Enterprises rely on a variety of data sources, including invoices, news articles, legal documents, and financial records, to drive operations. Efficient Information Extraction (IE) is crucial for transforming this data into actionable insights to inform decision-making. Natural Language Processing (NLP) has revolutionized IE, enabling fast and precise analysis of large datasets. Techniques like Named Entity Recognition (NER), Relation Extraction (RE), Event Extraction (EE), Term Extraction (TE), and Topic Modeling (TM) are essential across industries. However, applying these techniques individually can be costly and resource-demanding, particularly for smaller organizations without significant Research and Development (R&D) resources. Large Language Models (LLMs), powered by Generative Artificial Intelligence (GenAI), provide a more affordable solution, handling multiple IE tasks simultaneously. Despite their strengths, LLMs can struggle with specialized, domain-specific queries, often leading to errors. To mitigate this limitation, Retrieval-Augmented Generation (RAG) is used to enhance LLMs by integrating external data retrieval, improving both accuracy and relevance. Although the integration of RAG with LLMs is gaining attention, its application in business settings remains relatively underexplored. This paper introduces Business-RAG, a new approach combining RAG with LLMs to improve business-related IE. We focus on Llama and DeepSeek, two open-source models that highlight the strengths of LLMs in business tasks. By integrating RAG with these models, Business-RAG offers a powerful solution for extracting actionable business insights, showcasing the potential for further exploration and improvement in business intelligence applications.