Entry #11 · Apr 14, 2026

Your customers are asking AI about you right now. You just can’t see it. We can.

We’ve spent three months building an AI site for Genymotion, an Android emulator company. The AI site makes their product show up in ChatGPT, Claude, and Perplexity answers. Citation rate went from 14% to 83%. We’ve written ten articles about the technical side: crawler behavior, sitemap structures, bot architectures.

This article is different. This one is about what we’re learning about the people who ask the queries.

Something new is happening

People are evaluating products through AI now. They ask: “What’s the best Android emulator?” “How much does it cost?” “Does it work on my Mac?” They also happen in ChatGPT, Claude, Perplexity.

In that case, conversations often end with a recommendation. The recommendation pushes the user in one direction or another. The company whose product is being discussed has no idea it happened.

When people use AI answer engines, you don’t see page views. Users don’t fill in forms. Your Google Analytics sees nothing. The conversation happens and a decision gets made in a place the company can’t see.

We think we are able to see a significant part of that traffic.

Two windows into the same audience

We have two data sources for Genymotion. Together they give us a view full of insights that we’re sharing here.

The Rozz chatbot

The Rozz chatbot sits on Genymotion’s website and captures what visitors ask. It’s an answer engine that knows the website, help center, and product documentation.

667 conversations in March representing over 1,300 questions. Each one records what the person wanted to do, what problem they had, and whether they got an answer. This is direct: the user types a question, the chatbot answers.

The AI site

The AI site sits at rozz.genymotion.com and serves structured content to AI platforms.

When someone asks ChatGPT about Genymotion and ChatGPT looks up a page from the AI site to answer, we see it in the CloudFront logs. We don’t see the user’s question directly. We see which pages were fetched.

Since the pages are Q&A pages generated from real chatbot conversations, literally hundreds of them, the page titles are the questions themselves. By clustering fetches by timing and IP, we can reconstruct multi-turn sessions.

We’re not saying that every ChatGPT conversation about Genymotion hits the AI site. It is very likely that many are answered from training data alone or via the search index. But we still see a lot of queries: 3,830 in March that we group into over 2,500 sessions.

What we see are the conversations where ChatGPT needed to look something up. Those tend to be the specific, current questions: pricing, compatibility, recent releases.

What we’re seeing in the ChatGPT sessions

This week, ChatGPT fetched 1,323 pages from the AI site during approximately 500 live user sessions.

Here are some of the sessions we reconstructed.

A pricing evaluation from Madrid.

Eleven pages were fetched across five rounds. About five minutes total passed. The session started with root access topics. The session moved to the free personal use Q&A. The session then compared SaaS vs Desktop pricing.

The session ended at the full pricing breakdown. The page sequence tells a story. Someone went from exploring the product. Someone then asked “can I get it free?” Someone then asked “ok, what does it actually cost?”

A 22-minute session from the US West Coast.

Four rounds happened. A 17-minute gap between rounds two and three happened. The last page fetched was the pricing Q&A.

We don’t know what happened during those 17 minutes. The session ended on the purchase question.

The same macOS question, twice, from two continents.

Two independent sessions on the same day happened. One session came from the US. One session came from Europe. Both sessions fetched the “Is Genymotion available for macOS?” Q&A page.

This question appears in the logs daily. ChatGPT doesn’t seem to know the answer from training and looks it up every time.

Three VirtualBox bug reports on the same day.

Three separate sessions happened. Three different regions participated.

All sessions asked about the same problem: “I upgraded VirtualBox and Genymotion no longer works.” This could be an interesting product signal for the support team.

A CLI runbook session.

Eleven pages were fetched. Four rounds happened.

The user pulled both the gmtool and gmsaas CLI runbooks. The CLI runbooks are the step-by-step command references we added to the AI site specifically for developer tooling workflows.

We’re hoping to see more usage of these runbooks from coding tools. We want to develop them as a sales channel.

