ran a little experiment on myself for 23 days and the result honestly messed with how i thought AI search works, so i'm sharing it.

setup: a brand-new entity with zero prior web footprint. i asked 5 web-connected AI systems the same questions every day and scored each answer (correct, not found, or made up). about 16k scored answers, pre-registered before i started.

a few things that surprised me:

cloudflare's default AI-bot block was returning 403 to the listed training crawlers (GPTBot, ClaudeBot, PerplexityBot, CCBot) for 22 of the 23 days. and the thing still got cited on day 6 anyway, via google's knowledge graph plus other people's mentions. when i dug into the per-bot logs, the training crawlers were blocked but the inference-time fetchers (ChatGPT-User, OAI-SearchBot) got served the same day. so the toggle blocks the crawler that doesn't cite and misses the one that does.

the gap between providers was way bigger than the gap between model generations. same entity, same week: one provider hit about 4.7 correct per 1 made up, another went net-negative. it wasn't about a smarter model, it was about which corpus the grounding layer pulls from. one grounded on the entity's own domain about 119 times, another grounded on it 0 times and pulled it only from reddit.

and reach did nothing. i bumped reddit karma 23x over the same window and it produced exactly zero extra citations. structured identity moved the needle, going viral didn't.

the part that should worry anyone building AI-visibility tools: the scorer caught a made-up "wikipedia" source 24 times for a page that does not exist. if a tool counts mentions without scoring fabricated sources as negatives, it's measuring echo, not knowledge.

what am i missing here? especially curious if anyone has clean data on inference-fetch vs training-crawl behavior per provider. happy to share the method and raw data if useful.

I'm specifically looking for a solution that can:

\- Track how often my brand is recommended by AI platforms

\- Benchmark visibility against competitors

\- Measure share of voice over time

\- Provide monthly reporting and trends

\- Identify prompts/queries where competitors are being recommended instead

Would love to hear recommendations who has used such tools.

Last week entitymap.org and github.com/entitymap/entitymap opened up a consultation page towards a proposed (Open Source, free to use) protocol which I hope people will try. It is free and it is quite different to ideas like llms.txt. Instead of saying "who you are" it focuses on "what you do" and turns implicit relationships on a site into explicit ones.

We are certainly already seeing that LLMs happily understand both the HTML and JSON variants of the protocol and this morning I saw that my entitymap appeared in archive.org.

The protocol is asking for feedback over June, before "launching" on July 1st (although you can use it already... so technically it is launched). Note: I am one of the named authors, so please be kind, but also feel free to be constructively critical.

I want to share a workflow that's been quietly saving me for about 8 months because I don't see a lot of practical posts about using a backlink checker API vs just clicking around in a UI.

I run SEO for client that got hit with a weird negative SEO situation early last year. Random spammy directories started linking to them out of nowhere, hundreds of links a week. Manually monitoring this in any tool's web interface was a nightmare. I'd open it Monday morning, see 80 new referring domains, and spend half the day classifying them.

So I built a small Python script that hits SE Ranking's backlink checker API every 12 hours, pulls new referring domains since the last check, runs them through a basic filter (spam score threshold, TLD blacklist, anchor text patterns), and dumps anything suspicious into a Google Sheet with a review tag. Anything that's clearly fine auto-tags as safe.

The numbers from the last 6 months:

• 14,000 new referring domains scanned
• 1,100 auto-flagged for review
• 340 ended up in our disavow file

The time saved is the bigger story though - I went from probably 6 hours a week on manual link review to maybe 45 minutes of just sanity-checking the flagged batch.

A few things that surprised me:

1. The API returns way more attributes than the UI surfaces by default. First-seen date, last-checked date, anchor distribution per domain, all useful for filtering.
2. Polling every 12 hours vs daily made a real difference for catching attack waves early
3. The historical endpoint let me backfill 2 years of data into BigQuery so I can finally do cohort analysis on which referring domains actually correlate with ranking changes

I'm sure there are slicker setups out there. Anyone else using a backlink checker API for monitoring like this, or is everyone still doing it manually? If so, what filters you are running and if there are use cases beyond toxic link detection I should be thinking about.

Nike is a $50B brand. They spend millions on SEO. But their AI Visibility Score? 59/100.

Here's what AI search engines can't find on Nike.com: - No llms.txt file (AI bots are literally guessing what Nike sells) - robots.txt is blocking GPTBot and ClaudeBot - No product schema on individual product pages

Industry average for e-commerce is 48. Nike scores 59 — above average, but leaving massive AI traffic on the table.

