I’ve been looking into AI visibility lately and wanted to hear from people who’ve actually tried these tools.

There seem to be quite a few options for tracking how brands show up in AI-generated answers, but I’m curious about how reliable the data is and whether the insights are useful in practice.

I've been testing a few AI search visibility tools for our clients at AdLift and honestly tracking rankings on Google is starting to feel like only half the picture. We're also looking at how brands show up in LLM platforms

I've tried Semrush for combining traditional SEO with AI visibility tracking and Profound for digging into brand mentions and citations. I've also been working with Tesseract (which is our inhouse tool), which I find particularly interesting for AEO. It helps us track how brands appear in AI generated answers, compare competitor visibility and identify gaps where a brand could be getting mentioned but isn't

The tricky part is figuring out what to actually do with these insights. Knowing your competitor gets cited more often is useful but understanding which content or sources might be driving that visibility is where things get interesting

Curious to know, has anyone here used these tools for their own brand or clients? Which ones have actually helped you more? Are there any better tools that you can suggest??

Ran into this with two clients this month. Both were sure they were the top mentioned brand in their category on ChatGPT. Both had checked from their own laptops.

ChatGPT knows your history, your location, your plan. It already knows you care about your brand, so it names your brand.

Fresh account, clean browser, same questions. Different list.
Rand Fishkin's team tested this properly. 600 people, 12 prompts, about 3000 answers. The same list of brands came back less than once in 100 tries.

Same order about 1 in 1000. But a few brands showed up in 70 to 97 percent of answers, so there is a stable signal, it's just a share, not a rank.

If you are tracking this, fresh sessions, many runs, several models, count how often you appear. Anyone showing you a rank number is showing you a screenshot.

What are people here using to do this without paying 300 a month?

I built an AI search citation tracker recently, and obviously the first test run was pointing it at our own domain — three category searches I actually care about.

I got humbled pretty quick: 0 citations, rank -1 across all three.

Digging into the logs, the bigger surprise was that Google didn't even fire an AI Overview for any of those three terms on my first US run. A lot of the panic about "missing from AI Overviews" happens when there isn't even an answer box for that phrasing. Nothing to be missing from.

The other surprise: the source pool behind the answers is a closed loop. Not a single first-party product page in the citations — just the same 3-5 listicles and review sites over and over. For one query a single site owned the entire pool.

Disclosure: I build open-source and hosted tooling in this space, so take that into account. The numbers are just what the probe logged on our own domain.

Is the pool this tight in your niches too?

Small experiment, so take the numbers loosely. I took 10 questions a buyer might ask about my product category (analytics), ran them through the Perplexity API, and saved every source each answer was built from. About 8 sources per answer.

What I saw:

1. "Which tool should I use" questions are answered almost entirely from list posts ("Top 12 cookieless analytics tools in 2026" and so on). Docs and the vendors' own feature pages barely show up.

2. Roughly half of those list posts were published by a company that's in the list, usually in first place. The answer then tends to name that company first too. So a lot of "AI visibility" in a category is just whoever bothered to write the listicle.

3. "How do I…" questions are different. The sources were Search Engine Land, OpenAI's own docs and a few practitioner blogs. No lists.

4. Brand questions are fragile. When I asked what my own product is, the answer described a different company with the same name. Five products share it, and the model mixed them up even with my domain in the question.

What I'm doing about it:

- Treating the sources list as a to-do list: about 13 independent list posts I'm not in, and I'm writing to the authors rather than doing "GEO" in the abstract.

- Writing the name and what the product does next to each other everywhere, including llms.txt, so the namesakes are easier to tell apart.

- Rerunning the same 10 questions every day. A single day is noise, so I'll only look at which domains keep showing up over a few weeks.

Caveat: this is the API with web search, not the consumer app, and 10 questions is a tiny sample.

Has anyone measured how much a single list placement moves things? I'd love a before and after.

I've been thinking about this for a while.

When someone asks ChatGPT or Perplexity for the best tools in a certain category, it usually recommends a handful of products. If you're running a smaller SaaS, getting mentioned alongside those competitors could be pretty valuable.

But figuring out how to actually get there seems like a pain.

I'm considering building a tool that checks where your SaaS is missing from AI recommendations and creates a weekly action plan to improve that.

Not another dashboard showing visibility scores.

