This project developed a Retrieval-Augmented Generation (RAG) and Large Language Model (LLM) revenue intelligence system to mitigate customer churn and improve engagement efficiency. Addressing revenue risks stemming from data integrity gaps and siloed churn signals the team built a unified intelligence layer that connected multiple data sources into a vector knowledge store. The LLM can provide customer risk scoring, retrieve account payment and transaction history, and generate personalized outreach emails and call scripts in under two minutes. Featuring a 6-wave automated engagement cadence and predictive churn alerts, the system reduced manual account review time by 90%, enabled proactive retention strategies, and accelerated recurring revenue growth.