The increasing adoption of Retrieval-Augmented Generation (RAG) architectures by enterprises reflects the growing need to integrate large language models into corporate environments while preserving data confidentiality. RAG enables organizations to harness the advanced capabilities of Natural Language Processing systems without exposing sensitive information to external entities, thus ensuring compliance with privacy and security requirements. This thesis provides a comprehensive analysis of the structure and deployment of a Retrieval-Augmented Generation (RAG) system within an enterprise context. The work begins by detailing the ingestion process of corporate documents, followed by the inference pipeline, and concludes with a systematic approach to automating the entire workflow to achieve a production-ready RAG solution.