INDUSTRY AFFILIATIONS
We turn 3D assets into training data, perception models, and lifecycle intelligence — so autonomous systems can operate where real-world data is scarce, dangerous, or impossible to collect.
Before co-founding Zuru Automation, Matthew led North American business development at two computer vision AI startups — working at the front lines of how perception AI gets sold, deployed, and where it consistently stalls. The bottleneck was never the model. It was always the training data.
Matthew holds a BA in Sociology from Stanford University and an Executive MBA from INSEAD's Global Executive MBA program.
Yu-Feng is the architect of Zuru's patented ML method for synthetic data generation from vectorized 3D models — the core technology underlying the platform. He holds a PhD in Mechanical Engineering from MIT and served in the Republic of China Marine Corps, bringing direct military operational context to the defense environments Zuru serves.
Over two decades, he has deployed machine vision AI across manufacturing, clinical imaging, and industrial inspection systems. That breadth — simulation, sensor physics, real-world deployment — is uncommon at the technical founding level and reflects exactly the problem Zuru was built to solve.
Real-world training data is scarce and impossible to collect at scale — especially in defense, energy infrastructure, and industrial environments where sensor data is classified, hazardous, or operationally restricted.
We're raising seed to build the data infrastructure that makes autonomous systems deployable at scale — in defense, energy, and industrial environments where real-world data cannot be collected.
