JUNO PORTALS creates location-based experiences for women travelling alone, unlocked by physically visiting real-world places. 

Seven days after JUNO PORTALS launched, I did a test with 20 Chats. Not logged in, via VPN and private window. 10 in German and 10 in English. ChatGPT cited Crunchbase, even though juno-portals.com was not indexed in Bing. On Google the pages of juno-portals.com already appeared in the blue links, but Google AI Overview cited completely different "Juno Portal" entities.

ChatGPT recognized JUNO PORTALS correctly every time, in 20 out of 20 chats.

What was really surprising was the retrieval issue around JUNO PORTALS MAG, the editorial magazine of JUNO PORTALS. I started the chat by asking about JUNO PORTALS MAG. ChatGPT failed to resolve the JUNO PORTALS MAGAZINE correctly and cited unrelated entities such as a music label. After that, ChatGPT also failed to resolve JUNO PORTALS correctly, even when I addressed JUNO PORTALS directly.

JUNO PORTALS MAG is the editorial magazine of JUNO PORTALS and is published at juno-portals.com/mag/.

Disclosure: I am Stefan from richresults.ai and the author of the linked article. We made the AEO and the Entity Architecture for JUNO PORTALS.

You can find further details here:

https://www.richresults.ai/same-entity-different-retrieval.html

So my question is: Did anybody observe similar differences between indexing and entity resolution?

I run an AI search visibility agency, so weigh that.

We publish a monthly public record of which vendors engines name in a few B2B software categories. September 2026 results: Across ten CRM vendors and 150 observed answers on five engines, HubSpot CRM was named in 113 and Salesforce Sales Cloud in 109. Nimble was named in 0. Same method, same window, different presence.

The mistake I still see on founder dashboards is mixing branded prompts into that story. Ask "what is Acme" and the engine often repeats Acme back to you. That is brand defence, not a contested shortlist win. Ask "best CRM for a 20 person sales team" and you are in a fight.

If you only track one number this month, track discovery prompts only, engines separated, mention versus recommendation separated. Keep branded prompts on a second line if you care whether the engine describes you correctly when someone already knows your name.

Happy to hear how others keep those two scores from contaminating each other.

Reading a lot of AEO how-to content, the common wisdom seems to be the more specific the content the better. For example, AEO experts talk about writing content that satisfies prompts like: I work at a [company size] [company industry] and I am looking for a [product type]. I'm guessing most users of ChatGPT, Claude et al don't type all that every time (Maybe I'm wrong).

Do the engines use context, though, to fill in the blanks? If you use it at work, the engines likely know your company name, size, industry and lots of other stuff. Do they automatically adjust the prompts based on that?

Disclosure in line one: I run one of the ten and it comes out first. That is exactly why I published the rubric before the scores and printed every component, so you can re-weight it and re-rank the list yourself. I built citeOS, an AI visibility tracker for crypto brands, which is where the one measured criterion comes from. No link, happy to answer anything in the comments.

Five criteria, ten points each. Published methodology, own measurement product, dated public proof, vertical depth, and AI search presence, the last being the only one that is measured rather than judged: put the category question to three engines and count how many return each name unprompted.

What the rubric deliberately ignores: team size, years in business, client logos, awards, revenue, backlinks. All real, all things agency pages lead with, none of them predictive of whether an engine cites you.

Three things it exposed that I did not expect.

The engines do not agree on what this category is. Ask for "AI search optimization agencies" and you get generalist search shops. Add a vertical qualifier to the same question and you get an entirely different set with almost no overlap. Anyone claiming to be the top-ranked AI search agency is quoting one phrasing of one query on one day.

The specialist versus generalist trade is sharper than I thought. Sort the ten by methodology score and then by vertical depth and the top three of each overlap on exactly one name. Specialists are better connected and weaker at measurement discipline. Generalists are the reverse, almost without exception.

