Large Language Models (LLMs) have revolutionized numerous domains but face challenges in effectively harnessing enterprise data while safeguarding privacy. This study presents a novel framework combining LangChain technology with OpenAI's GPT-3.5 model, bridging the gap between enterprise data and LLM capabilities. By ensuring robust privacy safeguards through Explainable AI, LangChain facilitates secure data utilization without compromising sensitive information. The proposed framework accommodates major data source types and offers scalability to incorporate additional data types and sources. It features an LLM-powered data ingestion system, enabling automation and enhancing business intelligence applications. Leveraging advanced NLP capabilities, the system excels in language-driven data ingestion, showcasing the potential of LLMs. The efficacy of framework is evaluated against existing data ingestion methods, highlighting its dynamic nature empowered by a Python framework. Through a comprehensive analysis encompassing accuracy, response quality, usability, and profitability, LangChain demonstrates its superiority. The major advantage of this framework is the transparency, which is prioritized through a human-readable insights approach, providing users visibility into AI-generated content. Rigorous testing methodology, employing a diverse set of questions, showcases framework’s satisfactory accuracy rate, with 12 out of 15 responses meeting expectations.