Safeworld raises over $12M seed to build safety validation platform for generative-AI robots

Safeworld, a startup founded by Dr. Ding Zhao—director of Carnegie Mellon University’s Safe AI Lab—alongside Kyle Wong and Simo Rachidi, emerged from stealth with a seed round exceeding $12 million. The round was led by Shine Capital and a16z Speedrun, with participation from Box Group, Carnegie Mellon University Endowment, Innovation Endeavors, and SV Angel.

The company’s platform evaluates robotic control systems by running thousands of simulations populated with realistic human models to test unpredictable edge cases, such as humans tripping or carrying boxes around blind corners in factories.

Safeworld’s approach mirrors internal validation tools used by robotics companies like Tesla and Wayve, but the founders argue that third-party validation will be necessary for competitors to share safety data and build public trust.

Vishal Dugar, CTO of Gritt Robotics—which is partnering with Safeworld—said verifying robotic safety empirically is essential because formal proofs are often infeasible for complex, real-world systems.

Safeworld’s simulations aim to address variability in human appearance and behavior, including differences in posture, movement, and clothing, to ensure robots respond appropriately across diverse scenarios.

The startup is still determining whether its product will be a platform for external users or a services-based offering, but its founders assert that demand for safety validation will drive adoption as generative-AI robots move toward deployment.