What strong QA capabilities actually look like
Translation quality assurance is a broad term that can mean anything from a basic spell-check to a comprehensive program of automated scoring, human review, and trend reporting. When enterprise buyers evaluate QA capabilities, the relevant question is not whether a platform has QA features but whether those features are integrated into the translation workflow or require manual steps to activate.
A platform where QA runs automatically as part of every translation job provides fundamentally different value than one where QA requires a separate review step that localization managers must initiate. The former produces quality data as a byproduct of normal operations. The latter produces quality data only when someone remembers to run it.
The strongest QA platforms also distinguish between different types of quality issues and handle them at the appropriate point in the workflow: automated checks catch mechanical errors early, quality estimation routes low-quality output before reviewers see it, LQA scoring provides program-level measurement, and human review is deployed where it adds the most value.
The five QA capability layers that matter for enterprise programs
1. Automated Quality Checks
Automated Quality Checks evaluate translated content for mechanical errors: missing placeholders, incorrect punctuation, number mismatches, terminology violations, and formatting inconsistencies. These checks run within the translation tool and flag issues immediately, so translators can correct them before a job advances in the workflow.
Quality Check AI Correction extends this further by generating automated fixes for flagged issues rather than requiring translators to correct each error manually. For programs with high volumes of similar error types, AI Correction significantly reduces the time between error detection and resolution.
2. Language Quality Estimation
Language Quality Estimation (LQE) predicts the quality of machine-translated output before it reaches human review. By scoring translations on predicted quality, LQE enables intelligent routing: strings that are likely to require significant editing are flagged and directed for additional attention before advancing, while high-quality strings can proceed with minimal intervention.
For enterprise programs, LQE changes how human review bandwidth is allocated. Rather than applying uniform review across all content, review effort is concentrated where the quality signal indicates it is most needed.
3. LQA Agent scoring
LQA Agent scoring evaluates translations against an MQM framework automatically, providing structured quality assessment across full translation output without requiring manual reviewer bandwidth for every evaluation. The LQA Agent produces MQM scores that can be tracked over time, compared across language pairs and content types, and reported to leadership as evidence of program quality.
4. Quality Check AI Correction
Quality Check AI Correction allows translators to generate automated corrections for flagged quality check errors directly within the translation tool rather than correcting each error manually. This reduces the editing time associated with common error types and decreases the time between quality issue detection and resolution.
5. LQA dashboards and trend reporting
Quality data is only useful if it is accessible and actionable. LQA dashboards aggregate MQM scores across the localization program, with filtering by language pair, content type, vendor, and workflow. Trend data shows whether quality is improving or degrading over time, making it possible to identify systematic issues before they reach a threshold that affects customers or compliance.
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