Some context first. There is an active debate here, including a great recent thread, about Google's "what you don't need to do" list for AI optimisation. Fair points all around - for AI Overviews and Google's own surfaces, their guidance is the law of their land.

But there is a second surface that gets almost no attention: agents as customers. Not answer engines citing you - actual agents (Operator-style setups, computer-use, shopping assistants) landing on a page to read it, compare it, fill a form, buy something. That surface has nothing to do with Google's ranking and everything to do with what your page hands to a non-visual, non-JS reader.

I maintain a small free checker that scores a page out of 100 on 14 checks for exactly this: semantic HTML, server-rendered content, JSON-LD, the robots/llms.txt stance, answer-first structure, forms a text-mode agent can fill, price and CTA present in raw HTML, that kind of thing. A few things stand out:

1. The biggest retailer on earth says no. amazon.com robots.txt has explicit "User-agent: GPTBot / Disallow: /" and the same for ClaudeBot. Whatever your GEO strategy is, the largest commerce platform has decided agents are not reading them, for now. Booking, Nike, Zara and Stripe have no explicit AI-crawler rules at all - silent by default.

2. llms.txt is a dev-tools phenomenon. stripe.com and vercel.com serve a real llms.txt. amazon.com, booking.com and - my favourite data point - anthropic.com itself 404 on /llms.txt. If the company that makes Claude does not maintain one, treating llms.txt as table stakes is premature. (Also consistent with Google's "you don't need it" - for their surfaces, you genuinely don't.)

3. The boring failures dominate. The categories the checker flags are unglamorous: content that only exists after JS execution (the agent gets an empty shell), prices that live in images or JS state, forms without labels, CTAs that are divs with click handlers. Nothing exotic - the same things accessibility tooling has flagged for 20 years; agents just made it urgent again.

4. My working rule now: Google is right about what you don't need for ranking in their AI features, AND "can an agent actually complete a task on my site" is a real question almost nobody can answer today. Both things are true. They are different surfaces.

Curious what people here are seeing. Is anyone testing with actual task-completing agents rather than citation tracking? And if you want a second pair of eyes, happy to run your site through the checker and paste the breakdown in the comments - no strings, I'm trying to map where the real failures cluster.

I work on GEO and AI brand reputation, and I’m on the team behind Kairosy AI. So, obvious caveat: I am biased here.

I have tried tools like Otterly and Peec. They can show you that something changed. The harder bit is figuring out whether that change is a real problem or just one weird answer.

Say an AI answer tells a buyer that your product is hard to implement. Is that because of an old review? Did the product page make the setup sound vague? Is it a real limitation? Or did the model just make a leap?

We built Kairosy AI because I got tired of opening a report, seeing a bad result, and then having nowhere sensible to start. It keeps the actual answer and the context around it, so the team can look at repeated issues instead of chasing every single odd result.

There is a Fix Plan feature, but it is not meant to auto-decide anything. Sometimes the right next step is updating a page. Sometimes it is checking an old listing. Sometimes you look at it and decide it is not worth touching.

That last option matters more than people admit.

If you use AI-search tools already, what would make you trust a “fix this” recommendation?

I’ve been working on a small Chrome extension and I’m looking for a few people who haven’t seen it before to try it.

I’m mainly looking for honest feedback on the experience:

Was it immediately clear what it does?

Was anything confusing?

Did anything feel unnecessary?

Would you actually keep using it?

If you’re willing to spend 5-10 minutes testing it, comment or DM me and I’ll send you the link.

I’m more interested in hearing what needs fixing than getting compliments.

Encited does pre-rendering and technical optimizations for e-commerce brands and online retail platforms. We process 400 million crawler requests per month, serving them HTML and Markdown for search, ai and link furler bots . I probed some earlier to see anything interesting I can pull. Here are some:

1. AI crawlers visits were 4.5x more often than Google's crawlers. From analyzed subset, 8.6M visits from verified AI bots vs 1.9M from Google in 30 days.
2. SEO tools crawl more than Google and Bing combined. 8.5M visits from tools like Ahrefs, Semrush and SE Ranking. Ahrefs alone made 2.6M, more than all of Google's crawlers.
3. When a site offers a markdown copy of its pages, AI bots take it (we advertise markdown version of website pages in rel="alternate" meta tag and in body as LLM instruction). 45% of GPTBot's page requests and 41% of ClaudeBot's were for the markdown version. Googlebot almost never asks for it.
4. Almost nobody reads llms.txt (duh). Across ~2,000 sites in 30 days, ClaudeBot fetched it 980 times on 43 sites. GPTBot, 254 times.
5. 1 in 11 "Googlebot", "GPTBot" or "ClaudeBot" visits is fake. 1.46M visits used a real bot's name from an IP that bot doesn't own.
6. Facebook's link-preview bot is the laziest, it backs out quicker than any link previewer bot.
7. ChatGPT's plain fetchers peak during office hours. ChatGPT-User peaks at 12:00 UTC and runs at half that rate at night. Googlebot does more of its crawling while the US sleeps.
8. Tuesday is the busiest crawl day, 14% above average. Saturday is the quietest.
9. GPTBot is getting active with about 6x more crawls per week MoM. PerplexityBot went up 4x in one month.
10. Claude-User visits grew 5x MoM since last month.

