Robo.ai Expands Intelligent Data Capacity to 10,000 Hours

Robo.ai Inc. announced the capacity expansion plan for its intelligent data business segment. Following the delivery of its initial batch of real-world interaction data and the initiation of the revenue recognition process in the first quarter of this year, the company plans to advance this business line into a scaled capacity phase. To achieve a baseline delivery target of 10,000 hours of real-world interaction data in 2026 and to address the artificial intelligence industry's continued demand for structured data, Robo.ai is leveraging regional resources across the Middle East and Asia to establish a cross-regional AI data service network. Building upon the initial commercial processes established with DaBoss.AI, a Silicon Valley-based AI model data service provider, the company is advancing a supply chain system through partnerships based on Middle East-Asia regional synergies to create a data production platform serving the large model and intelligent industries. In the Middle East, Robo.ai plans to expand into multi-language and multi-scenario data annotation through strategic partnerships, building on its existing intelligent data operations. The company plans to develop standardized datasets centered on the Arabic language and local culture to consolidate its data node function within the regional AI ecosystem. Concurrently in East Asia, Robo.ai is integrating the embodied intelligent hardware supply chain by collaborating with local robotics manufacturers. This initiative seeks to secure the physical hardware, such as flexible robotic arms and physical robots, required for scaled intelligent data collection. By connecting the intelligent hardware and data collection segments of the supply chain, the company ensures its capacity to undertake data collection tasks requiring high-precision force control and spatial vision. Furthermore, in South Asia, Robo.ai is advancing cooperation frameworks with relevant data production platforms in India to address the data throughput requirements of AI model training. As a preliminary target for this regional cooperation, the company plans to develop an additional 30,000 hours of multi-dimensional scenario data collection and annotation processing capacity to steadily increase its total production scale.