, the model has to parse through irrelevant context that dilutes the signal. When 400 words are tightly focused on answering one question, the citation-worthy passage is easier to identify and extract.

The way I see it, most GEO content suffers from SEO hangover — we're still writing for crawl depth and keyword coverage when we should be writing for extraction clarity.

One important caveat: this framework works for informational and definitional content. We didn't test it on comparison or transactional pages, where longer context might still matter. Your mileage may vary there.

Curious — has anyone else experimented with aggressive content trimming for AI visibility? Would love to compare notes on what you kept vs. what you cut.

I work in PR and am interested in measuring the impact of earned media on LLM visibility and the relevant metrics and insights for PR.

Suggestions I’ve seen include measuring your performance for relevant prompts before and after a specific PR hit/campaign/announcement and measuring the same set of prompts over time.

A lot of GEO tools and content I see is set up for digital marketing and website content strategy that isn’t directly relevant for PR. And the fact that earned media is just one potential influence on LLM visibility complicates measuring overall visibility that is also influenced by website content and non-PR activities.

I think the suggestion to measure website referral traffic makes sense.

I’m still new to this so please forgive any ignorance.

Is it because the AI visibility didn't fetch the data properly?

or was the website's content was not good?

What do you guys think of AI visibilty tool and its credibility?

Off late, I am seeing a lot of AI visibility tools everywhere, sometimes there's a launch on Product hunt every day. And even I thought to build an AI visibility tool if there's so much market for it.

Will there be so much demand for AEO tools after a year or two?

for the people using AI visibility/GEO tools today
which one are u using? and if u could change one thing about it, what would it be?

feels like every tool has visibility scores and prompt tracking now, so i’m curious what’s still missing

Been thinking about what actually makes brands act on AI visibility, and it’s never the number. Nobody moves because they got a 58.

What moves people is seeing the actual answer: “when someone asks for the best [your category], the model recommends your competitor by name.” That’s not a metric, that’s a receipt.

Which makes me think the whole space is framing this wrong. We keep talking about scores and rank-style tracking, but the unit that matters is the query-level outcome: who got named, who got skipped, and what sources the model leaned on to decide.

Curious if others see the same thing — do clients/teams react to scores, or only when they see the raw answers?

Here's something I don't see talked about enough: getting cited by AI and actually getting traffic from those citations are two completely different games.

We spent the first 4 months of our GEO work chasing citation counts. Every week we'd celebrate hitting new highs — more brands mentioned, more pages referenced, more "visibility." Looked great on paper.

Then someone on the team pulled the actual referral data.

**73% of the pages that got cited by at least one AI engine sent zero measurable traffic over a 90-day window.** Not low traffic — literally zero. These weren't obscure long-tail queries either. Some had 15+ citations across different models.

So we dug into why. Three patterns kept showing up:

**1. The "encyclopedia entry" problem.** A lot of our citations were factual confirmations — AI pulling a stat or definition from our page to support a larger answer. The user never needed to click because they already got what they wanted. We were functioning as a fact-checking layer, not a destination.

**2. Citation position matters way more than citation presence.** Pages that consistently appeared as the first or second source in an AI response got 11x more clicks than ones that showed up third or later. Being "in the mix" means almost nothing if you're not in the top slots.

**3. Informational queries have a ceiling.** "What is X" and "How does Y work" type questions rarely drive clicks regardless of how well you're cited. The user's curiosity is satisfied by the AI summary itself. Comparison, pricing, and "best X for Y" queries? Those still send traffic because the AI answer is inherently incomplete.

The shift we made: we stopped tracking raw citation volume as our north star metric. Instead we started measuring **citation-to-click rate** — what percentage of our citations actually resulted in a visit. The number dropped (obviously), but the ones that mattered became way clearer.

I'm not saying citations are worthless. Brand visibility has its own value, especially for awareness-stage stuff. But if you're building a GEO strategy around citation counts alone, you might be optimizing for vanity metrics while the actual traffic goes somewhere else.

Curious if anyone else has split their citation data this way. Did you find the same gap?

