Kaskada is a streaming engine built for the data side of real-time AI: connecting models to live events and historical context at the same time.
That combination is the hard part. Real-time aggregation precomputes model inputs from streaming data through connectors and transformations, while history replay supplies the past context a model needs to interpret a current event. Event detection triggers proactive behaviour by identifying significant activity as it occurs, rather than waiting for something to query the model. Most systems do live or historical well; joining the two consistently is where real-time AI usually breaks down.
It ships with a user guide, worked examples, an API and a community. This is infrastructure for engineering teams building event-driven AI features – not something a non-technical user adopts. Check the project’s current maintenance activity before building on it, as with any component you put in a production data path.
