Trouble reporting is a substantial component in any technical product's maintenance workflow. In this project, we investigated a set of methods for streamlining this workflow, using both software solutions and machine learning. The aim was to find a way of grouping trouble reports for easier analysis and other potential usecases down the line. This project was conducted in the context of telecom infrastructure. Unstructured data that is produced both by humans and machines was transformed into embeddings representations, usingmultiple Bert based language models, of which one was domain specialised. The embeddings were clustered using multiple clustering techniques, and finally labeled using machine learning. Furthermore, we compared the use of our Bert models, with the use of the classical TF-IDF representation, with the aim of creating a baseline for the performance of these models . Trials showed that the best way of representing the trouble report depended on its content. TF-IDF had benefits when the keywords were few, exclusive to the group and carried a lot of relevance. However, when the keywords had many synonyms or were counter-productive to look at, the language model showed better results. The sentence model S-Bert was almost always superior to the other Bert-based language models, even the domain specialised one.