Post-editing machine translation (PEMT) is a high-variance, labor-intensive bottleneck in global content operations. Human editors correct raw MT output for brand voice, glossary compliance, and contextual accuracy, but manual effort scales poorly and introduces inconsistency. A custom automation workflow applies AI agents as a pre-editing layer, handling routine corrections and routing only complex, low-confidence segments for human intervention. This shifts editor focus to high-value work, directly reducing per-word cost and accelerating throughput for TMS and LSP platforms.




