The amount of data being generated in various domains and complex environments requires the use of AI techniques to facilitate data analysis. Currently, AI algorithms, some based on black-box models, are being used. However, the results are not 100% accurate, and these techniques do not allow professionals to verify the reasons behind the results to improve them. Therefore, at VRAIN, we propose Explainable AI, which enables users to identify the decisions that drive the results in an understandable way, allowing the system to evolve according to their needs. This service has numerous applications, such as information management, the analysis and interpretation of large amounts of data, the selection and consolidation of different data sources or repositories, or the understanding of biological mechanisms that lead to diseases. It offers benefits such as improved identification and treatment of the data used and cost reduction in studies and genomic domain analysis.