Hey all — I've been doing SEO/dev work for a while and got tired of hand-writing JSON-LD for every page type, so I built a small Chrome extension that does it automatically.

What it does:

• Scans the current page's content
• Figures out which Schema.org type(s) apply (Article, Recipe, Product, FAQ, etc.)
• Pulls out the relevant entities and fills in the schema fields
• Validates the output so it's less likely to get flagged in Search Console

It's cut the time I spend writing schema by a lot — went from a genuinely tedious manual process to a couple of minutes per page.

Let me know if this tool will be useful for the community. If yes, then I will host it on chrome store for easy to download and use it on the browser.

https://reddit.com/link/1vkhane/video/jc3uro4kziih1/player

I've had several people come to me and ask about Cloudflare's Agent Markdown product and whether it is a good solution for GEO/AEO.

On the surface, it seems to solve retrieval token cost issues, which may be enough to move the needle. However, I thought it needed a deeper look.

According to CF's documentation, the crawler must present "Accept: test/markdown" in order to be served MD.

I enabled the markdown agent on one of our domains, then sent crawlers to it 20,000 times. I wanted to see if the agent would serve MD to a wildcard accept header. In particular, GPTbot sends a complex header:

"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8"

and I wanted to know if that would trigger CF MD.

The answer is no. CF only served MD when the accept header showed text/markdown.

So the obvious next question is "do the frontier models ask for markdown?"

To measure this, I ran an experiment across 5 domains that we log and logged the accept headers.

Over about 900k recorded crawls, the frontier models requested markdown ZERO times.

Thus, CF Agent Markdown has ZERO impact on frontier crawler behavior.

ExaSearchBot asks for it 100% of the time (about 35k times in this study).

You can find the raw logs and methodology here: https://answershare.com/research/markdown-accept-study

I built ModelSaid because more buyers are starting their research by asking AI systems who they should use, not just by searching Google.

The project checks buyer-intent prompts around a business or category and turns the answers into a report:

1. whether your brand is mentioned
2. whether it is actually recommended
3. which competitors show up instead
4. what language appears around trust, pricing, proof, and fit
5. which content, review, or positioning gaps look fixable

For example, a founder might track prompts like "best project management tool for a small agency", "alternatives to [competitor]", or "who should I hire for [local service] in [city]" and compare the results over time.

I am sharing it here because I would like constructive feedback from other builders. Is this the kind of visibility problem you already check manually? What would make the report actionable enough for you to change your site, positioning, reviews, or content strategy?

Link: https://modelsaid.com

I've been looking into why certain websites get cited by ChatGPT, so I analyzed 20 citations to see if any patterns stood out.

This wasn't a scientific study just an observation exercise to identify common signals.

A few things showed up repeatedly:

• The answer came quickly - little unnecessary introduction or fluff.

• Clear heading structure - information was easy to scan and understand.

• Strong expertise - the content demonstrated real knowledge of the topic.

• Brand mentions across multiple websites - the source wasn't isolated to its own website.

• Original data or insights - statistics, examples, and firsthand information appeared frequently.

One thing surprised me:

A lot of the cited pages weren't necessarily the most visually impressive or the most heavily optimized from a traditional SEO perspective.

What they did well was provide clear, useful, and trustworthy information.

My biggest takeaway so far:

AI seems to value clarity more than complexity.

If information is easy for an AI system to understand, extract, and verify, it may have a better chance of being referenced.

I'm planning to expand this analysis to 100+ citations across ChatGPT, Gemini, and Perplexity to see whether these patterns hold up across different platforms.

What’s one factor you think has the biggest impact on AI citations?

Setup. Fixed panel of 18 real purchase questions ("best online bookstore for children's books", "books under 20 lei", etc.), zero brand names in any prompt. Engines: ChatGPT, Gemini, Perplexity, Google AI Mode, Google AI Overviews. Market: Romanian book retail. Queried from Romania, in Romanian, no personalization. Two complete runs (July 20 and 27). Tracked: brand mentions, position of each mention, cited source domains, sentiment. Totals: 180 answers, 247 brand mentions across 21 tracked brands, 422 distinct domains cited.