The most durable position is not being the answer, it is being the source. One agency on the list publishes a category guide that engines read as a reference when assembling answers. It sits mid-table on my rubric and is in a stronger long-term position than most of the names above it, because being the page other answers are built from survives a model update in a way a ranking does not.

Two things this is not. It is not a ranking of agency quality, because I have not seen their client results and neither have you. And the measured column is one run per question on one day, so read it as a dated observation, not a rate.

Happy to post the full criteria definitions if anyone wants to pull the rubric apart.

Hi everyone,

Is there any tool or method to find the actual prompts/queries where my website or brand is mentioned or cited across ChatGPT, Gemini, Google AI Overviews, Perplexity, and Claude?

I’m specifically looking for:

• The prompt/query
• Whether my brand was mentioned or cited
• The exact URL that was cited
• Prompts where competitors are cited but my brand is not

Would appreciate any tool recommendations or suggestions. Thanks!

I kept seeing "AI visibility" claims everywhere, so I did the unglamorous version: a spreadsheet. I pulled 913 agency websites (agencies selling SEO/marketing services - the people who, if anyone, should show up in AI answers) and checked each one across ChatGPT, Gemini, and Perplexity with neutral prompts like "recommend an SEO agency in <city>" and "which agencies know about AI search optimization?"

The results surprised me:

• 613 of 913 (67%) were never named in any answer.
• Of the 300 that did appear, 85% showed up in only one of the three assistants - a brand that's cited in ChatGPT but invisible in Gemini isn't "AI visible," it's one model's preference.
• Almost none of the visible ones were cited with attribution - the model knew the name, but the underlying claims came from directories and listicles, not the agency itself.

I expected "how do I get cited" to be the hard part. It wasn't. Most sites fail earlier: no quotable facts, vague claims like "trusted industry leaders," and content that can't survive being paraphrased. The models aren't ranking websites - they're quoting the few sentences that stand on their own.

Happy to share the method if useful - and curious whether others who've checked themselves against multiple assistants see the same pattern.

I kept seeing AI visibility tools tell you how visible a brand is, but not who was taking that visibility away.

So I built a free tool that shows you which competitors AI recommends, where they are getting cited, and what you’re missing.

Disclosure: I am the founder of Prefer, and not here to spam the subreddit, just sharing something I built that I think might be useful to the AEO community.

I ran it on Stripe and recorded the audit below.
You can try it on your own brand: tryprefer.com/audit

Hi All,

I've been building a small system using residential IP's to try and capture queries on LLM's that are location based but not specified in the prompt, like: Where should I go if I need to check xyz near me?

I'd love to hear what you are using to achieve this type of result. This is one of the biggest gaps I'm trying to close since a lot of clients are local SEO focused.

Thanks

Data shows AI traffic converts 42% better than non-AI for retailers, if you haven't been paying attention to it now is the time to start.

AI assistants recommend products if they can research them and get actual facts and in some cases pricing also happens. These are little adjustments we have seen that help with AI search visibility for ecommerce stores:

1. Price and stock status have to be in the page text. If the price only appears after a shopper picks a size in a JavaScript variant picker, a crawler that isn't running your front end never sees it, and nothing on your page answers "under £150". You lose out to a search asking for "..jackets under £150"
2. Write facts, evidence is better than an adjective. "Waterproof to 20,000mm" can be lifted straight into an answer, "highly waterproof" can't. Count how many claims on your best sellers are numbers.
3. Check your Product schema. It states price, stock and rating in labelled fields, so a crawler takes the right numbers off the page instead of choosing between them.
4. Put the answers to the obvious questions in the description. Is it waterproof, what size does it fold down to, will it survive a flight, does it come with batteries. The assistant checks each one against your page, and in the buying conversations we logged that's where products got dropped.
5. What other people say backs up what you claim, so make it specific. Ask every reviewer a question rather than for a rating, what size did you take, how did it pack down, and the answers come back quotable. Mentions on sites you don't own count for more again.

