Design and Implementation of a Real-Time RAG-Based Customer Relationship Management System Using Event-Driven Knowledge Updates and Vector Embeddings
Production CRM systems increasingly use large language models, yet typical Retrieval-Augmented Generation (RAG) implementations suffer from knowledge staleness due to 5–10 min batch processing cycles. This paper presents a streaming RAG architecture for business CRM applications that provides real-time knowledge updates with average document-to-query propagation latency of 3.1 s and strong retrieval quality. The event-driven system uses Apache Kafka for document ingestion, Rust microservices for embedding generation, PostgreSQL with pgvector for vector storage, and GPT-4 for response generation. On 62 insurance policy documents from 20 users and 102 test queries, mean document-to-query propagation latency was $3.1 ~\mathrm{s}, 75-150 \times$ faster than batch processing, with retrieval quality metrics of Precision@5 = 0.398, MRR $=0.938$, and NDCG${@} 10=0.942$ consistent with values reported in prior literature. Additional load testing with simulated users verified production-grade performance stability (P95 latency $<10.33 ~\mathrm{s}$), suggesting that streaming designs may mitigate the knowledge-currency vs. system performance trade-off in production CRM applications.