Nomadic
US software platform that turns robotics and AV fleet video into structured, training-ready datasets using domain-expert VLM agents.
Nomadic is a San Francisco-based software company building an understanding layer for physical AI, serving robotics and autonomous vehicle teams. The platform sits on top of a team's existing perception stack and converts operational video and multi-sensor data into structured behaviors, validated edge cases, and training-ready datasets. Its vision-language-model (VLM) agents curate, label, and structure footage automatically, turning raw fleet video into usable training data without human review.
Nomadic's domain-expert VLMs process RGB camera frames, lidar point clouds, and vehicle telemetry jointly within a single model rather than as separate streams reconciled downstream. Engineers can query raw video in plain English, for example retrieving every 18-wheeler cutting into the ego lane with under three seconds of headway, and receive timeboxed, labeled events in return. The platform automatically surfaces rare, high-value events from large video libraries, giving automotive, construction, and robotics teams the specific training data they need. Nomadic reports that its custom agents reach production-grade accuracy on use cases that defeat off-the-shelf models and reduce annotation budgets by 80% or more compared with manual baselines.
The platform is used by teams including Zoox, Mitsubishi Electric Automotive America, and Zendar. Nomadic announced $8.4 million in funding led by TQ Ventures, with participation from Pear VC, Jeff Dean, and angels and executives from OpenAI and Google DeepMind, in a March 31, 2026 disclosure. Its team includes specialists in computer vision, large-scale AI optimization, and production machine learning, with backgrounds at Amazon, Snowflake, Microsoft, and IBM Research.
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https://www.nomadicai.com/HEADQUARTERS
United States
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North America
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Import AI 453: Breaking AI agents; MirrorCode; and ten views on gradual disempowerment
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