Co-founder & CTO at Quago
Ran Arieli
I’m the Co-founder and CTO of Quago, where I lead the development of machine learning technology to detect in-game cheating and UA fraud at scale. Previously, I was the Data Science Team Lead at Unbotify. I built and led a team developing a pioneering bot detection solution, later acquired by Adjust and integrated into Applovin. With a background in behavioral analytics and real-time ML systems, I focus on building high-confidence detection solutions for gaming studios worldwide.
Questions & Answers
Why are you excited about your company/product?
What excites me most about Quago is the complexity of the problem we’re solving. Cheating and fraud in games are behavioral patterns that evolve and adapt over time. Traditional detection methods rely on static rules or event-based tracking, but we take a different approach. We analyze sensor-based data such as touch dynamics, accelerometer, and device motion to build a deep understanding of how real players interact with their devices. By applying machine learning to these high-dimensional data points, we can detect anomalies that indicate artificial behavior, coordinated cheating networks, or fake UA traffic. What’s really exciting from a technical perspective is that we're building real-time, high-scale detection models that need to adapt to constantly shifting attack methods, all while maintaining an extremely low false-positive rate. The challenge of keeping models accurate, efficient, and explainable at this scale is what makes working on Quago so fascinating.
How is your team uniquely positioned to solve the problem you're tackling?
There’s no off-the-shelf solution for detecting in-game cheating and UA fraud at the scale and sophistication that gaming studios face today. We had to build our models from the ground up to solve challenges that existing tools couldn’t handle. That’s why choosing the right people for our team is so important. We’ve brought together exceptional thinkers who love solving challenging problems, think outside the box, and aren’t afraid to build something entirely new. Our approach isn’t just innovative, it’s been tested at scale, giving studios the accuracy and reliability they need to detect and act on cheaters with confidence.
