Real-time Analytics · Risk Management
Xiangji Technology needed a cost-effective way to process and serve massive weather-observation, numerical-model, and satellite datasets. Accurate commercial weather forecasting depends on heavy computation and constant product iteration, but building enough on-premises infrastructure for that workload was too expensive for a growing specialist company.
Reported impact
Xiangji used Alibaba Cloud to industrialize commercial forecasting without overbuilding local infrastructure. The platform lowered development cost, improved deployment flexibility, and gave the company a scalable path to serve more weather-sensitive industries with data-rich AI forecasting products.
Xiangji Technology built its commercial weather-intelligence stack on Alibaba Cloud so it could turn large-scale meteorological data into industry services without carrying the cost of self-managed infrastructure. The company's Gavial spatiotemporal data platform acts as a PaaS layer for large weather datasets, handling large-volume query, processing, scheduling, and development needs. On top of that, the AI-Weather forecasting core combines machine learning with numerical weather models so the company can cover basic meteorological elements, more complex weather features, and domain-specific forecast adjustments for customers in agriculture, logistics, transport, retail, and energy. Xiangji has used Alibaba Cloud since 2015, relying on elastic compute and cloud-native container capabilities to reduce R&D cost while keeping customer data safe. The result is a commercial weather platform where AI improves forecast post-processing and industry delivery instead of remaining a research-only tool.
Observation feeds, numerical model outputs, and satellite remote-sensing data are ingested into Gavial, where they are processed and combined with Xiangji's AI-Weather engine. The forecast outputs are then delivered to downstream industry customers as decision-support services for operations, risk reduction, and planning.
After partnering with Alibaba Cloud in 2015, Xiangji said elastic compute and cloud-native containerization sharply reduced product R&D cost while improving security and stability. (original: 2015年，其与阿里云展开合作，借助弹性计算、云原生容器化等能力，大幅降低气象预测产品研发成本。)
Its Gavial platform provides large-scale query, processing, scheduling, and development for spatiotemporal data, while AI-Weather combines machine learning and numerical models. (original: “Gavial”时空大数据平台：集海量查询、快速数据处理、数据调度和开发于一体... “AI-Weather”智能预报核心：基于机器学习+数值模式的双预报引擎构建智能预报核心技术。)
Xiangji said it uses massive observed weather data, numerical-model data, and satellite remote-sensing data together with intelligent forecasting technology to help customers make better decisions. (original: 象辑科技拥有全球海量的气象实况监测数据、领先的数值模式数据和丰富的卫星遥感数据，利用行业领先的气象大数据云和智能气象预报技术，可为客户提供专业的气象服务。)
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