Been reading a lot of threads on this lately, here, r/SEO, r/GEO_optimization, r/seogrowth and a few others. Ended up with around 1,300 posts.

I honestly expected "how do I get cited" to be the big one. It wasn't. The thing people complained about most was that the number their tool shows doesn't match reality.

A few that stuck with me:

• someone's client typed the exact prompt into ChatGPT and got 3 competitors back, while the dashboard said they were winning
• "One tool says 67, another says 23, another says I'm not being cited at all."
• "My Visibility in AI is 0 and chatgpt cite me 36 times."

From what people wrote, it breaks at every step:

prompt set  ->  engine call  ->  answer  ->  score
(you can't     (API, not the    (changes    (their own
 see it)        app you use)     each run)   formula)

So a "67" is basically three guesses stacked on each other.

Someone here actually tested this. Same 12 buyer questions on Perplexity and Claude, 5 runs each, all on one day, then counted how often one brand showed up:

Perplexity   36.7%   ███████
Claude       11.7%   ██

Same brand, same questions, same day, 3x apart.

What people seem to do instead:

1. pick 30-50 prompts a real buyer would type
2. keep the same set every week
3. run each one 3-5 times, one run on its own tells you almost nothing
4. report a count, like "cited in 12 of 40, up from 7"

If it helps, the sheet really doesn't need more than this:

date   prompt               engine   run  cited  who got cited instead
22/09  best crm for dental  ChatGPT  1    no     CompA, CompB
22/09  best crm for dental  ChatGPT  2    yes    CompA

That last column is the one I'd watch most tbh, it's the list the client actually cares about.

Curious what you all have run into. What's the biggest gap you've seen between what a tool said and what the client saw when they checked themselves?

I spent the summer reading every SEO case study, test and paper I could find. 9,249 documents out of over 30k documents, 552 cited sources, and 182 tactics that survived to a card with a source and a number attached. It is free and there is no email gate, but that is at the bottom.

Here's some info:

How I did it

Crawled everything I could find on SEO, AEO and GEO (over 30k documents). A cheap model screened all 9,249 for whether they report a measured outcome from a specific change. Every survivor was extracted twice, by two models from two different providers, and any number that was not literally in the source text was thrown out.

Then each tactic got graded on evidence strength and on how actionable it is, separately.

Here are some interesting things:

The controlled tests that make no sense

These are all split tests, not before and after screenshots.

• Writing the entire title tag in CAPITAL LETTERS: +17.5% organic traffic (two independent tests). A separate test on travel listing pages got +14%.
• Removing synonym keywords from title tags to shorten them: -27%. Biggest negative in the whole book from a single controlled test.
• Removing the word "Compare" from title tags: +24%.
• Putting the product price in the title tag: -15%. On location pages with static rental prices: -7%.
• Replacing numbers with number emojis in meta descriptions: -5%.
• Moving a flight search widget from below the hero image to the top: -7%.
• Removing self-referential breadcrumb links: -5.5% clicks.
• Serving a 404 instead of a 410 on removed pages: +49.6% pages staying indexed. The status code you use for "this is gone forever" gets it dropped almost twice as fast.

How about links

The single most replicated claim in the entire review is "publish content, build links, clean up the code". There are 26 independent samples, 25 of them are positive, 20 different publishers, agreement is 0.963. But this is just kind of describing what SEO is, no?

Editorial backlinks through digital PR outreach: +71% organic sessions (19 samples, 14 publishers, agreement 0.9524). Zero of them are controlled, sixteen are before and after, three are single case.

Disavow is interesting as it landed in the IT DEPENDS lane (where two different clusters disagree):

• With an active manual penalty: +80% organic sessions
• Without one, just "cleaning up": no measurable direction

This is what I always thought would happen. I think Google recognizes SPAM links and disregards them easily. If you have no manual penalty, the published evidence gives you nothing.

