Robo.ai Inc. announced on 14 May 2026 that its wholly-owned AI data processing and compression technology subsidiary, Neurovia AI, has officially released its core technology platform, NeuroStream. Designed for the machine economy era, the platform utilizes a bitmap vectorization algorithm to provide high-fidelity, low-bandwidth, and low-power infrastructure support for the massive visual data generated by physical artificial intelligence. Mansoor Ali Khan, Chief Technology Officer of Neurovia AI, detailed the platform’s technical architecture and application cases.
To demonstrate the practical performance of NeuroStream, Neurovia AI has published comparative case studies on its official website. According to internal testing, a 5.5GB 4K 60fps original video processed through NeuroStream can be compressed to a file size of 278MB, reducing storage space usage by approximately 95%.
These tests indicate that NeuroStream significantly reduces file size while fully retaining core visual information such as the original video’s resolution and frame rate. This visually lossless characteristic ensures that the substantially compressed data continues to provide a clean and complete data source for subsequent machine vision and AI computations. This capability meets the strict data authenticity requirements of industrial, sovereign, and specific legal application scenarios, while also generating clear economic benefits for enterprises in terms of energy consumption, storage space, and processing latency.
Chief Technology Officer Mansoor Ali Khan noted that global unit data storage prices have increased approximately fourfold since 2026. According to industry estimates, every terabyte of data storage saved generates a direct economic benefit of USD 1,000 to USD 1,500 annually for an AI customer, in addition to indirect benefits related to transmission efficiency, energy consumption, data integrity, and security.
Through AI-native compression, intelligent visual optimization, and edge computing adaptation, NeuroStream converts traditional bitmap logic into a vectorized mathematical expression. This approach lowers transmission and storage costs while precisely preserving the critical visual information necessary for AI computation, offering an effective solution to rising storage expenses.
The platform provides native format compatibility with zero decompression usage costs, as processed images and videos maintain their original formats and can be directly accessed by systems without specific decompression software. This full compatibility with existing conventional video workflows substantially reduces system integration and friction costs for enterprises.
Furthermore, NeuroStream optimizes data quality and enhances machine vision recognition accuracy by intelligently improving the data signal-to-noise ratio during processing. This process aids in increasing the computational efficiency of AI algorithms and ensures that machines maintain high recognition accuracy on the compressed data.
The platform features an architecture adapted for low-computing edge deployment, enabling standard commercial computing devices to efficiently process hundreds of terabytes of data. This characteristic makes the platform suitable for deployment on edge sensors, drones, and mobile terminal nodes with limited computing resources. Additionally, the product ensures data security and privacy protection through lossless offline operation, allowing it to function independently in disconnected environments to meet the data compliance requirements of highly sensitive industries such as aerospace, medical imaging, and energy.
Moving forward, Neurovia plans to progressively introduce the NeuroStream platform to core application scenarios including autonomous driving, robotics, smart cities, industrial AI, and global intelligent networks. As the deployment scale of global edge computing nodes expands, NeuroStream is expected to effectively reduce network bandwidth pressure and overall energy consumption in data centers. This will enable global machine vision networks to become more efficient, real-time, and intelligent, providing solid infrastructure support for the large-scale commercial deployment of the machine economy.
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