为网球观众打造实时AI助手，用自然语言问答即时解析比赛数据。
Match Chat: Real Time Generative AI and Generative Computing for Tennis
- 采用智能体架构融合规则引擎与生成式AI，预处理并优化用户提问。
- 响应准确率92.83%，平均响应6.25秒，支持每秒120次请求。
- 界面无门槛，96.08%查询通过交互式提示引导，适合大众使用。
我们提出Match Chat，一个基于生成式人工智能（GenAI）与生成式计算（GenComp）的实时、智能体驱动助手，旨在提升网球观众体验，即时回应比赛相关问题。该系统在2025年温网和美网亮相，通过自然语言查询为约100万用户提供流媒体与静态数据无缝访问。其架构基于智能体导向架构（AOA），结合规则引擎、预测模型与智能体，对用户查询进行预处理与优化后传入GenAI组件。系统在每秒120个请求（RPS）负载下保持92.83%的准确率，平均响应时间6.25秒。超过96.08%的查询通过交互式提示设计引导，确保用户体验清晰、快速且低负担。系统隐藏底层复杂性，提供零门槛、直观的操作界面。两次大满贯赛事部署中，系统实现100%可用性，支持近100万独立用户，验证了平台的可扩展性与可靠性。本工作提出面向实时消费级AI系统的若干关键设计模式，强调速度、精度与可用性，为动态环境中高性能智能体系统的落地提供可行路径。
原文摘要 · Abstract (English)
We present Match Chat, a real-time, agent-driven assistant designed to enhance the tennis fan experience by delivering instant, accurate responses to match-related queries. Match Chat integrates Generative Artificial Intelligence (GenAI) with Generative Computing (GenComp) techniques to synthesize key insights during live tennis singles matches. The system debuted at the 2025 Wimbledon Championships and the 2025 US Open, where it provided about 1 million users with seamless access to streaming and static data through natural language queries. The architecture is grounded in an Agent-Oriented Architecture (AOA) combining rule engines, predictive models, and agents to pre-process and optimize user queries before passing them to GenAI components. The Match Chat system had an answer accuracy of 92.83% with an average response time of 6.25 seconds under loads of up to 120 requests per second (RPS). Over 96.08% of all queries were guided using interactive prompt design, contributing to a user experience that prioritized clarity, responsiveness, and minimal effort. The system was designed to mask architectural complexity, offering a frictionless and intuitive interface that required no onboarding or technical familiarity. Across both Grand Slam deployments, Match Chat maintained 100% uptime and supported nearly 1 million unique users, underscoring the scalability and reliability of the platform. This work introduces key design patterns for real-time, consumer-facing AI systems that emphasize speed, precision, and usability that highlights a practical path for deploying performant agentic systems in dynamic environments.
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