AI - Driven Internal Data Intelligence Assistant

The proliferation of generative AI presents significant productivity opportunities for software companies, but the practice of employees using public AI chatbots poses severe data security risks. This research paper demonstrates the application of a Retrieval-Augmented Generation (RAG) architecture into building a dedicated AI assistant for secure, internal corporate use by reasoning its responses exclusively in a company's private document repository, unlike a general-purpose model. This will ensure that sensitive internal data such as project specifications, internal wikis, project codes, knowledge documents, policy documents etc do not leave the company firewall during the AI operation cycle while also letting the company entity use AI to compare and access the needful resources from huge internal data.Our methodology involves processing and vectorizing internal documents, enabling semantic search for precise information retrieval, and leveraging a large language model (LLM) solely for generating context-aware responses from the retrieved data. We argue that this system provides a practical, secure, and efficient solution for knowledge management and employee assistance, balancing the power of modern AI with the non-negotiable demands of corporate data privacy.

Retrieval-Augmented Generation (RAG), AI data security, Knowledge management, vector database, code generation, Natural language Processing (NLP), Document intelligence

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