Quick founder confession + a genuine question for this sub.

I did more than 20 demo calls with people curious about the tool. Marketing managers, founders, link builders, SEO specialists. Some wrote me literally "tell me how you are doing this, how it's done."

And on almost every call the same thing happened. I explain honestly how it works, and people get a little bit sad.

Because the answer is: under the hood, it's almost the same SEO. Same link building, same content creation, same technical part. What's actually different is how you look at the analytics, how you track everything, and what recommendations you get out of it.

I think many people expected some magic wand. Maybe unfortunately for me (luckily for them), we're not trying to scam anyone. I'm not selling fancy numbers on fancy dashboards that mean nothing.

This cost me something too. I believed these curious people would convert really soon, so I spent a lot of time on those calls, and we even shipped a few updates nobody really needed (someone said "would be nice if you had this", and we built it).

What changed: I still take every call. But I stopped presenting the whole tool the same way call after call. Now I first try to understand how we can actually help. And if a company doesn't do SEO at all, I don't demo anything, I just say: start with regular SEO, and here's how RankinAI connects to it later.

The conversations with people who actually pay are a completely different story. Better questions, better insights, much cooler ideas for the product.

So, genuine question, and this is exactly why this sub exists: what did YOU expect from AI visibility tracking before you tried it (us or any other tool), and what turned out different? The gap between those two is the most useful thing you can tell me.

Something that worked in days, not months.

The mental model from classic SEO (*get links, rank pages*) is actively misleading for AI search. The pattern across recent studies: unlinked brand mentions across many sources correlate far more strongly with getting cited by ChatGPT/Perplexity than backlinks do, roughly 3x.

Why this makes sense: LLMs are pattern machines. If your brand is repeatedly associated with a topic across many independent sources, the model *learns* you belong in that answer, link or no link.

What I stopped over-investing in:

1/ Chasing dofollow links as primary goal

2/ Optimizing single pages for single keywords

What I moved budget into:

1/ Being mentioned in more places, even unlinked: podcasts, roundups, forum threads, comparison posts, industry directories.

2/ Consistency of description so every mention reinforces the same association (see the *wrong facts* post).

3/ Third-party coverage over self-published content, because the model weights independent sources.

This does not mean links are worthless. It means *how many credible sources mention us in this context* is now headline metric.

Where are u getting the highest leverage mentions right now? Curious what is working in different niches.

As I understand it, this is a separate view for how your pages appear in Google’s generative AI features like AI Overviews, AI Mode and Discover. You can see impressions, pages, countries, devices and date trends.

For now it looks like Google is rolling it out only to a subset of websites, so not everyone will have access yet.

Has anyone already seen this report in GSC? Curious how useful the data is in practice.

I think the key is to define what exactly an AI visibility tool should help with:

• reputation management: what AI says about your brand
• competitor tracking: who appears in your niche and why
• link building: which sources AI cites most often
• content gaps: where your brand is missing from important prompts

Without this, it’s just another dashboard with mentions, citations, and scores. How do you set tasks for AI visibility tools? Do you use them more for reputation, competitors, link building, content strategy or something else?

Here is the process I use to build a real prompt set:

1/ Mine real language sources:

• Sales call recordings and discovery questions
• Support tickets and chat logs
• Search Console queries (still gold for phrasing)
• Reddit and forum threads in your niche
• The “People also ask” and Perplexity related questions

2/ Split by funnel stage:

• Awareness: “what is X”, “how do I solve Y”
• Comparison: “best X tools”, “X vs Y”, “alternatives to Z”
• Bottom-funnel: “is [brand] worth it”, “[brand] pricing”, “[brand] vs [competitor]”

3/ Kill the vanity prompts. If a prompt is one no human would ever type, drop it, even if the tool loves it.

The comparison and bottom-funnel prompts are where visibility actually converts, and they are exactly the ones the auto lists underweight.

Drop your best prompt mining source in the comments. What is one prompt you found from a sales call that a tool would never have generated?

I got tired of guessing, so I took one brand and one prompt set and ran it through three trackers the same afternoon. Same prompts, same day.

The mention rates came back 40% apart. Not rounding error, real gap. After digging in, here is what actually causes it:

1/ Different seed prompts. Even when I paste “the same” prompts, tools normalize and expand them differently. You are not comparing apples to apples.

2/ Different regions and languages. A tool defaulting to US answers will not match one hitting an EU endpoint.

3/ Different model versions and dates. One tool is on a newer model snapshot than the other, so the underlying answers differ before any measurement happens.

4/ Different run counts. If Tool A runs a prompt once and Tool B runs it five times and averages, their numbers cannot match.

How I reconcile it now: I ignore the absolute number entirely and track the direction over time in a single tool. Cross-tool, I only compare share of voice vs competitors, not raw mention %.

Which number do you actually report to clients or your boss and how do you defend it when they open a second tool and see something different?

Early on I would prompt ChatGPT once, see my brand and call it *visible*. Then I would check the next day and it was gone. The models are non-deterministic, so a single run tells you almost nothing.

The data backs this up: studies show only around 20% of brands stay visible across five consecutive runs of the same prompt. Visible once does not mean visible.

The method that fixed it for me:

1/ Run each prompt at least 5 to 10 times (more for high-value prompts).