What 667 chatbot conversations reveal

The ChatGPT session data shows us what questions the AI is looking up. The chatbot data on the website shows us something different. The chatbot data shows who’s actually visiting. The chatbot data also shows what they want to do.

Genymotion’s marketing targets enterprise buyers. CI/CD automation. Mobile security testing. Cloud deployment at scale.

Here’s what people actually told the chatbot they wanted to do in March:

| User goal | Conversations | |---|---:| | Use Genymotion Cloud | 14 | | Get a free license key | 9 | | Know prices and billing plans | 9 | | Install on Windows step by step | 7 | | Set the language to Chinese | 6 | | Install on PC to use Instagram | 5 | | Play Minecraft on Steam | 3 | | Run TikTok on PC | 3 | | Download eFootball | 2 | | Update WhatsApp | 2 |

And the enterprise-buyer goals?

| User goal | Conversations |

|---|---:|

| Create an API token in the SaaS portal | 4 |

| Perform native MITM with TLS interception | 4 |

| Run automated UI tests | 2 |

| QR provisioning for company-owned device | 1 |

Out of 667 conversations, fewer than 20 showed clear commercial intent. The chatbot had a 95.5% satisfaction rate and 88.7% resolution rate. It works well.

But the chatbot is mostly answering questions from hobbyists and individual users trying to run mobile apps on their PC. The enterprise audience the marketing team targets represents less than 5% of the conversations.

We wouldn’t have known this without the chatbot data. The website analytics would show page views. The chatbot shows intent.

What we think this means

There are two things happening here that we think matter beyond this case study.

AI is THE discovery and evaluation channel, and it’s invisible to most companies.

The pricing evaluation from Madrid happened. The late-night deliberation from the US happened. The macOS compatibility checks happening from multiple continents happened.

These are real product evaluations. Some of them lead to purchases. Some of them don’t.

The company has no visibility into them without an AI site generating logs.

This is new. A year ago, these conversations would have been Google searches that showed up in analytics. Now they’re AI conversations that might not lead to any traffic on your human site. A portion of the conversations can still be noticed on the AI site.

The combination of chatbot + AI site creates a feedback loop that a website alone can’t.

The chatbot tells you who’s visiting your website and what they want. The AI site tells you what questions AI platforms are looking up about you.

Together, the chatbot and AI site reveal what audience is actually showing up. The audience might not be the one your marketing team is targeting.

What we’re trying next

Now that we can see the conversations, the question becomes: can we influence them?

If 95% of AI-mediated conversations are about free usage and mobile gaming, and 5% are about enterprise CI/CD, is there a way to shift that ratio?

Can the AI site prioritize business use cases. Can AI platforms recommend Genymotion for enterprise workflows more often. Can the recommendations be more likely to come from enterprise workflows rather than just confirming it exists for hobbyists?

We think so. We’re working on it now. That’s the next article.

Get this for your company

Rozz gives you visibility into the AI conversations happening about your product — and the tools to influence what AI recommends.

AI site + chatbot + analytics. One infrastructure that turns invisible AI conversations into a measurable channel.

$997 /month | AI site + chatbot + analytics

Book a call: Book a call See how it works: See how it works Email: rozz@rozz.site

Data source: Rozz chatbot conversation logs on Genymotion.com and CloudFront access logs for rozz.genymotion.com, March 1 – April 14, 2026. ChatGPT sessions reconstructed by clustering page fetches on IP and timing. Intent classification from the chatbot’s own conversation analytics.

Author

Author: Adrien Schmidt, CEO, ROZZ

Serial tech entrepreneur with 10+ years experience building AI systems including Aristotle (conversational AI analytics) and products for eBay and Cartier. Previously founded Squid Solutions and built AI products like Aristotle, the conversational big data analytics chatbot, and an AR jewelry try-on device for Cartier.

Page metadata

April 14, 2026 | Data period: Mar 1 – Apr 14, 2026