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Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.

Every enterprise has a data monster. And a way to take control. From AI to analytics, Confluent supercharges innovation across every organization with reusable, reliable, real-time data. So don’t let your data monster hold you back. Unlock value and unleash business impact with freeflowing, real-time data on The World’s Data Streaming Platform.

Confluent CEO Jay Kreps takes the stage alongside industry leaders at data streaming’s biggest event. Together, they’ll show why free-flowing, real-time data has become the key to unleashing the full potential of intelligent systems across every business. From live demos to real-world use cases to industry-changing product announcements, this year’s keynote is essential viewing for anyone looking to maximize the potential of their AI. Which is pretty much everyone. Don’t miss it.

Wix rewired 85% of its data volume onto Confluent Freight Clusters—and the result was lower costs and elastic scalability that handles Black Friday–scale spikes without manual intervention. Josef Goldstein explains why it felt like a magical solution.

Wix processes 40 billion events a day across use cases that range from minutes to milliseconds. Josef Goldstein explains why the entire upstream architecture has to be built around your most latency-sensitive lane—or none of it works.

Real-time data and AI are converging—and companies that have already solved the data pipeline problem are pulling ahead fast. Wix processes over 40 billion interactions every day across hundreds of millions of websites, and the architecture behind that scale didn't happen by accident. It was built, lane by lane, around the principle that your upstream data must be at least as fast as your fastest use case.

KCP automatically generates custom Terraform modules, allowing you to provision your entire target infrastructure and networking in just a few minutes for Kafka migrations.