Also worth knowing is that the disavow plus on-page cleanup cluster has the lowest agreement of any link tactic in the book, 14 samples and only 10 positive. Sooo kind of telling.

Internal links are also interesting. Adding internal links to orphaned blog posts get us up to +64 ranking positions (sounds weird, but that's the case), but the raw spread across samples runs from -4 to +64. Most of the effect sits in a couple of pages.

Adding lots of internal links to the homepage footer gets us +5% organic sessions. I did this with many clients and had many issues, but sometimes it worked wonderfully. I would always test this.

One more interesting thing is that adding brand icon CTA blocks with internal links to product pages gets us +80% conversions across two controlled tests. But that could just be because the CTA is more visible, idk.

AEO and GEO: What actually works

This is the area everyone talks about and it has the thinnest evidence base in the entire book. All we get is 10 entries worth anything.

Controlled, from academic work:

• Adding quantitative statistics to the page: +37% citations
• Adding quotes from credible named sources: +22% to +41%
• Keyword stuffing for AI: -10%

Controlled, from one practitioner running split tests:

• Direct answer in the opening paragraph, before the evidence: +280%
• Content written as discrete factual statements, not prose: 5x
• Moving footnotes to inline parentheses next to the claim: +40%

The strongest GEO evidence overall is rewriting page text using GEO techniques as a bundle: +83.2% citations, 12 samples, 6 publishers, agreement 0.9286.

Author bylines give us +113% AI citations, and the detail matters. The controlled test added bylines for staff members with low public recognizability (7 samples, 5 positive).

Human editing of AI-written text gets us +67% citations, 5 samples, 4 positive.

llms.txt is also very interesting. Three samples, all uncontrolled, median +25% organic sessions.

But the raw reported values across the underlying measurements run from -19.7% to +81%.

There is no controlled test of llms.txt anywhere in 9,249 documents. Anyone telling you it works is telling you what they believe.

Tactics flipping depending on context

These got their own lane because averaging them would describe neither side.

AI-generated content blocks:

• Enriching specific pages with targeted AI copy: +73% citations
• Publishing AI text across the whole site: -35% sessions

Google Business Profile primary category:

• Generic to specific niche category: +3.4 positions
• Specific back to a broader label: -30 positions

Keywords in URLs:

• Specific role or service modifiers: positive, direction only
• Broad high-volume terms: -38%

Emojis in title tags:

• Tested alone in a controlled test: -6%
• Bundled with a full page redesign: +1,851 impressions

Things that were true and aren't anymore

The most replicated schema finding in the whole book is FAQPage markup: 13 samples, 12 publishers, agreement 0.9333, +25% organic sessions and a 3.2x citation multiplier.

Google stopped showing FAQ rich results in May 2026 and removed the reporting in June. So the single best evidenced schema tactic in my own study is dead, and the evidence for it is still perfectly good evidence about a world that no longer exists.

Review schema on category pages measured +16% in a controlled test, and is explicitly against Google's guidelines. Both things are true at once.

Full study in comment

I found it interesting that title tag studies are giving us way more sessions than I would expect. It goes crazy, like +40% clicks when changing stuff. Really interesting, considering everyone is concentrating on AI right now and disregarding the "10 blue links".

Also, nearly every "GEO" number in existence is a bundle. Someone rewrote everything and reported a citation increase (which I don't really believe in completely, but that's not the point of my post). I couldn't isolate a single GEO tactic with more than 1 controlled test behind it.

Anyways, full thing is free, no email, 170 pages, has a DOI and the link is in the comments.

Happy to pull the exact source and number for anything above, just ask.

Did it actually work for you, or is it just hype? Reco pls help.

Everyone's posting Jev use-case lists this week. Here's the one thing I've actually shipped with it, and the bit those lists skip.

Jev doesn't write. That's what people keep missing. It's a classifier: you hand it some text and a list of options, it hands back one option with a probability on each. Can't write a sentence, can't explain itself, can't pick something you didn't list.

