This paper examines the potential of Generative Artificial Intelligence (Gen AI) in enhancing financial analysis through metric extraction by combining Large Language Models (LLM), NLP, and other techniques like vectorization and RAG. Due to the complex and time-intensive nature of traditional financial analysis, there is a pressing need for more automated solutions and less human interventions. Our research utilized web scraping to gather data (SEC filings), NLP techniques for data content pre-processing, embeddings & vector search for relevant content retrieval, and LLM models on Vertex AI for information extraction from the given context. We also implemented other techniques discussed in detail later in this paper. The primary aim was to provide a streamlined design to such a process that would minimize human error and provide personalized information through a novel AI assistant-style tool. The result is a chatbot tool that serves as an AI assistant, enabling users to navigate financial queries and extract meaningful insights with ease. This study highlights the transformative potential of Gen AI in financial analysis, catering to the demand for timely and accessible financial insights in a data-intensive market.