Quality defined by role + domain fit and consistent performance; achieved through vetting, briefing, calibration, and retention; measured through error patterns, rework rate, on-time delivery, and reviewer agreement over time.
It’s the output of a holistic system — vendor selection and briefing, project and linguistic asset management, workflow design, and expectation management — designed to perform under real conditions.
Language Quality Assessments (LQA) typically rely on an error typology (an error-category framework) such as the LISA QA model or MQM/DQF-style typologies often used in TAUS DQF contexts. These typologies can be adapted to match the specific use case by adjusting which error categories matter most, and how severity and weighting are applied.
When we use LQA as part of vendor qualification, we don’t start from scratch each time. We apply default weighting modes for common content types — essentially pre-configured “evaluation profiles” — so assessment is consistent, comparable, and fit-for-purpose. If a client has specific acceptance criteria (e.g., regulated terminology, strict brand voice, UI length constraints), we adapt those defaults — but we begin with a stable baseline so qualification results remain reliable across vendors and projects.
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