RAG and GenAI for Customer 360 Architecture

What this post added

This post elaborates on the Real-time Enterprise RAG feature thread by detailing the specific architecture for an AI-powered Customer 360 system. It outlines a layered approach involving a real-time data engine (event streaming with Kafka, stateful stream processing with Flink, and a customer profile store), a knowledge and embedding pipeline, and a retrieval and AI layer (RAG). It also highlights the importance of observability and governance. The post details the data flow from customer events to AI responses, emphasizing continuous event ingestion, real-time profile updates, and context-aware retrieval for guardrailed generation.