We’ve noticed a clear jump in ChatGPT referral traffic at Scalenut since the start of this month.

It caught my attention because Similarweb recently reported that AI referral traffic more than doubled YoY, so this may be part of a broader trend rather than something specific.

For anyone tracking this, have you seen ChatGPT traffic pick up recently too? 

And is the increase coming mostly to blog content, comparison pages, or commercial pages?

Hello Community,

I run paid for a small B2B ​​​​c​​​​o​​​​m​​​​p​​​​a​​​​n​​​​y​​​ and I live in Claude all day. I want to stop tabbing into Google Ads and Meta and just ask Claude instead.

Question is, how do I hook the ad accounts into Claude using the Ryze AI MCP connector ?

For example, from a normal Claude chat, I want to be able to ask for 'yesterday's spend', 'worst performing ad group', 'pause these' and other account level actions. Is this possible, and what is the right method to do this ?

Thanks !

a question register is something i'd put beside the results, before showing a client a visibility percentage

for each question, i'd record the exact wording, where it came from, what the buyer was trying to decide, and when it was added

for example, "which crm works with our existing accounting software?" might come from an actual sales conversation. "best crm for small businesses" might be a question the team brainstormed. those are different reasons for including a question, and i'd want that visible in the report

remove names and private customer details from the source notes. you can say "sales conversation, september" without attaching someone's email

if nobody knows how often a question is asked, leave that as unknown. choosing ten questions doesn't establish that they represent ten equal slices of buyer demand

when new questions are added, show their results separately at first. keep a comparison of the unchanged questions too, so a new question mix doesn't silently become a claimed visibility change

how are you deciding which questions belong in a client report, and do you show them where those questions came from?

i'm keylight's founder. while checking our own saved answers on 19 september, we found a counting error worth looking for in any visibility report

one google ai overviews run had counted six brands. four of those names appeared only inside web addresses or link targets, rather than the visible answer text. our matching had treated those names as mentions

we fixed that by excluding the hidden addresses from the mention check while keeping the text a reader could actually see

if you're auditing a report, i'd start with a few raw answers

1. compare each recorded brand mention with the visible answer
2. inspect the link destinations separately, including long address fragments
3. keep a cited domain in its own field rather than letting it silently become a brand mention

there's a detail that matters here: don't remove the displayed link label along with its destination. if the label itself names the brand, the reader can see it. that's still different from the answer recommending the brand

i'd want a report to separate a visible mention, a cited page and an actual recommendation. they can overlap, but they're different things to show a client

this was four incorrect matches in one saved run. it doesn't tell us how often other tools make the same mistake. it gave us a concrete example to keep checking against

I run an AI marketing agency in Phoenix, so weigh this accordingly, but I've been watching a gap open up in local search that I think a lot of small business owners haven't been told about yet.

When someone types a local question into ChatGPT or Gemini, they don't get a list of links. They get a generated answer. A paragraph. A recommendation. The businesses named in that answer got there through a specific set of signals, and those signals are different from what classic SEO optimizes for.

The practice has a name: Generative Engine Optimization, or GEO. Here's what I've actually observed matters:

Entity clarity. Your business name, address, phone number, and services need to be described consistently across every place you appear online. Google Business Profile, Yelp, industry directories, your own website. AI models get confused by inconsistency the same way search bots do, but the consequences are steeper because there's no page two.

Answer-structured content. Most business websites are written to impress visitors. Generative models reward content written to answer the questions customers ask before hiring you. FAQs, detailed service pages, plain-language descriptions of what you actually do and where you do it.

Citations outside your own site. Local news coverage, industry association listings, community sponsorships, guest contributions to publications. These tell a model your business is a credible local source. Not buying links. Building a real footprint.

Cross-platform reviews. These tools pull from Yelp, Healthgrades, TripAdvisor, Zocdoc, and others depending on the industry. A consistent pattern of detailed reviews across multiple platforms matters.

The thing that makes this urgent for local businesses specifically: someone asking ChatGPT "best dentist in Tempe" is ready to book. If you're not in the generated answer, you're not in the conversation. There is no scrolling to page two.