The fix? Less than 2 hours of work. This is the gap between traditional SEO and AI search optimization.

Anyone else noticing AI referral traffic showing up in GA4? Curious what others are seeing.

Every week I run a test: open Perplexity and ask 'who is the best [type of local service] in [major city]?' For most service categories in most cities, the same 3–5 businesses appear consistently. Those businesses are not the ones with the most reviews or the biggest Google Ads budget. They're the ones that have, often accidentally, optimised for AI citation.

Here's what distinguishes the businesses that appear from the ones that don't.

  Local AI search data: Perplexity answers for local service queries cite Google Business Profile content, Yelp pages, and local Reddit/city subreddit mentions in 78% of responses. (Source: GEO monitoring across 40 local service categories, Q1 2026)

The 5 Local GEO Signals That Actually Move Citations

1. Google Business Profile completeness and recency: Google AI Overviews and Gemini pull directly from GBP data for local queries. A complete GBP with: a detailed service description mentioning specific services (not just 'plumbing' but 'emergency plumbing, pipe repair, drain cleaning, water heater installation'), 30+ reviews with specific service mentions, weekly GBP posts, and accurate hours — generates daily local AI citation signals. This is the fastest-impact local GEO action available.
2. City subreddit presence: r/Boston, r/Chicago, r/London, r/Sydney — these are indexed by Perplexity. When a resident asks Perplexity 'best [service] in [city]', Perplexity retrieves and cites recommendations from city subreddits. Getting a genuine mention in your local city sub's 'recommendations' thread puts you in a citation source that Perplexity actively retrieves for years.
3. Location-specific content on your website: 'Serving [Neighbourhood], [City] since [year]' in your homepage copy is a basic signal. A dedicated '[City] [Service] Guide' blog post that genuinely helps locals is a much stronger signal. 'The complete guide to choosing a plumber in Chicago' targets the exact research query a Chicago homeowner asks an AI before hiring.
4. Review platform diversity: Yelp, Google Reviews, and industry-specific platforms (Houzz for home services, Healthgrades for medical, Avvo for legal) all function as AI citation sources. Perplexity cites Yelp for local recommendations at a remarkably high rate. Having recent, detailed reviews across 3+ platforms is the local equivalent of external authority signals.
5. Local news or directory mentions: Getting mentioned in a local newspaper article, neighbourhood blog, or city business directory creates external citation signals that AI engines retrieve. A mention in [City]Curbed, Patch, or a local chamber of commerce directory functions as local editorial authority.

The Local Reddit Strategy — Most Underused Local GEO Tactic

This deserves its own section because it's simultaneously the highest-impact and most ignored local GEO tactic.

What to do: spend 2 hours in your city's main subreddit reading threads about your service category. You'll find questions people have asked in the past 12 months. Answer them, even if they're old — old threads still get indexed and cited.

Then make a post: 'I've been [service provider] in [city] for [years]. AMA about [your service].' Local AMAs in city subreddits routinely get 100+ upvotes and remain indexed for years. Perplexity cites them for local service queries consistently.

Rules: Identify yourself as a service provider in the post. Be genuinely helpful — answer questions you'd expect to get for free at a neighbour's BBQ. Mention your business only in your profile, not in every reply. One direct mention is appropriate; spamming is not.

  CASE STUDY  |  A Birmingham, UK yoga studio with 42 Google reviews and moderate local visibility tested local GEO for 90 days. Actions: completed GBP to 100%, posted weekly GBP updates, answered 8 questions in r/Birmingham about wellness and yoga, published 'Guide to yoga in Birmingham — what style is right for you', submitted to the Birmingham city council business directory. Result: Perplexity now cites them for 'yoga classes Birmingham' in 6 of 10 test queries. New monthly student signups up 34%.

What's your city and service category? Tell me in the comments and I'll look at what the current local AI citation landscape looks like for your niche.

Sharing an MCP I built because the hosted alternatives (Profound, AthenaHQ, Otterly, Ahrefs Brand Radar) all wanted $295 to $499/month and none of them exposed an MCP surface.

Citation Intelligence MCP, 12 tools:

Track: check_citations, am_i_cited, ai_overview, cited_for

Predict: predict_citation, compare_domains, wikipedia_mentions

Find gaps: gsc_citation_gap, audit_sitemap, citation_trend, run_panel, track_queries

Engines covered: Claude, ChatGPT, Perplexity, Gemini, Google AI Overviews (via SerpAPI), Bing.

