Today’s products - devices, software and services - are well instrumented to permit users, vendors and service providers to gather maximum insight into how they are used, when they need repair and many other operational insights. Ensuring that products can rapidly adapt to a constantly changing environment and changing customer needs requires that the events they generate are analyzed continuously and in context. Insights can be synthesized from many sources in context - geospatial and proximity, trajectory and even predicted future states.Customers, vendors and service providers need to analyze, learn, and predict directly from streaming events because data volumes are huge and automated responses must often be delivered in milliseconds. To achieve insights quickly, we need to build models on-the-fly whose predictions are accurate and in sync with the real world, often to support automation. Many insights depend on analyzing the joint evolution of data sources whose behavior is correlated in time or space.In this talk we present Swim, an Apache 2.0 licensed platform for continuous intelligence applications. Swim builds a fluid model of data sources and their changing relationships in real-time - Swim applications analyze, learn and predict directly from event data. Swim applications integrate with Apache Kafka for event streaming. Developers need nothing more than Java skills. Swim deploys native or in containers on k8s, with the same code in each instance. Instances link to build an application layer mesh that facilitates distribution and massive scale without sacrificing consistency. We will present several continuous intelligence applications in use today that depend on real-time analysis, learning and prediction to power automation and deliver responses that are in sync with the real-world. We will show how easy it is to build, deploy and run distributed, highly available event streaming applications that analyze data from hundreds of millions of sources - petabytes per day. The architecture is intuitively appealing and blazingly fast.