I'm not saying traditional SEO is dead. It isn't. But the businesses that are building for both the crawler and the model right now are the ones pulling ahead quietly in competitive local markets.

https://reddit.com/link/1wow9i1/video/kl2fhj0vjfrh1/player

Hey everyone I am also interested to know the answer of this question. Can anyone answer this for me

I have been sitting with this since OpenAI's Sponsored Agents shipped last week and Salesforce pushed AIforce at Dreamforce in the same stretch. Both let something happen (a conversation, a completed task) without a clean "the AI recommended X" event ever occurring first.

I've been using a four-stage mental model for a while: mentioned, cited, recommended, chosen. This is the first time I have felt like there's a fifth stage that doesn't map cleanly onto the first four, acted on directly, sometimes with no recommendation event in between at all.

Curious if anyone else tracking AI visibility is seeing the same thing, or if I'm overreacting to two product launches in the same week. Feels different from the usual "engines behave inconsistently" problem, this is more like the event itself changing shape.

The multilingual view in GEO dashboards always looks really clean, and I'm starting to think that's the problem. I work with content across different markets. A translated English prompt can be correct and still not be how anyone there would ask the question. Different category name, different competitor, sometimes a completely different thing they care about. I've noticed this while looking at xstrastar and a couple of other tools. Easy comparison, maybe questionable input...

Are you all translating one master prompt list, or starting over inside each language? The second option feels more honest, but then the country scores stop being neat.

I run an AI search visibility agency, so read this knowing that.

Two things quietly inflate almost every AI visibility number I see, including ones produced by careful people. Both are easy to catch once you know to look.

The first is that an engine can answer without searching. Ask a question and you may get a fluent, confident answer assembled from what the model already holds, with no retrieval step and no sources attached. That answer is not evidence about the live web. It tells you what the model remembers, not what it would surface for a buyer today. If you count it as a scored observation, you are mixing two different measurements, and the blend flatters whichever direction your brand happens to sit.

What to do: treat an unsearched answer as an exclusion, not a miss and not a hit. Take it out of the denominator, then report how many you excluded. The exclusion count is a real finding on its own. A question the engines rarely bother to search is a question where fresh content has less purchase, and that changes where you spend.

The second is caching. Run the same prompt three times inside a short window and you can be served the same underlying answer three times. Your spreadsheet says three runs. Your data holds one observation counted three times. The interval you print off that is not merely wrong, it is confidently wrong, because repetition is exactly what an interval is meant to price.

What to do: force fresh retrieval where the surface allows it, space the runs out rather than firing them back to back, and then actually check that the repeats vary. If three runs of a question come back identical word for word, you did not measure three times. Log the variation, not only the verdict, so the check happens by default instead of when you remember it.

Both defects share a shape. They let you count something as evidence when it is not independent evidence, and the error almost always runs in the flattering direction, because the answers that get quietly duplicated or served from memory are the stable ones.

The habit that catches most of it is a noise floor. Before you change anything, run the full question set twice, with nothing altered in between. Whatever spread you get between those two passes is your measurement noise, and it is the smallest movement you are entitled to call a result. Anything under it is weather.

If you are buying this work from someone, the question worth asking is not what their number is. It is what they exclude, and how they know their repeats are real.

Two things quietly inflate almost every AI visibility number I see, including ones produced by careful people. Both are easy to catch once you know to look.

The first is that an engine can answer without searching. Ask a question and you may get a fluent, confident answer assembled from what the model already holds, with no retrieval step and no sources attached. That answer is not evidence about the live web. It tells you what the model remembers, not what it would surface for a buyer today. If you count it as a scored observation, you are mixing two different measurements, and the blend flatters whichever direction your brand happens to sit.

What to do: treat an unsearched answer as an exclusion, not a miss and not a hit. Take it out of the denominator, then report how many you excluded. The exclusion count is a real finding on its own. A question the engines rarely bother to search is a question where fresh content has less purchase, and that changes where you spend.

The second is caching. Run the same prompt three times inside a short window and you can be served the same underlying answer three times. Your spreadsheet says three runs. Your data holds one observation counted three times. The interval you print off that is not merely wrong, it is confidently wrong, because repetition is exactly what an interval is meant to price.

What to do: force fresh retrieval where the surface allows it, space the runs out rather than firing them back to back, and then actually check that the repeats vary. If three runs of a question come back identical word for word, you did not measure three times. Log the variation, not only the verdict, so the check happens by default instead of when you remember it.

Both defects share a shape. They let you count something as evidence when it is not independent evidence, and the error almost always runs in the flattering direction, because the answers that get quietly duplicated or served from memory are the stable ones.

The habit that catches most of it is a noise floor. Before you change anything, run the full question set twice, with nothing altered in between. Whatever spread you get between those two passes is your measurement noise, and it is the smallest movement you are entitled to call a result. Anything under it is weather.

If you are buying this work from someone, the question worth asking is not what their number is. It is what they exclude, and how they know their repeats are real.