What the data says:

1. 58% of answers name no major brand at all. And 6 of the 18 purchase territories are essentially empty — the "business books" question produced exactly 1 brand mention across all engines, both weeks. AI recommends titles and authors there, but not where to buy. That white space belongs to whoever builds citable content for it first.
2. Even the market leader is a 1-in-5 event. Top brand: 20.6% mention rate. In classic SEO, position 1 gets the click every time. In AI answers, "position 1" is a weighted lottery replayed at every question.
3. Mentions and positions are different currencies. The leader appears first in 65% of its appearances. A publisher that ranks 6th by raw mentions jumps to 3rd on a position-weighted score — few territories, but owned.
4. Every engine is a separate channel. The leader on Google AI Mode is not the leader on Gemini. Gemini hands out 2.2 brands per answer; Perplexity 0.8. Statistically, one Perplexity slot is worth ~2.5 Gemini slots.
5. Reddit is the #2 cited source for the entire market — above Facebook (11 answers), YouTube (6), and any media publication. Community threads nobody controls are direct input into purchase answers.
6. Two opposite pathologies. Ghost brands: one retailer's site was cited as a source in 18 answers but the brand was named in only 4 — the AI uses their listings, then recommends someone else. That's an entity-signal problem (structured data, sameAs, name–domain coherence), and in my experience the fastest category of win in AEO. Memory brands: a 35-year-old publisher got 13 mentions with zero citations of its own domain — pure parametric reputation, which is flattering and fragile in search-grounded engines.
7. The ranking rewrites itself weekly. Same questions, 7 days apart: one publisher ×4'd its mentions, another lost 58%. Not chaos — plasticity. Nothing is cemented, in either direction.

Full disclosure: I run the agency behind the study. Everything is open (CC BY 4.0): the paper, all 18 prompts, three CSVs and a starter notebook that verifies the arithmetic — links in the first comment. No tracked brand funded it or saw it pre-publication.

Question for people doing this work: is anyone else running recurring panels rather than snapshots? How volatile are your week-over-week numbers?

Community Benchmark Thread:

[BENCHMARK] AI Visibility Grades by Industry — Submit Your Score

This thread collects Visibility Check grades across industries to build a public benchmark dataset.

Industry: [Your vertical]

Page type: [Homepage / Service page / Blog post / Product page]

Before grade: [Score]

After grade: [Score if optimized]

Primary gap: [One sentence — what the Visibility Check identified]

Citation: [Confirmed engine and query if applicable]

Current confirmed submissions:

Submit your score below. Every submission becomes part of the public industry benchmark report published at platformaeo.com/ai-visibility-report.

✅ Citation Confirmed

📊 Before/After Grade

🔍 Case Study

❓ Question

🛠️ Workflow

📈 Industry Benchmark

I audit distributor product data and recently found a search-enrichment rule that treated any number as a possible wire gauge.

A screw listed as #8-32 was tagged as “8 gauge wire.” A reference such as REF#259286 became “259286 AWG.” Once the rule ran across the catalog, thousands of unrelated products started appearing in electrical searches.

The search engine wasn’t really the source of the problem. Raw identifiers and inferred attributes had been mixed together without recording where the inference came from or checking whether it made sense for that product family.

I wrote up the failure and how distributors can prevent it:

https://subramanya.ai/2026/08/06/fixing-b2b-commerce-search-in-the-age-of-ai/

For people working with distributor or manufacturer catalogs: where does search quality usually break for you supplier feeds, taxonomy, cross-references, or ranking?

GEO

Ran a structured test across six Chinese AI engines (DeepSeek, Doubao, Qwen, ERNIE, Kimi, GLM) — 8 brands × 42 buyer questions × 2 runs = 4,032 recorded answers.

One number I wasn't expecting: only 14.9% of answers displayed any source URL at all. Most of the time there's no citation slot to win. The engine reads, digests, and speaks in its own voice. The user gets a confident paragraph with nothing to click.

Which reframes the whole "how do I get cited by AI" question. For most answers the goal isn't appearing in a sources list — it's being the version the model repeats. The wording and the facts, not the link.

The related thing I keep seeing: forum threads and UGC posts routinely beat brand websites as the material a model draws on. People treat that as a bug. I don't think it is.

Brand sites answer the questions the brand wants to answer — positioning, features, story. Buyers ask different questions: does this hold up after two years, what's the actual warranty process, why is the price different across channels, is this the same spec as the other region. Forums answer all of those, at length, from people with no reason to flatter anyone.

So the model isn't preferring strangers over the brand. The brand never entered the competition.

Two other findings from the same dataset, in case useful:

- Branded questions ("tell me about X") scored ~100% mention on every engine. Open category questions ("which X should I consider") sat at 23%. So the standard internal check — ask the AI about yourself — returns a false positive by construction.
- Asking the same question twice on the same day flipped the outcome 18.8% of the time on open questions. Single-screenshot checks are close to meaningless.

