RAG and GenAI for Customer 360 Architecture

This post details the architectural shift required for AI-powered Customer 360, moving from legacy batch-oriented data warehouses to real-time, event-driven systems. It highlights the limitations of stale data for GenAI and RAG, emphasizing the need for continuous event ingestion, unified real-time customer state, context-aware retrieval, and guardrailed generation. The architecture combines a real-time data engine (Kafka, Flink, Customer Profile Store), a knowledge and embedding pipeline (document sources, embedding generation), and a retrieval and AI layer (RAG, GenAI Copilot) with cross-cutting observability and governance. The data flow emphasizes bypassing traditional database latency by using an event streaming platform to update the live customer profile in milliseconds, fusing this state with curated domain knowledge for RAG, and ensuring safety and compliance through guardrailed prompt construction.