Spot quality issues before clients do, with Translation Review Reports
If you run localization quality at scale, you already know the real exposure isn't "did the review happen," it's "would I know if it didn't go well." As more first-pass review shifts to AI, that question gets harder to answer with a straight face. That's the pattern showing up everywhere right now: the speed of change keeps outpacing our ability to sense it in time, and quality review is no exception.
Prove translation quality at scale and spot issues instantly with workspace-wide visibility
Today, proving quality across dozens of projects and reviewers usually means a spreadsheet somebody rebuilds every month, a PM you have to interrupt for a status update, or a client escalation that's the first real signal something slipped.
None of that scales, and all of it puts the burden of proof on you after the fact, instead of before the problem ever reaches a client.
Maintain continuous quality control with unified workspace-wide review reporting
Translation Review Reports replace that patchwork with one governed source of truth for review quality, workspace-wide. Approval rate, edit volume, and reviewer activity render automatically from data you're already generating in every project, with no export and no separate tracker to maintain.
From the workspace view you can drill into any single project, any single reviewer, or any single segment, and see the actual before/after edit behind the number, not just the number itself.
Prevent bottlenecks and target quality audits using granular reviewer and edit data
- Workspace-wide approval rate. See every project's trend in one view. Catch a quality dip and fix the project before a client ever sees the problem.
- Per-project edit volume. Compare how much reviewers are actually changing in any one project against your workspace average. Know with evidence, not instinct, which language pairs still need a second human pass.
- Reviewer activity tracking. See who reviewed what and how much. Rebalance workload before a bottleneck becomes a missed deadline.
- Segment-level before/after edits. Open the real edit behind any aggregate number. Run QA audits and reviewer coaching on evidence instead of impressions.
- Zero setup. Reports populate automatically from existing project data. Get this visibility today, with nothing to configure or migrate.
Protect client trust across enterprise workflows with proactive quality management
"The more content we can process through Smartcat, the more leverage we gain from the platform, and the more confidence everyone has in the translations. That confidence continues to grow as we scale." – Michelle Quirke, Program Engagement Manager, Topcon
- An LQA lead running a monthly quality audit pulls the workspace-wide approval rate trend instead of chasing five PMs for manual reports, and flags the one project trending below threshold before it reaches the client.
- A localization program manager on a high-volume enterprise account uses per-project edit volume to decide which language pairs still need a second human reviewer and which can safely run on the AI-reviewed pass alone.
- An enterprise admin uses reviewer activity data to rebalance assignments across an in-house review team ahead of a seasonal content spike, before anyone is overloaded.
How to access Translation Review Reports and drill into quality data
- Open Reports from your workspace navigation.
- Pick your scope: workspace-wide or a single project.
- Review the dashboard: approval rate, edit volume, and reviewer activity render automatically.
- Click into any segment to see the exact before/after edit behind the numbers.
Live automatically on Anticipate, Autonomous, Unite, and Enterprise plans. No setup required.
For a full walkthrough, see Understanding Translation Review Reports in the Smartcat Help Center.