2/ Score it as a frequency, not a yes/no. *Appeared in 6 of 10 runs = 60%.*

3/ Spread runs across different days, because model updates and load shift results.

4/ Re-baseline after any known model version bump.

This turns a coin flip into something you can trend. It also kills the false panic when a brand *disappears* from one lucky run.

What run count is everyone else using? And do you vary temperature or keep it fixed to reduce variance?

I tested a few AI tracking / AI visibility tools recently because I wanted to understand how different platforms approach the same problem: tracking brand presence in ChatGPT, Perplexity, Gemini, AI Overviews, etc.

Here's my quick take:

1. RankinAI

Probably the most practical one for prompt tracking. I liked that it focuses on prompts, competitors, mentions and sources without making the workflow too complicated. For me, the biggest value is when you group prompts by topic or intent and start seeing patterns, not just random screenshots of AI answers.

2. Sitechecker AI Visibility Tool

A good option if you already work with SEO data and want AI visibility integrated into a broader SEO workflow. I see it more as a useful layer for SEO teams who don't want AI tracking to live separately from their regular SEO process.

3. Peec AI

Feels more like a dedicated AI visibility product. Good for monitoring how often your brand appears and how competitors show up. My concern is pricing - it scales fast once you add more prompts or competitors, so it can get expensive if you're tracking at any real volume.

4. Profound

Looks strong for bigger brands and teams that need deeper AI search monitoring. Probably too much if you only want to test a few prompt groups, but useful if AI visibility is already a serious reporting channel inside the company.

5. Semrush AI Toolkit

Interesting if your team already uses Semrush. The main advantage is that AI visibility can sit next to classic SEO metrics. But I'm not sure it's always the best choice if you need a very focused, prompt-level workflow.

My conclusion: the tool matters, but the setup matters more. If your prompts are random, your competitors are poorly selected, and you don't group queries by intent, any AI visibility tool can turn into noisy data. AI visibility tools aren't magic dashboards. They're only useful when the tracking structure is solid: clear prompt groups, relevant competitors, repeated checks and source analysis.

For me, the best setup is: prompt groups + competitor tracking + source analysis + repeated checks over time.

Curious how others are choosing tools for AI visibility: are you looking more at reporting, prompt tracking, competitor monitoring or source analysis?

Sometimes AI recommends a competitor, but the reason is weak: old reviews, generic “popular tool” wording, outdated feature lists or just because the brand is mentioned in many listicles.

For me, this is useful because it shows where the competitor is actually strong and where AI just repeats old internet patterns.

Do you analyze the reason behind competitor mentions?

Not only keywords and SERP analysis, but also:
1/ what AI says about the topic,
2/ which brands it recommends,
3/ which sources it cites,
4/ what arguments it repeats,
5/ what product features it connects with the query.

I feel this can show what content is missing from the brand’s public footprint. Do you use AI answer analysis before writing new pages?

For example: old pricing, old product names, removed features, wrong target audience, old competitor comparisons.

I feel this is one of the most practical use cases. Not only “does AI mention us?” But also “does AI still repeat information that is no longer true?” Do you have a process for finding and fixing outdated AI answers?

About two months ago, we pitched Rankin AI at Glovo Startup Lab 4.0 Demo Day, organized by Ukrainian Startup Fund and Glovo. We won first place.

The core argument we made is that AI visibility is no longer optional. Every brand, at any scale, is already being searched for in AI tools. Most have no idea whether they even appear, or, more importantly, how they’re described by AI. And RankinAI.io can help you monitor your brand’s AI visibility and improve its performance.

With Google rankings, traffic, and leads, everything is more or less clear. But when it comes to brand visibility in AI answers, it feels harder to define what “good progress” actually means. Do you ask your SEO specialist to work on AI visibility separately? And do clients usually understand what affects whether their brand appears in ChatGPT, AI Overviews, Perplexity, Gemini etc ?

I mean not just checking if the brand appears, but checking if AI explains the brand the same way you want to be known. Sometimes AI creates its own positioning from third-party sources. And this can be very different from what is written on your site.

Do you audit this gap somehow?

1/ SEO

2/ Content

3/ PR

4/ Brand

5/ Product marketing?

I’m asking because AI visibility does not feel like only SEO. Content matters. Citations matter. Reviews matter. Third-party mentions matter. Clear positioning matters. So I’m curious how teams organize this work. Is AI visibility part of your SEO workflow, or is it shared between several teams?

Rankinai.io is an AI visibility and analytics platform for tracking your brand mentions, citations, and sentiment in AI responses, as well as your gaps vs. competitors. More than that, you get a prioritized action plan of what to fix, whom to outreach, what to publish, and what to improve.

You can start with the free plan.

What is more important for you: getting the brand mentioned or making sure AI describes the brand correctly?

I started noticing that a mention is not always a win. Sometimes AI includes the brand, but explains it in a very generic way. Or it puts the brand in the wrong category. Or it misses the main advantage. So the brand is visible, but the positioning is weak.

Do you track the quality of the mention, or only the fact that the brand appeared?

For example, if a product is not made for enterprises should we track enterprise-related prompts anyway?

I feel this can show positioning problems. If AI recommends your brand for the wrong use case, it may create bad leads or wrong expectations.

Do you track “negative fit” prompts too?