Webinar

Real-time context engine: RAG, memory & agentic infra

Getting AI systems to work in a demo is the easy part. Making them fast, consistent, and scalable enough to trust in production is where most teams hit a wall; juggling separate systems for retrieval, memory, and agent state that each need to be built, maintained, and kept in sync. This session shows you how to power RAG, long-term memory, and agent workflows from a single, real-time context engine on Redis, using reference architectures and practical patterns you can apply to your own stack.