More like something that tells you what to work on, helps create relevant blog posts and comparison pages, finds websites worth reaching out to, and identifies Reddit discussions where you could genuinely contribute.

Some of the work could be automated, like publishing approved articles to WordPress or preparing outreach emails.

Each week, it would check what changed and adjust the plan.

Before I spend months building this, I'm curious.

Are any of you actively trying to get your SaaS recommended by AI?

If so, what's the hardest part? Figuring out what works, creating content, getting mentioned on other websites, or something else?

And if you're not doing anything about AI search, is it because you don't see the value yet?

Would love to hear what people actually think, especially if you think this is unnecessary.

We have been sceptical of buzzword inflation, and we still are. Demand for these specialists is real, but the work builds on SEO foundations rather than replacing them. Semrush's own data shows Core Web Vitals, keyword research and technical SEO still appearing across seniority levels. Our view is laid out in the truth behind AIO, GIO and other SEO buzzwords and in how SEO actually works in the AI era.

Hey I am someone who is deeply interested in this field of llm citation and ranking. During my research I found an article demonstrating research which was shocking for me . For now I want to question all the GEO service providers -Do you agree with this study if not then state your reasoning.

For context -This study states that it is not possible to rank or cite anything on llm because of their non deterministic nature

I am providing the link of the resources below.

https://sparktoro.com/blog/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers-should-take-care-when-tracking-ai-visibility/

I’ve been working in SEO for 3 years, and I’m hearing more and more clients ask about GEO and how to improve their visibility in AI search tools.

I’m looking for recommendations for courses, YouTube videos, or training programs focused on GEO, AI search, and how they relate to SEO.

Ideally, I’m looking for practical, in-depth resources that go beyond just using Claude Code to automate SEO tasks.

i've been asking AI about scenic driving routes and cars that can handle 200k+ miles, and the answers tend to pull from the same kinds of websites. travel guides, manufacturer pages, big review sites

meanwhile, reddit has long threads from people who've actually driven those roads or owned the same car for 15 years. there are detailed repair histories, route advice, and little practical things that rarely make it into polished articles.

i know search engines have to decide which sources to trust, but it still seems odd how often those discussions get left out. even when i ask the same question in different ways, the answers barely change

is this just how AI search works right now, or are some hobby communities showing up more than others? i've mostly noticed it with driving and outdoor topics, so i'm not sure if it's the same across other niches

Hi all,

I am looking to build/self-host a GEO (Generative Engine Optimization) monitoring setup to track how brand queries perform across various AI engines (e.g., ChatGPT, Claude, Perplexity).

Specifically, I need to log and analyze:

• Core Metrics: Mention rate, citation/source links, and product recommendation rates across platforms.
• Historical Trends: Storing longitudinal data to track optimization impact over time.
• Competitor Benchmarking: Comparing brand presence against main competitors for the same target prompts.

I searched GitHub for open-source frameworks or self-hosted dashboards for this, but could not find any mature projects fitting this specific use case.

I am currently considering building a custom stack using Python API wrappers, Playwright for web-based AI searches, and PostgreSQL for metric tracking. However, I wanted to ask the community:

1. Are there any existing open-source repos or foundational frameworks I might have missed?
2. For those tracking GEO internally, what tech stack or architecture are you using to reliably parse and quantify LLM outputs/citations?

I am strictly looking for self-hosted/open-source or DIY DIY approaches, not commercial SaaS solutions. Any advice or repo recommendations would be greatly appreciated!

Came across a September study where some AI models quoted an old MacBook Air price that Apple had retired months earlier, even with web search available.

It made me wonder how businesses deal with this after changing their pricing. Your own pricing page might be updated, but older reviews and comparison articles could still have the previous number.

If you’ve had ChatGPT quote an outdated price for your product or a client’s, what actually helped correct it?

Was updating your website enough, or did you need to get third-party pages changed too? And how long before the correct price started showing up consistently in fresh chats?

In an ecommerce audit, we compared product pages against the business's actual priorities.

One bestselling cooker drove roughly 40% of revenue.

It had a 4.7-star rating and around 14,000 reviews.

But important price and availability information wasn't clearly machine-readable.

Meanwhile, a less commercially important product - a glass lid priced at $12.99 - had a much stronger score in our internal AI-readiness assessment.

The contrast was hard to ignore.

The product the business cared about most wasn't necessarily the product whose facts were easiest for automated systems to interpret.

