Most companies deployed AI translation without a quality framework. This page provides one. Eight measurable dimensions, defined escalation paths, and a governance model that turns "we think it's fine" into auditable data.
Jump to the framework ↓These aren't arguments against AI. They're arguments for governing it. The companies that win with AI translation will be the ones that measure it.
You deployed AI translation six months ago. Quality? You assume it's fine. But you can't prove whether output quality improved or degraded quarter over quarter. Without a baseline, improvement is invisible and degradation is silent.
When AI mistranslates a medical dosage, a legal clause, or a safety warning, who is accountable? The vendor? The model provider? The team that approved deployment? If nobody owns the answer, everybody owns the risk.
Someone on the team checks a sample when they have time. Not systematically. Not consistently. Not traceably. The industry term for this is "spot checking." The accurate term is "hoping."
The EU AI Act requires risk assessment for AI systems that affect people's access to information, services, or safety. If your translation pipeline produces medical, legal, or safety-critical content, it may already require documented quality governance.
EU AI Act in force since 2024Engineering owns the model. Marketing owns the content. Operations owns the workflow. Nobody owns the quality of the output. AI translation lives in the gap between departments, and gaps don't get governed.
A governance framework isn't a product. It's a discipline. These four pillars turn unmanaged AI output into a measurable, auditable, improvable system.
Context adherence. Hallucination rate. Terminology accuracy. Style consistency. Entity preservation. Cultural sensitivity. Formality consistency. Brand voice compliance. Each scored independently, each tracked over time. Quality isn't one number. It's eight.
When AI output falls below a quality threshold, what happens next? Who gets alerted? What's the fallback? A governance framework answers these questions before the failure occurs, not after.
Not "human-in-the-loop" (reviewing AI output after the fact). Human-at-the-core means a human governs the entire workflow: setting thresholds, defining escalation rules, training terminology models, interpreting quality trends. The human designs the system. The AI executes within it.
Not quarterly audits. Not annual reviews. Automated quality scoring on every output, trend monitoring across time, and governance reviews that catch drift before it becomes damage. The framework runs continuously because AI output varies continuously.
Three phases. No platform required. Works on top of your existing TMS and translation workflow.
Score your current AI translation output across all 8 quality dimensions. Establish the starting point. You can't improve what you haven't measured, and most companies haven't measured anything yet.
Define quality thresholds by content type. Set escalation triggers. Assign accountability. Build the reporting cadence. The framework adapts to your risk profile: marketing content tolerates more variation than medical content.
Automated quality scoring runs on every output. Trends are monitored weekly. Escalations route automatically when thresholds are breached. Quarterly governance reviews assess whether thresholds need adjustment as AI models evolve.
Published quality data from Kobalt's AI-enhanced localization operations. These scores are auditable and tracked continuously.
| Quality Dimension | What It Measures | Kobalt Published Score |
|---|---|---|
| Context adherence | Does the translation respect the source context and intent? | 95% |
| Hallucination rate | Does the AI add information not present in the source? | <5% |
| Terminology accuracy | Are client glossary terms used correctly and consistently? | 97% |
| Style consistency | Does the output match the client's defined style guide? | 94% |
| Entity preservation | Are names, numbers, dates, and codes left intact? | 99%+ |
| Cultural sensitivity | Is the output appropriate for the target market's norms? | Monitored per market |
| Formality consistency | Does the register (tu/vous, du/Sie) match the content type? | 98% |
| Brand voice compliance | Does the output sound like the client, not like a generic AI? | Scored per client |
A pharmaceutical company needed absolute terminology accuracy for product claims across 8 content channels. They moved from spot-check QA to a governance framework with defined thresholds per content type. Scientific claim consistency reached 99%+. Medical terminology accuracy hit near-zero error rate. The cost of quality assurance dropped by 45% because catching errors early costs less than fixing them late.
*Terminology consistency measures approved-term usage in regulated content. Distinct from overall terminology accuracy (97%) across all content types.
"AI liability will force risk-based quality models. As AI-generated content proliferates, organizations will need defensible quality governance."CSA Research, 10 Predictions for 2026
"Agentic translation will scale, with state and guardrails. The value moves to orchestration, quality governance, and workflow control."CSA Research, 2026
"Complexity management matters more than automation. Orchestration beats execution. Human-at-the-core systems are winning."Nimdzi Insights / CSA China Market Analysis, 2026
A structured system for measuring, monitoring, and improving the quality of AI-generated translations. It includes quality metrics, escalation triggers, accountability definitions, and continuous reporting. Without governance, you have AI translation. With governance, you have AI translation you can trust.
Eight dimensions: context adherence, hallucination rate, terminology accuracy, style consistency, entity preservation, cultural sensitivity, formality consistency, and brand voice compliance. Each dimension is scored independently and tracked over time.
Human-in-the-loop means a human reviews AI output after the fact. Human-at-the-core means a human governs the entire workflow: setting quality thresholds, defining escalation rules, training terminology models, and interpreting quality trends. The human doesn't check the work. The human designs the system that checks the work.
The framework scales, but the thresholds change. Marketing copy requires higher brand voice compliance. Medical content requires near-zero hallucination tolerance. Legal text requires absolute terminology accuracy. The eight dimensions are universal; the acceptable scores are not.
The EU AI Act requires risk assessment for AI systems that affect people's access to information, services, or safety. Translation systems that produce medical, legal, or safety-critical content may require documented quality governance. A governance framework provides the audit trail.
Yes. A quality governance framework is tool-agnostic. It sits on top of your existing workflow (Phrase, Lokalise, Smartling, any TMS). The framework defines what to measure and when to escalate. Your TMS handles the content routing.
The baseline audit takes 1 to 2 weeks. Framework design takes about a month. Continuous governance is ongoing. Most companies have measurable quality data within 6 weeks of starting.
The governance framework defines escalation paths for each quality dimension. Below-threshold context adherence triggers human review. Below-threshold terminology accuracy triggers glossary retraining. Below-threshold hallucination rates trigger a full stop on AI for that content type until the root cause is resolved.
We score your current AI translation output across all 8 quality dimensions. You get the data. No commitment, no platform to install.
Prefer email? ricard@kobaltlanguages.com