To work reliably, modern translation platforms allow us to process vast volumes of data and gain critical business insights. For example, we can take enormous volumes of files that have been post-edited, coupled with source content profiling and anonymized locale-specific productivity metadata, and run automatic scoring algorithms to capture the expected historical range of edits and words per hour for a specific locale pair and for individual strings. The latest state-of-the-art advances in AI give us the ability to capture full semantic, contextual and grammatical similarity, and perform machine translation quality estimation (MTQE) between source and machine-translated target, or benchmark several engines’ performance against each other. This multi-tiered approach results in “managing-by-exception” where human assessment is only necessary only for anomalies or escalations.