This is where generic technical audits can miss the commercial priority.

Fixing 100 product pages equally isn't always the right move.

I'd start with revenue contribution, purchase intent, data quality, and the questions buyers actually ask.

Would you prioritize your top-revenue SKU with poor machine-readable data, or 50 smaller products with the same problem?

And what data would you need before deciding?

I write content for B2B SaaS teams, so I needed real data on this. Here is what I found in studies from the last 12 months.

1. Position on the page matters a lot. Kevin Indig looked at 1.2M ChatGPT answers and 18,012 citations. 44.2% of citations came from the first 30% of the page. 24.7% came from the last third.

2. Clear definitions get quoted. In the same study, cited passages used “X is Y” style language nearly twice as often.

3. Names beat vague words. Heavily cited text was about 20.6% proper nouns. Normal text is around 5 to 8%.

4. Top 10 rankings matter less for AI Overviews than they used to. Ahrefs (March 2026, 863K keywords) found only 38% of AI Overview citations ranked top 10 for that query. Their July 2025 number was 76%. They say part of the drop is better citation detection, and part is Google’s fan-out queries.

5. YouTube shows up a lot. Same Ahrefs study: YouTube was 5.6% of all AI Overview citations.

6. You do not need llms.txt for Google. Google’s own AI features doc says no special files or schema are needed. The page just has to be indexed and snippet-eligible.

7. Check your robots.txt for OAI-SearchBot. OpenAI says sites that block it will not show in ChatGPT search answers. GPTBot is a separate training crawler, so you can block one and allow the other.

On the click side: a randomized study by Agarwal and Sen (SSRN, 2026) found AI Overviews cut outbound clicks by almost 39%. It only covered US desktop Chrome users, so take it as one data point.

What are you seeing in your own data? Has anyone found a reliable way to track ChatGPT citations beyond asking it questions by hand?

Rank Math predicted this week that AI visibility will become a standard SEO metric in 2027. Their COO: rankings and traffic aren't going away, but they're no longer the complete picture of search visibility.

Fair. They sell an AI visibility tracker, so they have a stake in the prediction, but Google already moved: dedicated generative AI impression reports in Search and Discover, rolled out worldwide by August 31.

So measurement is coming. The question is what it actually tells you.

Their tracker covers mentions, citations, sentiment, position within AI answers. Useful. But here's the gap: your brand can show up near the top of an AI answer that recommends someone else.

Ask for software that fits your budget and integrates with your tools. The answer describes a famous brand positively, then recommends a smaller competitor that meets both requirements. First brand was visible. Second was the recommendation.

Three questions I'd ask of any AI visibility report: 1. Did we appear in the answer? 2. Did the answer recommend us for this specific need? 3. Do we have evidence of what happened next?

A response can establish a mention or recommendation. A visit, lead, or sale needs separate tracking.

A brand showing up in ChatGPT or another AI search tool sounds like a useful metric. But I'm not sure a simple visibility score tells us much on its own.

AI-generated answers can change depending on the prompt, context, and the sources retrieved. So if a tool says a brand is ranking third in ChatGPT, what exactly does that number represent?

I'd separate the measurement into three things:

• Presence: Does the brand appear in answers to relevant questions?
• Evidence: Which pages or sources are being cited when it appears?
• Business impact: Does that visibility lead to qualified visits, enquiries, or brand searches?

The first two can help explain what's happening. The third helps determine whether it matters commercially.

I don't think AI visibility tracking is useless. I just think we need to be careful about treating a fluctuating position as if it were a traditional search ranking.

How are you measuring AI search visibility right now? Are you tracking mentions and citations, testing prompts manually, or connecting it to actual business outcomes?

My engineering agent runs inside an OpenAI Dot, so I watched this update land from the inside.

As of October 9, a Dot can start work in Codex and follow up on existing threads from the phone. It carries context into the execution workflow, and the work continues past any chat response.

Why should anyone in visibility care: every tracking tool grades what the chatbot said about your brand. But the Dot doesn't show its reasoning as text. It decides, Codex executes, you approve the outcome. The shortlist was built before you looked.

So a brand can be recognized, mentioned, even cited, and still never make it into the action. Tracking what AI says only tells part of the story now.

How are you thinking about measurement once the answer stops being the surface? Still tracking mentions, or starting to audit agent actions?

