Ornamental horticulture in Mexico is a growing industry that requires the inclusion of various technologies to automate production and marketing in order to increase its profitability. For this, data analysis is key, allowing obtaining knowledge to support decision-making; However, it involves exhaustive information processing time, affecting the productivity of companies due to the lack of a decision support system that implements dynamic business intelligence tools. This research work proposes a business intelligence web system for the creation of dynamic tools and execution of asynchronous queries to the database; which provides an analysis of the historical information on the commercialization of ornamental plants through tables, graphs and reports. It is developed using the PUA methodology, the Python programming language and the Django framework, employing an innovative approach by applying the DFS algorithm as a search mechanism to determine the relationship between the database tables, reducing extraction time, processing , analysis and presentation of information. As a result, it was possible to improve the use of historical information, streamline the processing and analysis of marketing information and, consequently, improve decision-making processes.