In the digital age, managing and interacting with document content has become increasingly complex due to information overload, particularly from PDFs and other formats. This project aims to enhance user experience and efficiency in document interaction by implementing advanced search algorithms, improving navigation structures, and integrating intelligent question-answering capabilities. We assess six question-answering models: BERT Large (uncased) fine-tuned on SQuAD, BERT Base (uncased), DistilBERT Base (uncased), ALBERT Base v2, Google ELECTRA Base Discriminator, and RoBERTa Base fine-tuned on SQuAD2. Performance is measured using confidence scores to determine accuracy and reliability. Additionally, the project involves developing a chatbot interface that leverages natural language processing (NLP) techniques to facilitate seamless and intuitive user interactions with document content. This includes extracting and structuring text from PDFs and other documents to improve accessibility and collaboration, particularly for remote teams. By addressing document format complexities and accessibility issues, this project aims to streamline workflows and reduce information fatigue, ultimately leading to higher productivity and satisfaction in document management tasks. The outcomes of the comparative study will inform the selection of the most effective QA model, thereby enhancing the integration of document content across diverse workflows and tools.