With the growing popularity of Artificial Intelligence, specially related to Natural Language Processing, we notice a remarkable development of Large Language Models, which finds in the Generative Pre-Trained Transformers (GPT) their most outstanding example. As a result, virtual assistants have being gaining significant presence in various areas of modern life. In this work, we present the development of an intelligent virtual assistant, based on a generative model. The assistant understands Brazilian Portuguese and is trained on the specific jargon of the Oil and Gas Industry. This assistant has the ability to interpret textual commands provided by users and execute corresponding actions within a corporate system. This methodology is the result of a careful analysis of different available generative models, aiming to identify the one that best suited the requirements of an intelligent virtual assistant in Portuguese. Additionally, it involves the creation of a representative dataset, with concepts specific to the system and the Oil and Gas Industry, to effectively train the assistant. A refinement process allows the identification of potential flaws and the improvement of the assistant s understanding to ensure accurate and targeted responses. Furthermore, this work presents the challenges and the inherent limitations of generative models, and proposes strategies to overcome them in order to achieve more precise and secure generations.