Learn about the top ways to use quality prediction in production translation workflows.

Hybrid translation

Speed up production with hybrid post-editing - part human, part machine.

Faster and cheaper

Save time and cut costs. Use quality prediction to find segments that can be used as-is - like 100% TM matches.

Focus human intelligence

Let human translators see, filter and sort by scores in the CAT, so they can work on what matters most.

Final review

Monitor the quality of human translations provided by linguists or vendors.

Higher final quality

Catch errors before they're published. Use quality prediction to find potential issues after human post-editing.

Efficient review

No more random sampling. Set a consistent bar, or sort by priority and work from the top.

Effort prediction

Estimate the post-editing effort before the work starts - like a fuzzy match analysis for machine translation.

Fairer and faster pricing

Post-editing effort varies wildly. Use quality prediction to get an instant third-party score for each segment, document and job.

Catch problems

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.

More solutions

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.

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