HUK-COBURG relies on decision trees to classify e-mails into topics. As the data is stored within relational database systems, this paper investigates the performance improvement of in-database evaluation of decision trees. The paper first summarises the theory behind binary decision trees, then describes the implementation as SQL case-when statements. The evaluation shows the benefit of the in-database application of decision trees by eliminating expensive extraction-transform-load time. In the future, we intend to optimise the training as well as inference of decision trees in SQL to fully eliminate the need for data extraction.