This paper presents an advanced question-answering (QA) system designed to support enterprise contract management by seamlessly integrating document and database information. Leveraging large language models (LLMs) and agent orchestration, our system delivers precise, context-aware responses to complex contract-related queries. We enhance retrieval accuracy through a pipeline incorporating Retrieval-Augmented Generation (RAG) and Text-to-SQL techniques, eliminating the need for LLM retraining. Through targeted Prompt Engineering, we refined the system’s ability to extract and synthesize key contractual information, significantly improving response relevance and accuracy. Our evaluation demonstrates the system’s potential to significantly reduce time-consuming tasks in contract workflows and provide actionable insights, marking a significant advancement in enterprise contract management systems.