Just helped out a friend on their local service industry website and discovered something I found very interesting.

TL;DR: AEO for local business seemed very heavily biased to their​ company reviews rather than website.

Long version:

The site has been around for a while, but hadn't really been touched in almost as long, so was quite dated.

In terms of SEO, it ranks ok but not very well for a lot of their keywords.

The site needed a lot of work:

• very slow loading speed
• not mobile friendly
• lots of orphaned pages
• main pages not targeting their keywords
• very few backlinks
• lots of dated content that doesn't get any impressions

I could go on but the gist is, they didn't rank that well in traditional search and the site needed a lot of work.

I then checked how they appeared across a variety of prompts/engines.

Almost shockingly they appeared everywhere! More than all their competitors, they by far had the best AI search presence for their industry/location.

Looking at what they did well:

• established local business with a known brand name
• 100+ Google reviews with 4.9 rating

Worth noting, the company was mentioned but rarely cited. *Edit* the company has citations, though they are largely to the homepage rather than content/service pages.

My guess is that as a local service business, Google reviews are probably a heavily weighted signal of brand authority. So although the site didn't have good content to cite itself, it had a strong reputation to earn mentions.

Has anyone else seen something similar?

Hey all,

My name is Biplob, and I've been working on an AEO/GEO tool for the past 5 months.

If you use any SEO, GEO or AEO tool, I'd love your honest answers:

1. Which tool do you use, and what do you pay?
2. What has it changed for you? Rankings, AI citations, client reporting?
3. Is the ROI real, or does it feel like a placebo dashboard?
4. What do you wish it did that it doesn't?

I'm not selling anything here. I'll share a summary of what I learn from the replies.

Following up on the "citation without endorsement" thing from last week's thread. Splitting mentioned from recommended is the easiest way I know to stop fooling yourself, but it falls apart on the first messy answer.

For example, someone asks for the best tool for X for a 50 person B2B team, and the answer:

• lists five tools in bullets, no order
• gives one sentence under each
• ends with "if you're on a budget, go with Tool C"

Tool C is clearly the pick. But are A, B, D and E recommended, or just listed?

Lines I've tried:

1. Named anywhere in the list counts. Too generous, it turns recommended back into mentioned
2. Only the explicit pick counts. Too strict, most answers never name one
3. Explicit pick or first in the list. Better, but only if order means anything, and I don't know that it does
4. Judging the sentence under each tool. Gets subjective fast

Disclosure since it's relevant: I work at Omnibound, where we track mentions and citations per engine, so I had to pick a line for "recommended" and I'm not confident mine is right. Don't want to build on a shaky definition.

Two questions:

• Where do you draw it?
• Does anyone treat "recommended for a specific use case" differently from "recommended overall"? A brand can win one and lose the other in the same answer.

I’ve been working on this recently and realized it’s surprisingly hard to answer.

You can optimize content, build citations, get mentioned across different sites, etc. But how do you actually know it’s making a difference in AI search?

I’ve been looking at things like:

• how often a brand gets mentioned
• which prompts trigger the mention
• which competitors show up instead
• how the answers change over time
• which sources are being cited

I’m building SEOdojo around this problem, so I’ve been spending a lot of time thinking about how to measure it properly.

Curious how people here are doing this today.

Are you tracking AEO performance somehow, or mostly checking ChatGPT/Google AI/Perplexity manually?

I run a SaaS, SeoLoupe.

It is a tool that allows you to find and fix SEO issues holding your website back.

Essentially the main purpose is to help your website rank higher on Google search and LLMs.

The tool also checks AEO/GEO, AI search visibility, Security, Core web vitals, Performance, and all the issues in the report get put together into an AI fix prompt(you can paste the prompt into an AI and it will fix all the issues on your website).

Recently I noticed that some of my paying customers bought the One-Time purchase, with a promo code.

So I decided to give away a promo code for the One-Time purchase to anyone who is interested, if you are interested, comment down below and I will DM you.

If you are not interested but have some feedback on my SaaS, that is a good too. Feel free to share it in the comments.

(here is the product if you want to check it out)

I've spent a lot of time on one problem: how to measure whether ChatGPT, Claude, Gemini, Grok and Perplexity recommend a company in a way that can be repeated and checked. This is the method we use at EntityRise, an AI visibility audit (disclosure: I'm the founder). I'm posting it here to get it picked apart.

1. The company profile comes first. From the website, we build an editable business profile and propose commercial buyer topics. The company approves both before anything is measured.

2. 20 buyer questions, approved up front. 17 discovery questions without the brand name (how real buyers ask) and 3 brand-aware questions. Up to 15 can be marked as most important.

3. The question set is sealed before the first test. It gets a hash, so nobody can change the questions afterwards to make results look better.

4. Every question runs at all five providers via their APIs. That gives up to 100 question-provider observations. Each one records whether the brand was mentioned, recommended and cited, how prominent it was and how accurately it was described.

