Learn about the top ways to use quality prediction in production translation workflows.
Speed up production with hybrid post-editing - part human, part machine.
Save time and cut costs. Use quality prediction to find segments that can be used as-is - like 100% TM matches.
Let human translators see, filter and sort by scores in the CAT, so they can work on what matters most.
Monitor the quality of human translations provided by linguists or vendors.
Catch errors before they're published. Use quality prediction to find potential issues after human post-editing.
No more random sampling. Set a consistent bar, or sort by priority and work from the top.
Estimate the post-editing effort before the work starts - like a fuzzy match analysis for machine translation.
Post-editing effort varies wildly. Use quality prediction to get an instant third-party score for each segment, document and job.
Whether it's a bad OCR scan of a PDF or an engine having a bad day, catching problems early gives you a chance to react.
Have other ideas or need something else?
ModelFront provides quality prediction as an API, so you can use it in infinite ways.
We're happy to chat about what works - and what doesn't.