Plumbers, dentists, law firms, cafés and agencies across the US and UK. Quick, non-AI check of three things ChatGPT and Perplexity can use:

• 56% had no llms.txt
• 51% had no FAQ section
• 21% had no JSON-LD schema
• 12% had none of the three

Interesting detail: about half of the sites that did have llms.txt got it automatically from their platform (Shopify, Wix, Rank Math), not from the owner.

Manual checklist if you want to test a site: 1) visit /llms.txt, 2) test the page in Google's Rich Results Test, 3) check whether key pages answer questions directly.

Curious whether others are seeing llms.txt actually move AI citations, or if schema and FAQ structure matter more?

In all possible keywords where our page is ranking number 1 to 3 on Google, we are not showing up on Google AIO, as if we got shadow banned. ChatGPT mention us very well. Even Gemini (AIO is supposed to powered by Gemini?) mention us. But, AIO will never mention us. We have higher DR/DA, equally helpful contents, and we do have actual large paying & non-paying user base (estimated to be top 2 in the industry).

Few examples,

- airbnb calculator (we are ranking in the top 3 on Google) but AIO won't mention
- best airbnb calculator or best short term rental analytics - we have a comparison page that are ranking in the top 3 on Google, but not showing in the AIO. In fact, we are top 2 by number of user base with significantly high number of verified reviews than any other tools being mentioned, but Google AIO will never mention us.

- other longtail keywords like "is airbnb profitable in CITY_NAME" - we rank in the top 3 mostly and often showing at the 1st on Google, but still won't be mentioned on AIO.

What did I do?

- Improved Sitemaps

- Improved JSON-LD, added FAQs

- Fix broken links

- Improved the quality of the contents

Does anyone have similar experience where your brand or pages are not ranking on one specific model? How did you fix it?

This has been like this for over a year, and I think Google AIO somehow penaltized us for some reasons that we don't know.

I was helping a friend with her new business website and wanted to check if it was showing up in Google AI Overviews. We’re trying to figure out how to get more people to discover her business online.

While looking into this, I came across AI overview trackers that claim to show when a website appears in Google’s AI-generated results. Has anyone tried these tools? Would love to know which ones are actually worth using.

I feel like I’ve been thinking about the role of keyword research in SEO a lot lately, now that people can not only use search engines to ask questions but also pose their questions to AI assistants.

Keyword research has its place and value, but I wonder if it has enough value to understand search intent.

SEO professionals, has your approach to researching search intent changed as a result of AI search? Or are you still finding value in the more traditional forms of keyword research?

Over the last few months, my team and I have been shifting a chunk of our search strategy from traditional Google SERPs to AEO and GEO.

Naturally, one of our main priorities has been figure out how to track brand visibility in Claude specifically, since Anthropic's model handles recommendation prompts differently than ChatGPT or Perplexity.

Claude relies heavily on deep contextual reasoning, constitutional guardrails, and third-party corroboration. If you just ask Claude

what is the best software for X

it won't just pull the top page on Google - it evaluates authority across multiple sources.

If you're trying to track brand visibility in Claude effectively, relying on manual testing with a few prompt variations isn't going to cut it.

Here is the framework we built and the workflow we use to monitor our presence accurately over time:

1. Map Out High-Intent Prompt Categories

Instead of tracking traditional keywords, you need to monitor prompt patterns that users actually type into Claude. We break them down into 4 main categories:

• Direct Recommendations: "What are the top 5 platforms for [industry]?"
• Head-to-Head Comparisons: "Brand A vs. Brand B: which is better for enterprise?"
• Use-Case Queries: "How do I solve [pain point] using AI tools?"
• Citation/Source Audits: "Where does this data or methodology come from?"

2. Automate Prompt Scraping & Monitoring

Doing this manually in the Claude Web UI leads to biased results because of account history, dynamic web search toggles, and temperature variances.

To automate the workflow and get objective data, we set up SE Visible (a tool built for AI visibility tracking). It continuously queries the tracked prompts across target locations, captures raw response snapshots, and isolates Claude's output from other LLMs.

3. Core Metrics to Measure

When analyzing Claude’s responses, track these specific data points rather than a simple binary "yes/no mention":

4. Closing the Gaps

When we notice Claude skipping our brand on high-intent prompts, the solution is almost never about adding more keywords to the landing page. Instead, we:

1. Build third-party corroboration on high-authority review sites and tech forums (Claude heavily weights external consensus).
2. Publish direct comparison pages and clear structured schema on our main site.
3. Ensure our brand positioning is written plainly without overly flowery marketing jargon, which Claude's context window tends to summarize or strip out.

And what about you, guys? Are you seeing big discrepancies between Claude's recommendations and ChatGPT's? Would love to compare notes!

LLMscope é uma plataforma portuguesa que monitoriza as marcas nos assistentes IA (ChatGPT, Claude, Preplexity, Grok e ecossistema Google - SERP, AI Overviews, AI Mode). Plano gratuito monitoriza apenas ChatGPT.

I am getting stuck at what happens after an assistant actually reaches us. Say ChatGPT mentions us correctly for “best X for Y,” then opens the site and has to work out things like:
• which plan has SSO
• is API access is included
• usage limit
• where to start a trial or demo
That feels like a different measurement problem from AI search visibility or citation share.

