Measure AI translation quality, and the human review effort behind it

As AI takes on more of the translation, the question changes. It is no longer "can AI translate this?" It is "how good is the output, and how much human effort does it still take to finalize?" For teams running AI plus review workflows at scale, that answer is surprisingly hard to see. The signal is spread across many projects, language pairs, configurations, and reviewers, on staggered timelines. So it gets reconstructed by hand, or not at all. As content volume and the number of AI configurations grow, the blind spot does not just get bigger. It compounds, and teams end up trusting automation they cannot actually see.

The Translation Review Report closes that gap. It reads the work already happening in your workspace and turns it into evidence, so quality and review effort stop being a feeling and become numbers you can point to.

Know how good your AI translation really is. You can tell content was edited, but not how much, where, or why. The report shows how often AI output is approved as-is versus edited, your edit rate across every workflow and language pair, and the exact segment-level changes behind it.

Put a number on the human review AI still demands. Review effort is real but invisible in aggregate. Estimated reviewer time per 1,000 words, broken down by language pair, configuration, and reviewer, shows exactly where human effort is going, which work is cheap to finalize and which is quietly eating your reviewers' time.

Find the configuration that is costing you, and fix it. When quality or turnaround slips, finding the cause usually means digging through documents by hand. Filter to any workflow, language pair, AI profile, or reviewer, and drill from a workspace trend to the single document and segment behind an outlier. You can see whether to adjust the AI configuration or the review agents that handle edits before a human opens the document, so tuning is targeted instead of guesswork.

Show that automation is paying off, over time. Without evidence that review effort is dropping, trust in automation and the case to expand it both stall. Trend charts track edit rate and reviewer effort across periods, so you can show review effort falling as your agents learn from your reviewers. That is the evidence that justifies the setup internally and supports doing more of it.

The Translation Review Report is a family of three connected views:

1. Translation Review Report (workspace): how often AI translations are approved as-is versus edited, plus edit rate and estimated reviewer time per 1,000 words, broken down by workflow, language pair, AI profile or template, reviewer, and time period.

2. Project Report: the same review metrics scoped to a single project, with a per-document breakdown.

3. Document Edit Report: a segment-level, before-and-after diff showing exactly what each contributor changed, and why.

Edit rate is measured as manually edited words divided by completed words. Auto-approved jobs and AI-only documents are excluded, so the number reflects real human reviewer behavior. All three reports export to PDF for sharing.

The Translation Review Report works best for teams where manual review is a standard step. If your team never reviews translations by hand, the headline metrics will read as N/A, since there is no human effort to measure. For everyone else, open the report in your workspace to see your edit rate and reviewer effort, filter to the workflows and language pairs that matter most, and start finding what to tune.

Learn more in the Help Center.