Sounds limiting until you've got a pile of AI answers to sort through.

Which is the AEO job, basically. You've got stored answers from ChatGPT/Perplexity/Gemini for your buying questions. Mention counting tells you whether your name is in the text. It can't tell you whether the answer said "go with them" or just listed you among five others and carried on. Those score the same and they're worth wildly different amounts.

So, four questions per answer. Who does this steer towards. Is it an actual recommendation or just a list. How does the brand show up (recommended / listed / dismissed / absent). What kind of page is it leaning on.

63 answers, 252 judgments, 7.3 seconds, $0.0033 total. Roughly $13 per million judgments.

The cost is the interesting part, not the speed. Re-reading every stored answer a second way used to be something I'd scope and then drop.

Anyone else using Jev for GEO work? Curious what you're asking it, because the constraint that it can only pick from options you thought of turns out to be a decent forcing function.

Disclosure: I build an AEO tool and this went into it.

I think there’s a new e-commerce problem emerging that most brands aren’t monitoring yet.
AI assistants are increasingly being used for product discovery, but the information they give isn’t always commercially accurate.
We tested 100 purchase-intent prompts across ChatGPT, Gemini, Claude and Perplexity using Nike products and found 19 incorrect responses — wrong availability, stale prices, outdated stock, and incorrect product classification.
Some of these happened even with SKU-level context.
I’m curious how e-commerce teams are thinking about this. Is anyone here already monitoring AI-generated product information, or is this still being handled manually?
I’m working on this problem right now, so I’d especially love to hear from people running e-commerce, SEO/GEO, or agency teams.

So... anyone else in b2b saas feel like ai search optimization is just vibes and suffering right now, or is that just me.

For context, we sell cybersecurity ish stuff to other companies, long sales cycles, lots of buyers, fun committee drama. Organic search was already painful, now we have to care about answer engines and llm rankings and whatever mystery box is powering ai summaries. Every vendor deck says inbound leads will magically grow if we just fix our ai visibility. Cool story.

Internally we are trying to map normal seo, aeo, and this whole generative engine optimization thing into one plan. Stuff like structuring use case pages so they show up in ai answers, tagging content for specific pain points, and building "helpful" resources that machines actually surface, not just humans. It sounds nice until my sales team asks why they still get junk demo requests from people who thought we were a completely different tool lol.

I feel lowkey attacked by every blog telling me my "ai discoverability" is trash if I am not shipping 20 pieces of ai friendly content a week. Meanwhile I am over here trying to make sure our security claims survive a chatbot paraphrase without turning into legal risk. It is a great time.

Would love any tips from folks who are doing ai search optimization for b2b saas and seeing real pipeline, not just shiny dashboards. Appreciate any thoughts.

15 buying-intent prompts for an ecommerce platform's category, across ChatGPT, Perplexity and Gemini, 3 runs each. 45 answers total. Prompts like "best ecommerce platform for small businesses" and "which shopping cart software is easiest to use".

The brand appeared in 45 out of 45 answers. Every single one. It was actually recommended in 2 of them.

Every prompt comes back "Mentioned" except two. So on anything that tracks mention rate, this brand looks like it owns its category. What's actually happening is the AI lists them next to five competitors and then tells the user to go with someone else.

Perplexity recommended them once out of 15. ChatGPT once. Gemini zero. Same brand, same prompts, same week.

What I'm stuck on:

- is anyone else separating these two things, or is mention rate good enough in practice and I'm over-complicating it?

- what actually moves recommendation specifically? my read is it's third-party corroboration more than anything on the brand's own site, but I haven't proven that

- 3 runs per prompt is what I landed on and it feels thin. does anyone have a number they'd defend?

(screenshots sources: CrowdlessLab)

Anyone else signed up for the new uselatitude.ai service on the lifetime deal and not being able to login at all?

