A large volume of legal documents was obtained by scraping a web database of European Union court rulings to provide an example corpus of legal documents. The solution relied on using state-of-the-art NLP models to power the semantic search engine. For the initial proof-of-concept, pre-trained sentence transformer models were chosen for their general text encoding capabilities. In later phases of the project, custom models were fine-tuned using a dataset tailored for the legal domain. The semantic search and novelty detection algorithms were based on measuring the proximity of a user’s search query to the chunks of text in the document corpus. The text classification model was trained in a self-supervised fashion by exploiting the HTML structure of the scraped documents. The web application was developed using Streamlit and deployed as a Docker application on the Azure Cloud, allowing the client to run the service from anywhere.