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Confluent Blog0 Hot0 comments23 min read
Confluent: Data in motion
Batch customer data platforms can't capture user intent as it forms — by the time a nightly sync completes, the intent moment is gone. A streaming-native architecture built on Apache Kafka and Apache Flink handles the full spectrum of personalization latency windows, from sub-100ms real-time bidding to multi-day email campaigns, using the same four-job pipeline: connect, stream, process, and govern. An AI-native layer (Confluent Intelligence) sits on top, enabling streaming agents with MCP tool-calling, a real-time context engine for LLMs, and built-in ML functions (ML_PREDICT, AI_COMPLETE) for embedding, ranking, and generative copy — all running as Flink jobs with exactly-once semantics and full lineage. The guide covers three production patterns (retail product recommendations, media feed personalization, cross-channel cart abandonment orchestration), a five-capability vendor evaluation framework, and a three-phase rollout roadmap from streaming backbone to autonomous agentic personalization.
Source: https://www.confluent.io/blog/real-time-hyper-personalization-architecture. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