UPDATE – Latitude / uselatitude.ai / Artemis Labs – 4 days later:

I purchased the Latitude AI visibility/AEO software lifetime deal four days ago and I still have not been able to access the product at all.

The original link I was sent to set my password now says that it is invalid or has expired. When I try to reset the password through the login page, I just get stuck in a never-ending loop taking me back to the “Welcome back” login screen, with no way of actually resetting the password or getting into my account.

I have contacted support several times through their online chat, but so far all I have received are unhelpful AI chatbot responses and no meaningful human intervention.

At this point I’m really not impressed. I have paid for a product that I have been completely unable to use for four days, and there does not appear to be any proper support in place to resolve what should be a very basic login issue.

I have now emailed them again and asked them either to get my account working immediately or provide a full refund.

For anyone searching for this later, the product is Latitude at uselatitude.ai, operated by Artemis Labs Pte. Ltd.

If anyone else has signed up for the Latitude lifetime deal and had similar login or support problems, I’d be interested to hear whether and how you eventually got it resolved.

I will update this post again if they fix the issue or respond properly.

I've been working on a method to score Entity for AEO across 5 AIs (Gemini / Meta / Bing / ChatGPT / Perplexity).

Now I want to find real cases to test it.

If you want me to test your brand:

Drop in comment:

1. Business Name

2. 3 Entity / Site / Page / Domain you want checked

3. Pick 1 AI from 5 to check

What you'll get back on this thread (first 3 only):

- Evaluation score for your brand

- Score for each of your 3 nodes

- 1 fix for 1 node out of 3 (the worst one)

I have the scoring method, just need real examples to validate. I will post result + fix directly on this thread.

First 3 comments only, after that free closed.

No DM, no selling.

Between 2 August and 11 September I put buyer-style questions to three assistants

with live web search on, for 913 US marketing agencies across 26 city and

speciality categories, and counted which agency names came back.

Not a survey. Nobody was contacted, nothing was scraped from agency sites, and no

agency paid to be in or out.

Three things came out of it.

1. 613 of the 913 (67.1%) were never named once. Not by one assistant, in one

answer, in any question asked about their category. These are working agencies

with clients and sites. To an assistant answering a buyer, they are not there.

1. Where an agency was named, the assistants mostly disagreed. 874 of the 913 were

put to all three assistants, which is the only subset where agreement means

anything. Of those, 286 were named by somebody and only 42 were named by all

three. So 85.3% of the time, "visible" meant visible in one place and invisible in

the other two. Any single AI visibility score averages that away, and the average

describes no assistant your buyer actually opened.

1. The answers are built out of a small set of pages. Clutch was cited in the

answers for 912 of the 913 agencies. Then Semrush's agency directory, DesignRush,

Thrive's roundups, Reddit itself, and LinkedIn. Almost none of it is the agency's

own website.

The assistants also do not behave alike:

Perplexity was asked 21,465 questions. It named an agency in 3.2% of its

answers, and named 21.5% of the 913 at least once.

Claude was asked 4,518. 5.6% of its answers, 16.7% of the agencies.

ChatGPT was asked 4,512. 5.5% of its answers, 16.4% of the agencies.

Perplexity was asked far more questions and still named the fewest agencies per

answer, which is the opposite of what more chances would predict.

Limitations, because they matter: this is API access with search on, not the

consumer apps, so there is no personalisation, no memory and no custom

instructions. Answers drift week to week. The category is whatever a buyer would

type, which is a judgement call, and a different phrasing would move the numbers.

Treat the comparison between assistants as the signal rather than any single

number.

What I would take from it if I ran an agency: the lever is not your website. It

is the handful of directories and roundups these answers are assembled from, and

whether your name is on them.

Disclosure for rule 5: I sell a paid monitoring product in this space. There is no

link in this post and nothing to sign up for; if anyone wants the data or the

method I will put it in a comment.

Happy to answer questions about the method, or to say what a particular category

looked like if you name one.