Building the data infrastructure to train the next generation of Physical AI and Vision-Language-Action models
AI is shifting from text to the physical world. Data infrastructure is missing.
VLA and Physical AI systems require petabyte-scale ego-centric video with high-entropy edge cases
Most datasets are small, static, and curated — missing the edge cases that cause real-world failures
There is no scalable "Data Infrastructure Layer" for Physical AI that supports semantic search and reasoning
From Capture to Training-Ready Intelligence
Turnkey multi-sensor continuous capture at scale across 500+ vehicle network
Action-level ground truth with near-miss, cut-in, and unsafe-proximity events
Semantic retrieval across video archives with natural-language queries
Intent and behavior prediction from vision + telemetry fusion
Industrial-grade vision and telemetry at scale
Semantic search to retrieve scenarios, incidents, and edge cases instantly using natural-language queries
Launch Novus →Multi-sensor capture, playback, QA, and export-ready validation workflows
Action labeling at scale with timestamps and severity scoring
Intent classification and behavior prediction from vision and telemetry
India's real-world complexity generates edge-case rich data at scale
Mixed traffic with 2W/3W/4W/trucks + pedestrians in urban density, highways, and industrial choke points creates edge cases that occur more frequently than staged environments
500+ trucks and 2W last-mile network providing continuous, action-dense real-world sequences across repeatable baselines and diverse conditions
Active trials with StackAV and paid pilots with logistics partners create a data flywheel from continuously running fleets
Physical AI is the next frontier of Generative AI
GenAI Global Market (2025-2032)
Embodied/Physical AI (2025-2030)
Achievable Market (2026-2030)