Published Paper Details:

Large Language Models (LLMs) have advanced data analysis capabilities, but most systems rely on cloud infrastructure, raising concerns about privacy, latency, and data security. This paper presents an offline-first data analysis system that integrates locally deployed LLMs with a Retrieval-Augmented Generation (RAG) model to enable secure and context-aware insights without cloud dependency. The proposed system processes data entirely on local machines using document ingestion, vector retrieval, and LLM-based reasoning, ensuring that sensitive information remains within the user environment. Additionally, federated privacy principles are incorporated to support decentralized and secure model updates. The system is designed for low-spec devices and supports real-time querying, document understanding, and knowledge synthesis. This paper discusses the architecture, implementation, and performance trade-offs, demonstrating a scalable and privacy-preserving solution for intelligent data analysis in offline environments.