Product development companies are collecting data in form of Engineering Change Requests for logged design issues and Design Guidelines to accumulate best practices. These documents are rich in unstructured data (e.g., free text) and previous research has pointed out that product developers find current it systems lacking capabilities to accurately retrieve relevant documents with unstructured data. In this research we therefore evaluate the capabilities of two Natural Language Processing models to find relations between documents in two databases with Engineering Change Request and Design Guideline documents. The aim is to turn hours of manual documents searching into seconds by utilizing Natural Language Processing to find documents that are related and rank them in order of significance. Domain knowledge experts evaluated the results and it shows that the models applied managed to find relevant documents in one third of the cases tested. The models can iteratively be improved by feeding back responses from the user where he/she answers whether or not a specific document were relevant or not.