When regulation is published by financial authorities, it is often dense with legal language. Supervised entities and others subsequently incur significant delays and costs in parsing this into actionable terms. Natural Language Processing (NLP) has the potential to streamline this process, fostering progress toward the holy grail of machine-readable regulation. In the meantime, the same NLP tools can be leveraged to identify and highlight the relevant sections of a legal document, reducing the burden on both the supervisors and the supervised. To provide the global community of financial authorities a hands-on experience, this proof of concept, presented as part of the Regtech for Regulators Accelerator (R2A) allows a user to upload an NDA, let the service parse it, and suggest actions based on the content.