Moving from Anecdote to Evidence
Scaling AI translation in an organizational context requires a blend of automated evaluation indicators backed by rigorous, decision-grade human-in-the-loop safeguards.
Why Quality Evaluations of AI?
Quality Evaluations of AI provide localization teams with a structured approach to evaluating AI translation, giving clear evidence for decision-making.
Automated Metrics
- Standard efficiency indicators (METEOR, TER, BLEU) using “Golden Translations” or TM references as a benchmark.
- Defined error typology covering accuracy, fluency, compliance, and language categories.
Human Annotation
- Decision-grade layer using a minimum of two linguists to annotate error classes and provide detailed hallucination reporting.
- Linguistic depth that identifies specific failure modes that automated metrics often miss.
Scoring Framework
- 1-4 scoring system (worst to best) designed to provide a clear segment distribution report.
- Actionable data built to inform workflow routing, prompt optimization, and governance thresholds.
Quality Evaluation reimagined for AI translation
Move beyond generic quality estimation to documented valuation depth. Our framework provides the evidence required to determine where AI is safe to scale and where human review must remain mandatory, allowing teams to balance global efficiency with absolute brand safety.