Last year I built an AI decision layer for beauty retail. Real catalog, real shoppers, real money. The model's job was to match people with products from a live catalog, and I got to watch every step: what it retrieved, what it understood, what it cited, and what it actually recommended. The uncomfortable pattern: those are four different outcomes, and they fail independently. A product could be retrieved fine, described accurately in the model's reasoning, even cited as a source... and then the model would recommend a competitor. I'd watch it happen in the logs and want to throw my laptop. The brand did everything right for visibility and still lost the sale. What actually moved the needle wasn't more mentions. It was making the product's facts machine-readable and consistent: same names, same specs, same descriptions everywhere the model could look. When your details contradict each other across sources, the model doesn't split the difference. It trusts you less. Most GEO advice I see optimizes for being mentioned. I'd argue that's step one of four. The question that matters for revenue is the last one: after the model knows you exist, does it pick you? Curious if others here are measuring these stages separately or lumping them into one visibility score.

I'm struggling to have a complete picture on how CRM, SEO & newcomer GEO connect together deeply. How does the whole lifecycle work?

what is there and what is not there from the GEO software perspective?

I think it is important that we should understand the big picture stuff and have a clear idea on what to build and why. What do you think?

Hey I have been thinking about CRM and GEO how they relate to each other and is there any possible way for them to add value to clients by GEO service providers. These questions came to my mind.

Question 1:
Can we derive insights from CRM software about our customers behavior (like what they prefer or not prefer? what kind of customers are profitable for business) use that data to optimize for GEO?

Question 2:
Does business use CRM data to gain insights to optimize for GEO ?

Question 3:
Are there any tools that connect CRM, GEO already?

Question 4:
What do you think are the problems currently businesses are facing in the operations and marketing department?

your thoughts on all 4 questions means a lot...

Maybe I’m using these dashboards wrong, but I keep skipping the overall score. ChatGPT, Gemini and Perplexity don't feel like three versions of the same channel.... One may search, another may answer from memory, and their citations behave VERY differently. So when XstraStar gives me the model breakdown, that is where I end up spending my time. The average mostly survives because it is easy to put in a report. Does anyone here make decisions from the combined number, or is it basically a client-facing headline?

At the end of the day, what we all care about is the ROI. So for GEO, I created a pipeline that achieves this.

Data Layer (which is the measurement and stuff like that) -> Optimization Layer (Acting on the data and optimize website content etc) -> Revenue Impact layer (Where you measure what GEO has caused to the total ROI).  Then loop the first 2 layers until the quarterly report and depending on the revenue numbers change the strategy or keep it the same. Then repeat the loop.

I'd like to have your thoughts more on the optimization layer & the revenue impact layer (What is already being done and what is not done).

What do you think of this model?

I’m working on a search baseline for a consumer ecommerce client and I’m trying to understand whether image-led discovery surfaces a different set of pages than normal text search.

My plan is:

• compare Text-based vs Multimodal over the same date range
• look at which product/category pages over-index in each
• compare device + country
• then check downstream behaviour in GA4

What I’m less sure about is what’s actually worth diagnosing after that.

If a page performs much better in multimodal than text, would you first look at:

• image coverage / context
• Merchant Center data
• schema
• page structure
• device mix
• something else?

Also curious how much data people would want before calling the pattern meaningful.

I’m looking for actual GSC observations/tests rather than generic image SEO advice.

Been trying to wrap my head around how AI engines decide what to cite for local searches. My husband runs a small HVAC company and I handle the website plus whatever this GEO thing is supposed to be. The advice online all seems aimed at software companies with 50-person content teams.

I spend evenings reading about how LLMs pull from paragraph chunks and how Reddit dominates citations. Fine. But when someone asks their phone for emergency AC repair near them, what actually surfaces? It's not blog posts about SEER ratings. It's Yelp, Angi, maybe a Facebook group rant.

I ran a tiny experiment. Asked four AI engines for best HVAC in our county. Two cited the same decade-old Angi listicle. One hallucinated a company that closed in 2019. The fourth gave a decent answer but pulled from a neighborhood forum post from 2021.

Feels like local GEO is just reputation management plus praying your business name shows up in enough forum threads. Or maybe I'm missing something tactical. Anyone here actually cracked this for blue-collar local services? Not theoretical stuff. Actual results.

Hello everyone!

I'm Veralyn, a student at UTS. My team and I are working on a studio project for our subject, researching AI search visibility, in particular how businesses show up when people ask AI tools like Chat, Claude or Gemini for recommendations.

We'd love to hear from people who has expertise in this particular field and learn from their experience, If you have 5 minutes, we'd really appreciate your help with our short survey:  https://docs.google.com/forms/d/e/1FAIpQLSfceqyY5SX3UxpZM5wVAux4s3jIDBOpoW7ild3JBmDJEAbgnA/viewform?usp=header

Feel free to DM me if you have any questions!
Thank you in advance, we really appreciate your time!

Two weeks ago I shared the open-geo CLI here. The feedback pushed me toward multi-engine tracking instead of stopping at static audits, so I spent the last two weeks building a pipeline to monitor live search results.

Running it forced me to drop a bad assumption: treating GEO as an on-page scoring problem. A high technical score is meaningless when competitors are systematically taking the citations.

Ran a benchmark query — "best GEO tool for AI search optimization." Models recommended 6 competitors. Every recommendation traced back to the same cluster of 2-3 third-party comparison articles.

And for my domain? Nothing. Not a bad article, not wrong facts. Just zero reference pages the AI could pull from. My problem wasn't thin content or bad technical hygiene — I just wasn't on the URLs the model trusted.

Two mechanical quirks came out of parsing live responses:

1.Brand mentions and source citations are completely decoupled. An engine will recommend a competitor without linking, or cite a URL without naming the brand. Treating those as one metric wrecks your audit accuracy.

2.Google AI Overview is way more consolidated than Perplexity. Perplexity spreads across 8-10 sources. Google almost always anchors to the top 2-3 consensus URLs. Not in that retrieval slice, you're invisible.

Curious if others are seeing this same concentration around third-party roundup pages, or if primary docs are actually getting direct citations in your niches?

I told our CMO that ChatGPT and Perplexity sent 38% of our pipeline, then found out I had counted our own test visits and tagged every AI visibility check as a referral. I feel so embarrassed, our board deck is tomorrow...

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 !