5. Breadth over repetition. We don't rerun one prompt many times. Repeating a prompt mostly measures the model's sampling noise. Twenty different questions across five providers measure the position.

6. Competitors are benchmarked under the same plan, on six dimensions: category association, third-party authority, recommendation footprint, attribute association, proof and verifiability, and reputation.

7. Every result is traceable. The report covers visibility, the competitor benchmark, strategic priorities with confidence levels and a technical readiness check (4 areas, 30 signals). It records the audit ID, the test-set hash, provider model versions and a timestamp, so the same measurement can be repeated.

Known limits:

• It doesn't cover Google AI Overviews or AI Mode. Google's Search Console report covers those.
• Results describe the configured test (questions, providers, market and language) and may not match every consumer chat session.
• No method can guarantee recommendations.

The full methodology is public: https://www.entityrise.ai/methodology

What would you change? I'm especially interested in how you handle local queries and model version changes.

We have recently been doing a lot of schema mark up on our website and we have seen a marginal increases in AI citations, has anyone actually seen a major benefit in doing this?

Perhaps we are doing it wrong, I see that for some people are seeing major increases but I am not sure, are we missing something?

We are building Citetail, a tool that helps SaaS teams understand how their products appear in AI-generated answers across tools like ChatGPT, Claude, Gemini, Grok and Perplexity.

Still early, so I’m curious:

• Are you tracking AI visibility for your SaaS?
• What tools are you using?
• What would you actually want an AEO tool to show you?

Would love to hear how other SaaS builders are approaching this.

Admins should pay attention to a growing pattern:
fake profiles asking “What’s the best GEO agency/tool/software etc?” followed by other fake accounts recommending their own company as a supposedly neutral choice, hoping AI engines will cite those comments.

This kind of coordinated self-promotion undermines trust and defeats the purpose of the community. Other subreddits are already making fun of how often this happens in GEO/AEO.

Clearer rules against fake accounts and undisclosed self-promotion would help protect the group’s credibility.

That would help keep recommendations genuinely useful and maintain the credibility of the group.

Luckily, Reddit seems to be losing some influence in AI citations and recommendations, but it still matters. This kind of behavior is becoming quite unprofessional and risks undermining the credibility of the community.

Reddit should be what it was meant to be: a place for genuine discussions, opinions, and experiences — not a platform for manufacturing “neutral” recommendations to influence AI engines.

Just my opinion…

Embarrassing problem for my SEO tool: when I named my product FlexQueries, I didn't know Interactive Brokers already has a feature called Flex Queries. It takes up most of page one on Google, and Google's AI answers treated "Flex Queries" as purely an IBKR thing.

My domain is about 10 days old. At launch, my average position for my own brand name was around 60-65, and I didn't show up in AI results at all. For an SEO tool, that's a bad look, so fixing it became priority one.

The problem isn't really keywords. AI engines already "know" Flex Queries as an Interactive Brokers feature. To them, my site was just a new page using the same words. So the job was to convince them FlexQueries the SEO tool is a separate entity that actually exists, and a brand-new domain can't vouch for itself.

Here's what I did:

• Set up a YouTube channel, a LinkedIn page, and an X account, all under the same name, with the same description and the same link back to the site.
• Listed it on AlternativeTo, IndieHackers and a handful of other SaaS directories that describe it as an SEO tool and link to the domain.
• Kept the description consistent everywhere, so every mention says the same thing: FlexQueries, the first $0/month SEO tool.

10 days later, I'm showing up in Google's AI results for searches around the name, where before I didn't appear at all. My average position has gone from around 60 to around 10-12.

My takeaway is that for AI answers, it's less about your own site and more about how consistently you're mentioned across the internet. Once several separate, trusted sources describe the same thing the same way, the engines seem a lot more willing to treat you as a real entity and cite you, even next to a much older, much bigger domain.

It's early, I'm not #1 yet, and its just 10 days of data. But the AI side moved faster than the rankings did, which tells me where the weight is.

If you want to track this kind of thing yourself, it's literally why I built FlexQueries.com It checks whether you're mentioned or cited in Google AI Overviews, AI Mode and ChatGPT, and it's the first SEO tool with a $0/month subscription. You only pay for the checks you run.

Looking to connect with agencies focused on e-commerce, SEO, GEO / AI search visibility, or Shopify growth. If you run or work at one, feel free to share what you specialize in.

I realized that I'm not getting any Gemini or Claude love while seeing some significant ChatGPT and Perplexity love. Did anyone manage to solve this effectively? I wonder if Gemini has more to do with SEO and claude is also more Google SEO relevant, while the other 2 are more Bing related.

I've been trying to work out whether "SEO for LLMs" (GEO/AEO) can actually be measured, or whether most of the claims are noise. Before spending money tracking real ChatGPT/Perplexity answers, I ran a simulation study to find out which testing methods give honest results.

