When boards need to make a data-driven decision, they may ask some quick questions about reports made by many people in a company (e.g., financial reports, what-if analysis…). Behind the scenes, these financial reports and what-if analysis require the involvement and collaboration of database administrators, business analysts, and data scientists (if the forecast is needed). This process may take a few weeks to put a report together. Therefore, we propose a framework for enhancing the Boardroom TOD System with Financial Ontology and Embedded Machine Learning. Trying to make the decision-making process more efficient, by defining a board meeting as a closed-domain QA with Text-to-SQL & TOD System problem and combining Knowledge Retriever (Ontology System) to enhance it.