Netradyne, a SaaS provider of artificial intelligence (AI) and edge computing, has announced the successful closing of a $90 million Series D funding round. Led by Point72 Private Investments and with participation from Qualcomm Ventures and Pavilion Capital, the funding round will help the company expand into new markets and accelerate the development of next-generation AI systems.
The investment follows unprecedented growth in the commercial fleet industry, with Netradyne’s cutting-edge technology revolutionizing driver safety and fleet performance.
With a strong presence in the U.S., Canada, Mexico, Germany, the U.K., Australia, New Zealand, and India, Netradyne plans to use its newly acquired capital to expand into Europe, Ireland, and Japan.
The company will also invest in R&D to develop new AI models to enhance autonomous vehicle systems through strategic partnerships.
Avneesh Agrawal, CEO and Co-Founder at Netradyne stated: “This funding provides us with the resources to accelerate growth, expand our technology capabilities, and deliver even greater value to our customers worldwide. With this support, we are poised to scale our innovations globally, deepen our impact, and continue advancing safety and efficiency across the transportation industry, redefining what’s possible for fleets and communities alike.”
Netradyne: Excellence in AI-powered Autonomous Driving
The company’s flagship product, Driver.i, is an AI-powered dashcam system that utilizes edge computing and real-time data analytics to monitor and enhance driver behavior. Unlike conventional fleet management solutions, Driver.i not only identifies risky driving patterns but also actively reward safe driving behaviors.
Netradyne’s advanced fleet safety solutions provide a comprehensive assessment of driver performance by analyzing 100% of drive-time data. The Driver.i system integrates quad-view dashcams and edge computing to deliver real-time information. This allows a fleet to reduce accident rates by nearly 50%.
The company has amassed over 18 billion miles of vision-analyzed driving data, which refines its AI models to achieve 99% accuracy in detecting driving patterns. Such a dataset serves as a foundation for future innovations in autonomous driving and driver assistance technologies.
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