Technology is increasingly being used to translate both audio and written content from one language into another. One key piece of technology used for this purpose is natural language processing (NLP) which, through the processing of language using computers, understands the meaning of the language as it is naturally spoken and translates accordingly, rather than simply translating content word-for-word. Machine translation (MT) allows for multiple documents to be translated at a much greater speed than a human translator. MT systems can be Generic (ad-hoc translations via public-facing channels) or Customisable (which can form translation memories based on terminologies from a specific domain such as healthcare). Cloud-based MT systems have also emerged in recent years, reducing implementation costs further. While many of these systems offer translation into multiple languages, MT remains inferior to human translation when it comes to understanding the particular context, style and tone of each language, making it unsuitable for critical applications such as legal documents and safety manuals. Different cultures have varying lexical items that are native, along with informal terms and idioms which machines may not be able to comprehend in some cases. Companies now blend machine translation with human expertise, as seen in Post-Edited Machine Translation (PEMT). Unbabel (USA) exemplifies this by merging fast machine translation with precise human review, reducing costs while ensuring accuracy.