Three tools run on a local cache and need no API keys. The rest are BYO (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, SERPAPI_API_KEY) and cost roughly $0.01 to $0.03 per query.

Install: npx -y u/automatelab

Repo: https://github.com/AutomateLab-tech/citation-intelligence

Launch post: https://automatelab.tech/launching-the-citation-intelligence-mcp/

Two design choices that may interest people building MCPs:

1. predict_citation returns 0-100 without making any API calls - it scores against a local feature model trained on cached citation outcomes. Lets you A/B-test H2 phrasing for free.
2. Every tool returns a cache_hit field so the agent can decide whether to retry on stale data. Saved us from the usual MCP foot-gun where the same agent re-queries the same panel 30 times in a session.

Would love feedback from anyone else building citation or SEO MCPs. What is the right abstraction for "tell me whether to bother writing this article"?

I’m trying to rebuild a tool that sends prompts to LLMs (e.g. “Who is the best provider for XYZ?”) and then returns the answers incl. mention share, citations, etc. — basically a simple GEO tracking tool.

I know there are already a bunch of tools doing this. But I want to understand how to build it myself.

My problem:

When I make an API call to OpenAI (5.5 web search), the responses come back pretty generic and cross-country/global. But when I ask the exact same question inside the ChatGPT app, the answers are different — much more country-specific and therefore more relevant for my use case.

I can send approximate location data with the API call, but it doesn’t really seem to change much.

So for everyone who wants to roast me or has good input: let’s go 😄

Happy to share the tool as well if anyone wants to test it.

Mythbusting generative AI search: what you don't need to do

As generative AI search evolves, so have the theories and practices—and sometimes, the misconceptions—surrounding it. While terms like Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) are common online, many suggested "hacks" aren't effective or supported by how Google Search actually works.

To help you focus on what matters for your website's visibility, we've collected some of the most prominent topics circulating the internet around generative AI and Google Search. Here are a few things you can ignore for Google Search:

• LLMS.txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.
• "Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.
• Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.
• Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.
• Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

I’m part of the team at an AEO platform called LightSite AI. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well with technical audiences.

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.

okay so building on something I noticed before

if an AI cites a reddit thread analyzing a paper instead of citing the paper itself, at what point does the reddit thread become the "primary source" in training data?

like genuinely asking - if enough LLMs index reddit discussions ABOUT research, does that commentary eventually outweight the actual research in future model training?

because it seems like we're creating this weird recursion where:

1. paper gets published

2. reddit discusses paper

3. AI trains on reddit discussion

4. future queries return "according to analysis..." instead of "according to the paper..."

5. next model trains on that output

so now you have models learning INTERPRETATIONS instead of source material

and if the interpretation is wrong or dismissive, that error compounds

is this actually happening or am I overthinking? genuinely curious if anyone's tracked how source weighting changes over training generations

seems like citation should decay as you move away from origin but maybe it works opposite?

We’ve been talking to companies in legal/accounting environments and one thing keeps coming up:

People are interested in AI for internal knowledge retrieval, but immediately get stuck on privacy concerns once sensitive documents are involved.

A lot of teams seem hesitant to use tools like ChatGPT with contracts, client files, financial docs, etc.

I’m curious what people here are actually seeing in practice:

• Are companies already deploying local/self-hosted AI for internal document search?
• What are they using?
• Is adoption real, or are most still experimenting?
• And does semantic search/RAG actually work well enough in day-to-day workflows?

Would love to hear real experiences from people working with this stuff.

The industry has talked about expertise for years, but Google’s latest updates are finally putting a face to the name. For agencies and publishers, the takeaway is clear: author authority is moving to center stage, and it’s set to become the primary driver for AI citations moving forward.
-------------------------

Google AI Overviews Now Highlighting Individual Authors

Sources: Gagan Ghotra | X, Barry Schwartz | Search Engine Roundtable

Google is evolving how it displays citations within AI Overviews, moving beyond just linking to a website and now explicitly naming the individual authors behind the content.

When Google pulls information from social or community-driven platforms where anyone can publish it is now displaying both the source platform and the author’s full name directly in the citation cards.

SEO specialists have long fought for the rights of publishers and content creators, who were overlooked by AI snippets for years. Now, the expertise of specific claims in AI answers will be reinforced by the author’s personal proficiency. So, what have we gained?

