Hyundai’s Minho Baek visits Beijing Langhui to discuss embodied-AI data services

On 10 October 2026, Minho Baek, Senior Manager of Procurement Planning at Hyundai HMGRIC, and his colleagues visited Beijing Langhui Technology’s operations site in Jinan. The discussion covered data processing, professional annotation, quality management, and project cooperation for robotics and embodied AI. The Langhui team introduced its workflow and service scope, and the two sides discussed sample validation and possible next steps.

The visit followed earlier email exchanges and online meetings. Hyundai’s focus was professional post-processing of data already collected, and an on-site view of how the work is organized, how quality is checked, and how results are delivered.

From scope to delivery

Langhui walked through a service path that starts with clarifying the request and confirming annotation rules, then moves through processing, quality inspection, and delivery. The discussion covered first-person video, robot-operation data, and pick-and-place tasks in manufacturing and logistics, so that data content, annotation targets, and the intended use stay aligned.

For robotics and embodied-AI development, a data service has to complete the labeling task and also understand the action, the scene, and how the customer will use the result. Langhui treats task scope, work instructions, and acceptance criteria as items to settle at kickoff, then uses sample alignment and in-process feedback so the output can support training and evaluation.

Quality checks and records

Quality management was a main topic. Langhui described staged checks, issue feedback, and correction, with the same criteria applied from sample validation through batch processing and acceptance. Catching drift early, keeping one working standard, and retaining quality records gives a delivery that can be inspected and traced.

Access control and deployment

The two sides also discussed confidentiality and work management: who can access the data, centralized work on the platform, isolation of customer data, and deployment. Langhui outlined project-specific options — work on the customer’s servers, or cloud collaboration — and the need to state the access scope, use limits, and delivery method in the actual plan.

A sample trial, then a wider look

They discussed a small sample-labeling trial. The intent is to test, on real data, whether the requirement is understood, whether annotation quality holds, and whether the delivery fits, before any broader evaluation of cooperation. Langhui will continue on sample preparation, rule confirmation, and result feedback.

Beyond the current annotation need, they also talked about a possible later extension into data collection and more robot applications. If the requirement becomes clearer, scene setup, collection, annotation, and quality management could be coordinated as one service for embodied-AI research and application.

The visit gave both sides a clearer view of the requirement, the service path, and how a project could be run. Langhui will keep building professional data services with explicit standards, a controlled process, and practical delivery, and will keep working with companies and research teams in China and abroad.