Saw the Claude in Chrome GEO audit prompt going around last week. Good starting point — the raw HTML vs rendered DOM separation is smart. But after months of running GEO audits, I've found 4 dimensions HTML-only audits systematically miss.

I call it the SEEC framework — Sources, Entities, Evidence, Consistency.

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1. Sources — beyond robots.txt

Most audits check robots.txt for GPTBot / ClaudeBot / PerplexityBot. That's necessary but not sufficient. In 2026 you should also check:

• llms.txt at root — still emerging but some AI agents fetch it when present. Curated table of contents for LLMs.
• RSS / Atom feeds — many AI crawlers still discover fresh content via feeds.
• Sitemap freshness — <lastmod> accuracy matters more than sitemap existence.

Quick test: curl -A "GPTBot" https://yoursite.com/llms.txt. If you don't have one, your competitors probably don't either. Window of opportunity.

2. Entities — the Wikidata / Wikipedia signal

LLMs anchor facts to entities. Brands with a stable Wikidata QID + Wikipedia article get cited disproportionately more in ChatGPT / Perplexity / Gemini answers.

Audit checklist:

• Does the brand have a Wikidata QID?
• Is it referenced via sameAs in the Organization JSON-LD?
• Are Wikipedia mentions consistent with the schema data?
• Does the founder / CEO have their own Person entity, also referenced?

3. Evidence — citability of your content

The Claude prompt checks for "stats, dates, sources, author attribution". Right idea, but granularity is off. What actually matters:

• Specific numbers with sources — "45% of…" beats "many companies…"
• Dated statements — "As of Q1 2026" beats undated claims
• Named authors with credentials — with matching Person schema
• Direct quotes with attribution — LLMs love these

Rule of thumb: if a paragraph can't be cited standalone in an AI answer, it won't be.

4. Consistency — the cross-source check

The one nobody audits. LLMs cross-reference facts across sources. If your Google Business Profile says one thing, Wikipedia another, your schema a third — LLMs don't cite conflicting entities.

Manual check: pick 5 facts from your homepage (founding year, HQ location, employee count, main service, key metric). Search each on Google, Perplexity, ChatGPT. Do all sources agree? If not, that's your GEO fix list.

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What I don't audit (and neither should you, probably)

• Word count — irrelevant for GEO, concise beats bloated
• Keyword density — dead concept
• Site speed specifically for GEO — CWV still matter for classic SEO, much less for AI answer inclusion

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Curious what signals others are tracking. What did I miss?

I have been trying to understand AI visibility tools a little better. Most of them appear to work with a set of questions, either written by the customer or generated from keywords, topics, brands, competitors, and related terms. That seems reasonable at first. But it made me wonder about a few things.

If a customer provides the keywords, themes, and brands that matter to them, how neutral can the resulting question set really be?

If the system generates questions from those inputs, is it discovering what people naturally ask, or is it creating questions inside the semantic space the customer already wants to be associated with?

And if the customer writes the questions directly, would they not usually formulate them in a way that makes the desired brand, product, or wording more likely to appear in the answer?

For example, there seems to be a big difference between asking:

>“How can I improve my email marketing?”

and asking:

>“How can Klaviyo improve my email marketing?”

Both may be valid questions. But in the second one, Klaviyo is already part of the framing before the AI has answered anything. The same applies more indirectly when questions are generated from a brand name, product category, target keywords, competitors, or desired associations.

So when a report says that a brand appeared in 18 out of 25 AI answers, what do those 25 questions represent?

Are they a realistic sample of what people would independently ask?

Are they based on actual user demand?

Are they generated from the customer's desired associations?

Or are they simply the questions most likely to make a specific brand or product relevant?

I am not saying that this makes AI visibility meaningless. It may still be useful to measure whether a model connects a brand with a topic, whether it recognises a product category, or whether it mentions a company once a user has already framed the problem in a certain way.

But should all of that be interpreted as the same thing?

At what point are we measuring independent AI visibility, and at what point are we measuring the effect of the questions we chose before the AI ever answered?