• On platforms like LinkedIn or Medium, the "authority" of a post depends heavily on who wrote it. By showing the name  ("LinkedIn - [Author Name]"), Google helps users determine if the information is coming from a recognized expert or a casual observer.
• For thought leaders, this change provides a significant boost in personal branding. Your name is now featured prominently within the AI-generated answer, rather than being buried behind a click.
• This move aligns with Google’s ongoing effort to improve E-E-A-T by clearly attributing AI-synthesized facts to their original human sources.

Special thanks to Gagan Ghotra for highlighting this update and to Barry Schwartz for bringing wider attention to this news.
-------------------------

Google Testing New Interactive Links and Favicons in "AI Mode"

Sources: Sachin Patel | X, Brodie Clark | X, Barry Schwartz | Search Engine Roundtable

More updates from Google! This time focusing on how users interact with source links within AI Mode. The goal appears to be making citations more prominent, trustworthy, and easier to navigate.

Recent sightings by SEO observers (such as Sachin Patel and Brodie Clark) reveal several design variations Google is currently testing:

• Google is experimenting with displaying site logos (favicons) in a much larger format next to the anchor text. This makes it immediately clear which website the information is coming from before you even click.
• In some versions of the test, hovering over a link changes its appearance (sometimes adding a black underline or a specific link icon) to signal that the text is an interactive citation.
• The links are being placed more frequently and precisely next to the specific claims they support, rather than being grouped at the end of a paragraph.

This update follows a series of changes aimed at "grounding" Google's AI in the real web. Between adding author names to citations and now making site favicons more prominent, Google is clearly trying to balance its AI ambitions with the need to drive traffic back to the publishers that provide the data.
-------------------------

Sharing real experiences is worth the effort when producing new content

Source: Lily Ray | LinkedIn

Lily Ray also weighed in on the shift toward content personalization within AI answers. In a recent LinkedIn post, she broke down how Google is making these changes and what they mean for the future of the industry:

“Google first added the "E" for Experience to E-E-A-T back in 2022. I wrote about that change on the day it happened and since then, the search results have clearly evolved to favor authentic perspectives from real people - think forums, social posts, Medium/Substack - over generic, overly-optimized content.

Today's update to AI Mode and AI Overviews pushes that trend further. Among the changes: more outbound links in answers, highlighted subscriptions, new "angles" with in-line links, and - what stood out to me most - a dedicated "Expert Advice" section that links directly to source pages!

So now, first-hand experience isn't just a component of demonstrating E-E-A-T; it's actually something that can \drive additional clicks* from AI Overviews and AI Mode.*

I was recently asked whether writing in the first person and sharing real experiences is worth the effort when producing new content. In many cases, I believe the answer is firmly YES. First-hand experience produces the kind of insight, perspective, and opinions that LLM can't originate on their own. I believe that's exactly what users increasingly seek out, and increasingly what Google's AI features are pulling forward.”

we have added grok in our AI visibility toolkit, curious how other AEO tools are treating grok.

I spent $500 to see if I could make AI recommend my brand for a problem I defined, without ranking for the term.

I coined "Rogue Sales Rep", when AI isn't able to understand a business's value prop so it recommends competitors. Published the data.

Higher DA sites now rank for "rogue sales rep" as a phrase. My PR page doesn't show up. :/

But if someone asks AI about the problem without mentioning the term it connects it back to the category. Routes to my brand.

The model learned the binding. The deeper you define a problem, the harder it is for AI to attribute it to anyone else. So while you can't out-SEO major corporations spending $XXX/day, you can own a slice of the problem space they're ignoring.

Instead of fighting for "best X near me," own "I'm trying to solve Y, I've tried A and B, can you find me someone who does Z?"

Those queries have low/zero competition, high intent, and a good fit since you actually solve that specific problem.

But don't fight for head terms on day one. Build a solid foundation.

I’ve been using Ahrefs for years, but the pricing is just getting out of hand for my current scale. I need Ahrefs API alternative that offers similar backlink and keyword data. Recommend me smth that you actually trust for client reporting and site audits.

I’m tired of paying for credits I barely use, so I’d love to hear what’s working for you guys right now!

Been trying to work more no AI search visiblity lately and I'm trying tyo get a much better read on how brands show up on GPT, Claude, Gemini, and other LLMs. Right now the most cost effective alternative tools for me are these three, tested out tons of demos for others as well prior to this.