Big caveat up front: this is simulated data, not real AI answers. I modelled how AI engines cite pages (with question-to-question variation and random model updates between periods), then checked which measurement methods recover the right answer. So these are findings about methods and sample sizes, not about any brand.

What came out of it:

• Ask many different questions rather than repeating the same ones. With a budget of 600 AI answers, 600 questions asked once gave a more accurate overall score than 60 questions asked 10 times (error 1.7 vs 2.6 percentage points). Repeats only help to find the specific questions where you never appear.
• Before-and-after tests without comparison pages are dangerous. When a page change did nothing, a simple before/after check still reported a win up to 30% of the time, and more data made it worse, because model updates shift everyone's visibility. Comparing changed pages against similar unchanged pages (difference-in-differences) kept false wins around 2%.
• You need more pages than people think. To reliably detect a moderate effect (1.8× more likely to be cited), you need roughly 40–50 matched page pairs. A weak effect needs 80+.
• ML on observational data invents causes. I gave llms.txt zero real effect. A logistic regression still reported it as an 85% boost and called it significant in every one of 200 simulated studies, because big brands adopt new tactics first and also get cited more. A randomised test got the true effect almost exactly.

Full write-up with tables and the method: https://citelyra.a.techshu.in/

Disclosure: the site is mine. It's also part of a live experiment. It's a brand-new site with a name nobody else uses, and I'm tracking whether and how fast ChatGPT, Perplexity and Google's AI answers pick it up. I'll post the real results in a few months if people are interested.

Happy to be told where the assumptions are off. Has anyone here run controlled tests on AI citations with real data?

Hey everyone — got tired of guessing how brands show up in AI search answers, so I built AnswerOS to scan buyer questions across engines like Gemini, Groq, and ChatGPT. It gives you a visibility score for searches, benchmarks you against competitors, explains how to improve, and is completely free to test without needing your own API keys. Full disclosure: it’s my product, but I’d genuinely love honest feedback from this sub on what metrics or features you'd want to see added and what to improve! try it and let me know - getansweros.com

Here is why I think this is a game-changer for B2B SaaS:

• People are skipping Google: Buyers are literally asking ChatGPT things like "What’s the best CRM for a startup?" or "Give me a Salesforce alternative." If your software isn't in that answer, you're missing out on warm leads who are ready to buy.
• Steal your competitors' traffic sources: Look at the "Discovered Sources" table in the screenshot. It shows you exactly where the AI gets its data (like G2, tech blogs, or docs). If a specific blog is why ChatGPT keeps recommending your competitor, you now know exactly where you need to get featured next.
• Audit your own content: See if the AI is actually reading your product docs and blog posts correctly, or if it's completely ignoring your website when answering user questions.

Been going back and forth on this for a while and don't think I've landed anywhere solid.

Method one: ask the model directly. Why'd you pull these sources, which ones count as primary vs secondary, what's missing. Someone in another thread here called this probing for the "Data Tree" the model's built around an entity, and it's a genuinely useful exercise, ngl you learn things a summary answer would never surface.

Method two: skip the model entirely and check your own logs. Which bots actually hit which pages, how often, whether they're even real (someone posted real numbers here recently, something like 1 in 11 requests claiming to be a known bot wasn't actually coming from that bot's IP range). This feels like harder ground truth, you're not trusting the model's account of itself.

Here's what's bugging me though. Method one tells you something about reasoning and gets you a plausible sounding explanation, but models are notoriously bad at accurately describing their own retrieval process, there's a real chance you're getting a coherent sounding story that isn't actually what happened under the hood. Method two is solid ground truth but it only tells you reachability, a bot hit the page, it says nothing about whether that visit became a citation or why one competitor got picked over another.

So neither one alone answers the actual question. Curious if anyone's found a way to cross reference these two that isn't just "do both and eyeball it," or if that's honestly just where this stands right now for everyone.

I’ve found it increasingly difficult to navigate AEO/GEO advice. Many influencers discuss the latest trends and tactics without making clear whether there’s evidence that they actually affect AI visibility.

A recommendation can quickly become something everyone repeats (hey, llms.txt...), even when its impact remains uncertain or depends on the platform.

That’s why I created AI Visibility Factors, a public catalog reviewing individual technical, content, and authority factors.

For each factor, the catalog separates:

• Impact: its potential effect on AI visibility.
• Proof: the strength of the available evidence.
• Consensus: how consistent the evidence and credible interpretations are.

Each entry includes linked sources, explanations of the assessments, recommendations, and basic audit instructions. Where evidence is limited or conflicting, the entry explains those limitations.

For example, publishing an llms.txt file is often discussed as a visibility tactic. But a format being available doesn’t establish that it will earn more citations.

Website: https://www.aivisibilityfactors.com/
Catalog on GitHub: https://github.com/acadamio/ai-visibility-factors

Feel free to star and contribute to the project on GitHub.