Our Solution: Smart Campus AI
To overcome these challenges, we built a next‑generation customer service platform powered by an AI large model fine‑tuned on park‑specific data such as historical tickets, institutional documents, FAQs, and conversation logs. Retrieval‑Augmented Generation (RAG) sits at the heart of the system. Unlike a model that depends only on memorized training data, RAG retrieves updated knowledge from live sources—like booking rules, café menus, or visitor policies—at the moment a question is asked. The system then combines this retrieved data with user intent to generate grounded, accurate responses that remain current over time.
This architecture unites two capabilities. The understanding layer interprets intent and entities, discerning details such as time, resource type, and location while enriching them with knowledge dynamically pulled in through RAG. The execution layer connects seamlessly with the Yunfan campus platform, transforming a conversational query into multi‑step actions: checking availability, creating reservations, issuing visitor passes, or triggering front‑desk notifications.
Application Impact
The deployment has shifted campus services from basic usability to proactive, personalized assistance. Conversations no longer end with vague answers but instead resolve business needs in full. Service accuracy now exceeds ninety percent, labor costs have been reduced by forty percent, and operational agility has increased, since the system continuously updates in sync with campus documents and regulations. Employees and visitors alike experience a smoother, more intelligent interaction, building trust and driving satisfaction across the campus environment.
Why RAG Matters
The integration of RAG ensures that answers are always based on the latest institutional knowledge and policies, rather than static or outdated model training. This eliminates hallucinations, maintains compliance, and allows the AI to adapt immediately whenever a new process or guideline is published. In practice, this means that even in highly dynamic environments, the system continues to provide reliable, context‑aware support without requiring extensive retraining.