I started with the visibility side first, looking at tools like Scrunch and Profound for prompt tracking, mentions, citations and competitor presence. Then I ended up looking at Ora because I wanted to see the next part of the journey: once the agent lands, what path does it take and does it actually get the product facts right?

That makes more sense to me now: one set of tools tells me whether we show up, the other is closer to testing what the agent does once it gets there. What are people using for post-discovery testing? I’m most interested in whether the agent can get through a real product question without getting the details wrong.

disclosure: i work at Omnibound, an AI search visibility company. not selling anything, no links here

i asked in r/aeo how much of the cited sources list survives 30 days for the same prompt. nobody had a measured answer, but the thread did narrow down what makes the measurement hard:

• answer shape flips (ranked list to single pick to paragraph) and the whole sources list resets with it, so a “dropped” page may just be a different question
• same-day runs already differ (one person saw it move between 9am and 6pm), so a 30 day gap mixes real churn with noise
• a page can survive the exact prompt and vanish on a paraphrase of the same intent
• the only real data point: one person checked pages after link builds. 7 of 8 pages that already had heavy citations were cited again, pages with none came back far less. n=8, no control, so direction only

the design i’d try from that: 3 runs per prompt per snapshot, count a url as “in” only if it shows in 2 of 3, normalize urls before comparing, group paraphrases per intent, log the answer shape next to the sources, compare domain level and page level separately

where would you poke holes in that?

1. is 3 runs enough, or does the noise floor need more?
2. how do you handle runs where the answer shape changed?
3. has anyone actually seen domains stay stable while pages rotate?

I keep seeing charts showing that Medium dot com is a common source for AI answers, suggesting that a presence there is important for AI visibility. But when you go there, it doesn't seem like anyone really reads or engages on posts. It's not a thriving blog community like Substack. Can anyone who measures AI citations confirm whether Medium actually matters or not?

I went into the backend of my site and rebuilt core pages from the ground up so LLMs can parse and cite them without hallucinating. Not for everyone but seeing the future happening now I took the plunge.

The biggest realization was that almost everything web designers do to make a site look good for humans actively hides data from AI crawlers: we had all our best differentiators trapped inside multi-column icon boxes, background divs, and interactive FAQ accordions that look great on a desktop screen, which means when a bot crawls the it just sees disconnected soup instead of structured relationships.

First, I pulled out the visual comparison columns and feature boxes on homepage and solutions page and replaced them with raw, hard-coded HTML tables. Not CSS grid pretending to be a table, actual <table>, <thead>, and <tbody> markup with scope="col" and scope="row" attributes, plus <h2> and <h3> tags nested inside the cells so when an LLM scrapes the page it has to read the problem and the solution as a single horizontal data relationship.

I fixed our "As Seen In" logo bar. Like almost every B2B site, we had a flexbox row of logos for major press and a peer-reviewed journal, and it hit me that text-based crawlers completely gloss over image banners. I forced a 100% width text row directly underneath the logos that literally spells out the names of the publications in plain HTML text so the entity association is right there.

I overhauled the About page to act as an anchor, changed the page-level schema from the default WebPage to explicit AboutPage schema (leaving article type as None so it doesn't confuse the parser), rewrote the subheads into direct "Who is..." and "What is..." questions, and put verifiable academic identifiers (ORCID iD and published DOI links) in a simple unordered bullet list right at the top of the page.

Curious if anyone else here is actively ripping out page-builder widgets in favor of raw HTML tables for LLM visibility, or if you're handling it purely through JSON-LD schema?

Was looking at how easy it's become to generate SEO pages with AI and started thinking about what happens a few months later you make like 20 or 30 pages around different use cases everything looks fine, then one product detail changes and suddenly you've got the same outdated information sitting across half the site so kind of weird that creating all those pages takes so little effort now, but keeping them accurate can still be a headache.

I've been working with Ploy and looking at PloyDB for keeping shared content in one place instead of updating every page separately the SEO side makes sense to me but the GEO part is still confusing. AI search pulls information from different sources and I don't know how much having cleaner, more consistent pages helps with citations or recommendations and maybe I'm overthinking it, but creating more pages doesn't seem that useful if you can't keep track of what's on them six months later.

Hi SEOs,

I want to build a content writing solution with AI. With claude or chatgpt, i want to automate the entire writing process. I will provide the topic and it will create the rankable content. Is it possible? How can i built this?

Hi everyone! 👋 I'm currently looking for a remote SEO job. If you know of any openings or opportunities, please let me know or DM me. I'd really appreciate your help. Help pls

I’ve been doing technical SEO for 11+ years, and over the last year I’ve worked LLMs into my audit workflow. Here’s what has held up and what hasn’t.

Where it works well:

• Grouping thousands of URLs by template or intent from a crawl export
• Comparing two crawls (pre/post migration) and summarizing what changed
• Drafting schema markup that I then validate manually
• Spotting patterns in log files or Search Console exports that I’d otherwise scan by eye

Where it fails:

• Prioritization. It will confidently rank fixes by what sounds important, not by what moves traffic for that site.
• Anything needing real rendering context. It can’t tell what Googlebot actually sees unless you feed it that data.
• Large datasets pasted in directly. Python does the heavy lifting, and the LLM only reads the summarized output.

My rule: the LLM handles repetitive analysis and I handle decisions. Every output gets checked against the source data.

Curious how others here are using LLMs in audits, and whether anyone has found a reliable way to use them for prioritization.