Translation quality control is the process of evaluating and managing translation quality to ensure content meets linguistic, brand, and compliance standards.

In AI and machine translation workflows, quality control is essential to reduce risk, protect brand integrity, and enable automation. Reliable quality evaluation allows organizations to scale multilingual content production while maintaining consistent standards.

Machine translation quality evaluation assesses how accurate, fluent, and fit-for-purpose machine-translated content is.

It determines whether content can be published automatically or requires AI or human post-editing. Effective machine translation quality evaluation enables safe automation, reduces manual review, and supports enterprise-scale localization.

The Phrase Quality Performance Score (QPS) is an automated translation quality metric in the Phrase Platform, based on the MQM 2.0 framework.

QPS generates immediate quality scores that support AI translation quality control and automated workflow routing. By setting customizable quality thresholds, organizations can automatically lock high-scoring segments and route lower-scoring content for review, improving efficiency, transparency, and control.

AI translation quality control combines automated quality scoring with customizable thresholds and AI checks.
With this approach:

• High-quality content can be automatically approved and delivered
• Lower-quality machine-translated content can trigger AI or human post-editing

This intelligent routing reduces unnecessary post-editing, increases straight-through processing, and lowers localization costs while maintaining quality standards.

Yes. Phrase allows organizations to tailor translation quality evaluation to their business needs.
Teams can:

• Define custom quality thresholds
• Configure AI-based quality checks by domain or language
• Align evaluation with brand voice and terminology requirements

This ensures quality control reflects real business risk rather than generic benchmarks.

MQM (Multidimensional Quality Metrics) is a standardized framework for categorizing and weighting translation errors.

Modern translation quality evaluation systems use MQM-based methodologies to produce structured, transparent scores that can be integrated into automated workflows. Phrase QPS is built on the MQM framework.

Automated quality evaluation identifies which segments meet predefined quality thresholds.
This enables organizations to:

• Approve high-quality content without review
• Route only flagged segments for editing
• Focus linguist effort on higher-risk material

The result is lower post-editing volume and more efficient resource allocation.

Rule-based QA (quality assurance) automatically detects predefined errors in translated content.
In Phrase, built-in QA checks in the TMS CAT Editor help translators identify issues such as:

• Terminology inconsistencies using term base checks
• Spelling errors
• Incorrect or missing tags

Rule-based QA strengthens translation quality control by catching objective errors before delivery.

Software localization QA ensures that translated UI content meets technical and brand requirements.
In Phrase Strings, QA checks help:

• Flag broken or missing placeholders
• Detect translations that exceed maximum character length
• Identify terminology that does not follow approved term bases

This prevents UI issues, protects syntax, and ensures consistency across localized software experiences.