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Robo.ai Subsidiary Neurovia AI Launches NeuroStream™ Technology Platform to Build Physical AI Visual Data Infrastructure

AIIO
  • Robo.ai Inc. (Nasdaq: AIIO) announced the official release of NeuroStream™, its core AI data processing & compression platform developed by subsidiary Neurovia AI.
  • The platform utilizes a bitmap vectorization algorithm to provide high-fidelity, low-bandwidth, & low-power infrastructure support for visual data generated by physical AI.
  • Internal testing shows NeuroStream™ can compress a 5.5GB 4K 60fps video to 278MB, reducing storage usage by approximately 95% while retaining original resolution & frame rate.
  • The technology offers visually lossless compression to ensure data authenticity for industrial, sovereign, & legal scenarios while lowering energy consumption, storage space, & processing latency.
  • CTO Mansoor Ali Khan noted that global unit data storage prices have increased fourfold since 2026, with every terabyte saved generating $1,000 to $1,500 in direct annual economic benefits.
  • NeuroStream™ converts traditional bitmap logic into vectorized mathematical expressions to reduce costs while preserving critical visual information for AI computation.
  • The platform maintains native format compatibility with zero decompression usage costs, allowing processed files to be directly accessed by existing systems without specific software.
  • Intelligent signal-to-noise ratio improvement enhances machine vision recognition accuracy & computational efficiency on compressed data.
  • The architecture supports low-computing edge deployment on standard commercial devices, enabling processing of hundreds of terabytes on sensors, drones, & mobile nodes.
  • Lossless offline operation ensures data security & privacy for sensitive industries like aerospace, medical imaging, & energy in disconnected environments.
  • Neurovia AI plans to deploy NeuroStream™ in autonomous driving, robotics, smart cities, industrial AI, & global intelligent networks to reduce bandwidth pressure & energy consumption.
  • The platform aims to support the large-scale commercial deployment of the machine economy by making global machine vision networks more efficient, real-time, & intelligent.