Yunisheng Mountain Ocean Big Model — China's First Remote Concentrated Rail Transit Intelligent Customer Service

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Yunisheng Mountain Ocean Big Model — China's First Remote Concentrated Rail Transit Intelligent Customer Service

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Jinan Metro has deployed China's first remote concentrated intelligent customer service system for urban rail transit, powered by the Yunisheng Mountain and Ocean large language model (LLM) in partnership with AI company Yunisheng (云知声). The system consolidated customer service operations from 11 metro stations into a single unified operations center, fundamentally transforming rail transit customer service from distributed, station-based staffing to centralized AI-augmented operations. A single customer service agent at the unified center can now handle passenger requests from multiple stations simultaneously, dramatically improving operational efficiency. The AI-powered system offers three key capabilities: (1) Voice-Guided Navigation — passengers can use natural voice interactions to get precise directions to destinations, transfers, and station facilities, with the AI understanding context and providing personalized guidance; (2) Facial Recognition Ticketing — passengers can purchase and validate tickets using facial recognition, eliminating the need for physical tickets or mobile phones and reducing ticket processing time to seconds; (3) One-Click Remote Human Assistance — when AI cannot resolve a passenger's issue, the system enables seamless escalation to a human agent at the unified center with full context preservation, without requiring the passenger to move to a different location or wait on hold. This deployment represents a significant shift in rail transit AI applications from single-point tools (e.g., automatic ticket gates or station information kiosks) to full-chain productivity platforms that span the entire passenger journey. The LLM-based system continuously learns from interactions to improve response quality, and its remote centralized architecture allows Jinan Metro to maintain high-quality customer service even during peak hours without proportionally increasing staffing costs. This model is being closely watched by other Chinese city metro operators as a template for AI-driven operational transformation in public transportation infrastructure.