NomadicML, a startup focused on organizing and analyzing video data from autonomous vehicles and robotics systems, has raised $8.4 million in a seed funding round at a post-money valuation of $50 million. The round was led by TQ Ventures, with participation from Pear VC and Jeff Dean.
The company has also gained early industry traction, securing customers such as Zoox, Mitsubishi Electric, NATIX Network, and Zendar. Additionally, it recently won first prize at NVIDIA GTC’s pitch competition.
Founded by CEO Mustafa Bal and CTO Varun Krishnan, the company is building a platform that converts large volumes of video footage into structured, searchable datasets using vision-language models. The founders, who met while studying computer science at Harvard, previously worked at companies such as Lyft and Snowflake, where they encountered challenges in handling large-scale video data.
The platform is designed to help organizations extract actionable insights from massive video archives, enabling them to identify rare edge cases, monitor fleet performance, and feed specific scenarios directly into AI training pipelines. This addresses a key bottleneck in the development of autonomous systems, where large volumes of data often remain underutilized due to the complexity of processing and analysis.
Describing the product’s value, Bal noted that the platform provides companies with meaningful insights from their own operational footage, helping advance the development of autonomous systems rather than relying on generic datasets.
From an investor perspective, Schuster Tanger highlighted that building such infrastructure internally can distract companies from their core focus of developing autonomous machines, making NomadicML’s offering particularly valuable.
The company plans to use the newly raised capital to onboard additional customers and further enhance its platform capabilities, as demand grows for scalable solutions that can manage and interpret vast amounts of video data generated by autonomous vehicles, robotics, and other physical AI systems.
