SafeWorld Raises $12.2M to Scale Robot Safety Testing

SafeWorld, an AI lab focused on robot safety simulation technologies, has officially launched after securing $12.2 million in seed funding. The round was co-led by Shine Capital and a16z Speedrun, with participation from Box Group, Carnegie Mellon University Endowment, and other prominent investors. The company aims to address the critical need for scalable safety testing as billions of AI-powered robots are set to be deployed in human environments.

The challenge SafeWorld tackles is the lack of efficient safety testing methods for AI-driven robots. Traditional physical tests are slow, expensive, and unable to cover the vast number of potential edge cases. SafeWorld's platform offers a solution by simulating thousands of variations of dangerous or unexpected scenarios, allowing enterprises to evaluate robot behavior without real-world risks. The platform is designed to be user-friendly, enabling teams to build scenarios from past incidents, safety standards, and robot logs.

SafeWorld was co-founded by a team with extensive experience in AI, robotics, and entrepreneurship. Dr. Ding Zhao, Director of the Safe AI Lab at Carnegie Mellon University, brings over 17 years of research in safe autonomous systems. Kyle Wong, a veteran founder, previously led Pixlee and served as CEO of StartX. Simo Rachidi, another co-founder, has a background in enterprise-scale data pipelines and ML systems at Salesforce Einstein.

The company is already collaborating with major automotive OEMs, warehouse automation leaders, and medical device manufacturers. Investors in the oversubscribed round include Ovo Fund, Valkyrie, and founders and executives from leading tech companies like NVIDIA and Google DeepMind.