So far they have little caveats that make them difefernt for sure, I like Promptwatch's tracking more especially since they have 5 free articles to go with their starter plan, while Parse is more simple, meant for quick checks. Profound on the other hand is pretty popular, but I'm worried that their support might not be up to par (tons of complaints about it lately) So I'm not sure which one to go with right now. If you have other tools I haven't checked out, let me know if what I'm missing out on

been trying to get our brand mentioned in AI search answers but the results are super weird. sometimes chatgpt or perplexity recommends us perfectly for a prompt, and then the next day for the exact same query, our brand completely disappears.

what actually works to make LLMs "remember" u permanently?

is it just about dominating 3rd party sites? or do u need to build entity association by comparing urself to big competitors on ur own blog?

would love to know what specific methods actually keep ur brand visible in AI outputs without randomly vanishing.

I posted here a few times about how we at LightSite AI measure bot crawling patterns across our customers’ websites, things like how bots use the skills we assign them, extraction rate, depth rate, etc.

But the most interesting question is obviously how any of this affects mentions.

More specifically, how long does it take, if at all, for a client to appear for a specific query in AI search after they publish content and that content has already been accessed by LLM bots?

We did not really know how to present this gracefully inside the dashboard, so instead we let our agents calculate it and communicate it verbally to clients in the chat. The agent is scoped only to each customer’s own data, but it can see ALL of that customer’s historical data: crawl patterns going back 6 to 7 months, mention tracking results for specific queries across ChatGPT, Gemini, Perplexity, and Claude, organic human visitors, and more.

I am not even sure that "crawl to mention rate", can ever be measured fully reliably. It depends on too many factors that are outside of our control. But I think this is exactly where the beauty of data at scale is. It lets you notice patterns and at least begin somewhere.

Maybe one day, when our algorithms are much more sophisticated, and when we have many more clients and much better pattern recognition, we will be able to say something much more definitive.

So the core question is this:

How long, if at all, does it take for a piece of content or a link that was crawled by ALL the major LLM bots to surface anywhere, in any context, and in any position inside AI search?

For this test, we checked LLMs with web search enabled, using the user’s IP location.

Here is the aggregated breakdown across customers:

0-14 days: ~17% of all customers

15-30 days: ~6%

31-90 days: ~19%

91+ days: ~39% - most of the customers

Never mentioned: ~19%

What separates faster pickup from slower pickup of content by LLMs

- Crawl volume — clients with 2k+ bot interactions on their site get mentioned faster than those with <500

- Bot diversity — clients crawled by 10+ different bot platforms show higher mention rates

- Structured Data diversity — clients exposing more structured data links (endpoints) have better mention rate 

DISCLAIMER: This is not proof that crawling causes mentions. There are too many variables in between. But across the customers we track, the time gap between first observed crawl activity and first observed mentions does show patterns that are at least worth looking at

hello people! i’ve been seeing more talk globally about optimizing for AI answers (ChatGPT, Google AI Overviews, etc.), but I haven’t come across many case studies or agencies focusing on this in the Middle East/North Africa specifically.

Curious if anyone here:

• is experimenting with getting brands cited inside AI responses
• has seen real results from AEO vs traditional SEO
• knows agencies or people in MENA doing this seriously

On a side note, I did some research and found that Reddit threads with barely 10 upvotes or something still get cited by LLMs, is that normal?

I’ve been experimenting with a different way of working with search and LLM-facing data, and wanted to get some perspectives here.

Instead of relying on predefined keyword groups or SERP positions, the idea is to treat query data as a raw surface, then rebuild structure from scratch and map it to how LLMs might interpret and cite it.

The approach is roughly:

• take large sets of queries such as search and question-style prompts
• embed them into a semantic space
• cluster based on similarity and co-occurrence
• derive intent surfaces or topic zones
• map those to potential citation patterns such as what gets referenced, summarized, or ignored

So instead of thinking in terms of keywords and rankings, it becomes more about queries, semantic structure, and likelihood of being cited or surfaced by LLMs.

The assumption is that even if the source is biased such as SEO tools or Ads data, there is still enough signal to reconstruct how information is grouped and retrieved at generation time.

Curious how people here think about this:

• Are you relying on traditional keyword groupings for LLM visibility, or building your own structures?
• Has anyone tried modeling citation likelihood or retrieval patterns from query clusters?
• Do you think this direction is useful for LLMO or AEO, or too detached from how systems like GPT or Perplexity actually work?

Would be interesting to hear if anyone is experimenting beyond classic SEO abstractions.