ARCHIVES

The exponential growth of unstructured textual data in the form of PDFs, academic articles, legal documents, and enterprise reports poses significant challenges for efficient information retrieval and comprehension. Traditional document processing approaches, often reliant on manual review and keyword-based search, are time-consuming, error-prone, and incapable of delivering context-aware insights. This research proposes Docu Mind AI, an intelligent document analysis system powered by advanced Natural Language Processing (NLP) techniques and Retrieval-Augmented Generation (RAG) frameworks. The system incorporates document ingestion, semantic chunking, vector indexing, and real-time query interpretation using large language models (LLMs). A user-friendly web interface built with Streamlit enables seamless interaction, allowing users to upload documents, generate concise summaries, and receive real-time responses to queries. The integration of OCR and parsing techniques ensures multi-format support, while FAISS-based vector embedding facilitates fast and scalable information retrieval. Experimental evaluation demonstrates the system’s effectiveness in automating document comprehension, reducing manual effort, and enhancing productivity across academic, legal, enterprise, and governmental domains. The findings highlight the potential of DocuMind AI to serve as a scalable foundation for next-generation intelligent document management solutions.