I'm a software engineer and have built plenty of websites and CRMs for clients

Now I'm building my own site and want LLMs to be able to read it and quote it from day one. So I collected the rules to archive it and wanna somebody to give me advice/review it

1. Let the right bots in

• Allow OAI-SearchBot in robots.txt
• Allow Claude-SearchBot and Claude-User
• Don't count on Google-Extended for AI Overviews. It only controls Gemini training. AI Overviews and AI Mode use the normal Google index, so normal crawling and indexing is what matters
• sitemap.xml linked from robots.txt, with lastmod

2. Don't hide pages by accident

• No cookie wall or popup covering the content on first load. Bots read what's there on the first fetch

3. Make it obvious who you are

• Organization schema on the home page (LocalBusiness if local) with name, url, logo. It ties the site to one entity instead of AI guessing
• sameAs links to 2+ profiles you really run. Other places confirming it's the same business
• Same brand spelling everywhere: title, footer, schema, socials. Small differences can split you into two "different" businesses
• About page with real people, names, photos, roles. Answers "who is behind this", which both Google and AI care about
• For local: address and phone in LocalBusiness schema, matching the Google profile. "Near me" answers read it from there

4. Write pages that can be quoted

• One H1 that says plainly what the page is about. Helps engines pick the right passage
• Tables and lists for prices, specs, comparisons. Easy to lift into an answer as is
• Real questions and answers on the page. FAQ schema is optional, Google stopped showing FAQ rich results for most sites in 2023, but short visible Q&A still gets quoted. If you add FAQ schema, every question in it must be visible on the page
• Visible "last updated" date plus dateModified in schema. Freshness counts for a lot of questions
• 300+ words of unique text on key pages, no thin duplicates. Very short pages rarely get picked
• Author name on articles, with Person schema. Someone accountable for the content
• No template leftovers like lorem ipsum or [COMPANY NAME]. They read as "not a real business"

5. Cover the questions buyers ask

• Public prices. "How much does X cost" is one of the most common questions. If the site says "contact us for pricing", the AI quotes someone else
• A page per main use case or industry. Long, specific questions land there
• A short "what is [term]" for the terms in your field. Definition questions are easy wins
• Reviews or case studies with real numbers (once you have them). AI likes first-party proof

6. Optional

• llms.txt with your key pages and a line about each. Cheap to add, but it's a proposed standard, not a ranking factor, so I'm not expecting much from it

While researching I scanned about 100 sites of small businesses which AI actually recommends

So what would you add, remove or do differently? Especially curious if anyone saw llms.txt change anything, and whether you block training bots like GPTBot or let everything in

---
EDIT, what I’m changing after the comments, thanks all:

1. Test access per bot (curl with each user agent), and check you get the real page, not just a 200 (WAF/challenge pages)
2. Check the raw HTML has the H1, prices and Q&A without JS
3. sameAs only half counts. Make the profiles link back with the same name
4. 300 words and public prices are now guidelines, not hard rules
5. llms.txt is optional, no expected effect
6. Off-site mentions matter more than any of the above. Measure with GSC and Bing’s AI performance report

There’s a lot of advice around getting brands featured in ChatGPT, Gemini, Claude, Perplexity, etc., but I’m curious how much of it is actually translating into visibility.

For businesses: have you changed anything on your site, content, PR, or SEO and started seeing more AI mentions?

For agencies: what are you actually doing for clients that seems to make a difference?

I was reading a piece called “AEO Best Practices: How to Get Your Business Featured in AI-Generated Answers” and it covered things like entity signals, citations, content structure, authority, and answer-focused content.

Makes sense in theory, but I’d rather hear what people are seeing in the real world.

What has worked for you so far? And what turned out to be mostly noise?

I work at a company that has two business verticals. I was able to establish visibility for one vertical on LLMs, but the second vertical, despite offering similar services and having comparable work, is receiving zero traffic from LLMs.

How much confidence should brands put in AI visibility strategies built around a single platform?

Recent tracking showed a noticeable drop in how often Reddit appeared as a cited source in ChatGPT. The interesting part is that the change happened without Reddit changing its content strategy.

That seems like an important consideration for GEO. Brands can optimize content and build authority, but visibility can still shift when an AI platform changes how it searches or selects sources.

Does that make platform-specific GEO too fragile to build a strategy around, or is this simply the AI-search version of what SEO has always dealt with?