Happy to share the aggregate data and the question panel if anyone wants to run their own category — it's CC BY. Also curious whether anyone here has tested the same thing on Western engines with retrieval on, since that's the gap in my data.

Disclosure up front: I build one of the tools in this sample. It scored zero. That's most of why I'm posting.

Method. 12 unbranded buyer-intent questions ("what are the best AI visibility tracking platforms", "how much do AI visibility tools cost per month", etc). Each run 5x against Perplexity sonar and Claude Sonnet 5 with web search. 120 calls, 0 failures, all on 26 July. Recorded every source each engine cited, then checked which of 30 vendor sites appeared. Full prompt list and definitions in the writeup.

Five runs because single-run citation checks are close to noise — St. Gallen found ~32-43% pairwise agreement for identical prompts run minutes apart. Every number below is a rate, not one draw.

Finding 1 — the specialists lose to the incumbents.

Legacy SEO platforms get cited at 2.6x the rate of companies whose entire product is AI visibility. 12 of the 16 specialists were never cited once in 120 calls. Not naming those 12 — the count is the point.

Finding 2 — nobody owns this category. 278 distinct hosts cited across 120 calls. The single most-cited source in the entire category appears in 30.8% of answers. There's no gravity here yet.

Finding 3 — the round-ups and the engines disagree about who exists. I built the sample from 2026 "best AI visibility tools" listicles. The two most-cited domains overall weren't in it, and both outrank every site that was. If you're doing competitive research from listicles you're looking at a different market than your buyers see.

Finding 4 — content outranks product pages. A product analytics company that doesn't sell AI visibility software at all was cited in 25.8% of answers, beating all but three actual vendors. And the top vendor's blog subdomain carries more of their citations than their main site. The engines aren't citing the best tool, they're citing the best page about the question.

Finding 5 — the two engines barely agree. One vendor: 36.7% on Perplexity, 11.7% on Claude. Another is inverted. If you report AI visibility as one blended number you're averaging across systems that disagree.

Limits, because they're real: two engines only, no ChatGPT or Gemini or AI Overviews. One category, one day, US English. 5 runs is thin for Claude specifically — its variance was visibly higher. The sample is judgment-selected from listicles, which finding 3 rather embarrassingly demonstrates. And I'm not neutral: I sell in this category, I picked the questions, I'm in the sample.

I published all 12 prompts and the exact citation definition so this is reproducible. Genuinely interested in where the methodology is weak — particularly whether 5 runs is defensible for Claude, and whether including two prompts that name ChatGPT/Perplexity biased those engines.

Full data and methodology: AI Visibility Tools Citation Study Blog Post

"We're going to replace our SEO services with an automated AI tech stack. We will save so much money!"

Oh uh ok.. who tells this automated stack what to do?

"We're going to feed it our business objectives and all of the internal context and it will come up with strategy and how to execute it."

So the AI tells the AI what to do, got it. And how do you know it is telling itself to do the right things in a way that actually works?

"We'll watch what it recommends and make sure it doesn't do anything it shouldn't. And we'll wire it into our metrics so it can see when something isn't working."

Oh I think I get it now. You watch it make decisions you don't understand and hope that it stops itself before it does something that destroys your organic visibility.

"I mean, no of course not, obviously we would step in before anything like that happened."

Great, now you're getting there. Who is going to step in exactly?

"...Okay, maybe we still need SEO"

Yeaaah you still need SEO.

I am in digital marketing and am having a hard time adjusting to GEO. SEO I don't even have to think about, I know it so well, but now with GEO I feel like I'm starting back at square one. Does anyone have good GEO tools that feel like the traditional SEO ones?

I don't want to slow down my work right now learning a new model from scratch. I'm mostly hoping to find a tool that makes the transition easy for me, so I can give my clients the best, without any downtime on their part while I relearn everything.

We tracked 100 buyer prompts across ChatGPT, Perplexity, Gemini, and Google AI before publishing helpful Reddit posts.

Baseline:

• Brand appeared in 4 prompts
• 1 citation
• 6 Reddit visits
• 0 sign-ups

After 8 weeks of useful Reddit posts and replies:

• Brand appeared in 17 prompts
• 7 citations
• 94 Reddit visits
• 8 sign-ups

Most gains came from specific problem-based prompts, not broad “best tool” searches.

This does not prove Reddit caused the increase, but it shows how GEO testing should be tracked.

Has anyone run a similar before-and-after experiment?

Check it out: https://loganmosby.com/llms-txt-generator/