AI Translation Quality Monitoring
See how Kobalt monitors AI translation quality with measurable metrics: terminology accuracy, hallucination detection, cultural scoring, and complete audit trails for EU AI Act compliance.
See the governance gap ↓AI translation is fast, scalable, and largely unmeasured. These are the five risks that grow when nobody monitors output quality.
Your team uses machine translation but nobody measures output quality systematically. Errors surface when customers complain or auditors flag issues. By then, the content has been live for weeks.
Reactive, not proactiveLLM-based translation can produce fluent text that says the wrong thing. Traditional QA catches grammar errors, not factual inaccuracies. Without hallucination detection, you are publishing content you cannot verify.
Fluent but wrongMT engines optimize for linguistic accuracy, not cultural appropriateness. A perfectly translated phrase can be culturally offensive or commercially ineffective. No automated system catches this reliably without human expertise.
Accurate but inappropriateThe EU AI Act requires documentation of AI-assisted content processes. Most MT workflows have no systematic quality logging, no decision trails, no risk classification. When auditors ask how you monitor AI output quality, what do you show them?
No compliance trailYou can report on volume, turnaround, and cost. But what about terminology accuracy? Context adherence? Hallucination rate? Without baseline measurements, you cannot improve what you cannot see.
Unmeasured = unmanagedNot all AI translation workflows are equal. The difference is in what happens after the machine produces output.
Machine translation with no quality layer. No measurement, no governance, no audit trail. Output goes live without evaluation. Nobody knows what the hallucination rate is because nobody checks.
Acceptable for internal reference content where errors have no external impact. Unacceptable for anything customer-facing, regulated, or brand-sensitive.
Post-editors fix obvious errors but lack systematic quality evaluation. No hallucination detection, no cultural scoring, no trend analysis. Quality depends on the individual editor, not on a repeatable process.
Better than raw MT, but quality is inconsistent and unmeasured. You cannot report on terminology accuracy or hallucination rates because nobody tracks them.
Every piece of AI-translated content is evaluated against five quality metrics: terminology accuracy (97% to date), context adherence (95%), tone adaptability (95%), hallucination detection, and task adherence (98%). Human experts validate AI output at every risk threshold.
Complete audit trail for EU AI Act compliance. Quality dashboards with trend lines. Monthly reports. You can see exactly where quality stands and how it changes over time.
These are auditable numbers from our production system, not projections. Every metric is tracked per deliverable and published monthly.
Every piece of content is evaluated across five metrics: Context Adherence (95%), Terminology Accuracy (97%), Tone Adaptability (95%), Hallucination Rate (tracked monthly, target below 5%), and Task Adherence (98%). These are published, auditable metrics measured on production content to date.
Quality scores are tracked over time, producing trend lines that show improvement trajectories and flag regressions. Monthly reports aggregate metrics by language pair, content type, and AI engine.
97% terminology accuracy 95% context adherence <5% hallucination targetAI agents handle intake, translation, and initial QA. But brand-critical content, regulatory text, and low-confidence outputs are automatically escalated to human experts. The system knows what it does not know.
ISO 9001 and ISO 17100 certified processes govern every human review. Escalation triggers are systematic, not subjective: low confidence scores, hallucination flags, regulatory content classification, and quality scores below established thresholds.
ISO 9001 + 17100 Automatic escalation Human-at-the-coreFrom baseline metrics to continuous monitoring in four weeks. No disruption to your current workflow at any point.
Send us a sample of your AI-translated content (any language pair, any content type). We run it through our quality evaluation pipeline and deliver a baseline report: terminology accuracy, hallucination instances, cultural flags, and context adherence scores. You see exactly where your current MT output stands.
We process a batch of your live content through our AI quality orchestration system. AI agents handle translation and initial QA. Human experts review flagged content. You receive quality scores, trend data, and risk classifications for every piece of content.
Ongoing quality evaluation with published dashboards. Terminology accuracy, hallucination rates, and cultural scores tracked over time. Monthly quality reports. Complete audit trail for compliance. Quality improves because you are measuring it.
Book an assessment to see where governance gaps exist.
Book an AI Translation Governance AssessmentFive core metrics: terminology accuracy (97% to date), context adherence (95%), tone adaptability (95%), hallucination rate (tracked monthly, target below 5%), and task adherence (98%). Each metric is measured per deliverable and tracked over time. Monthly quality reports show trends, flags, and improvement trajectories for every language pair and content type.
Hallucination detection runs at multiple levels. Automated checks compare source-target semantic alignment to flag content that is fluent but factually divergent from the source. Human reviewers then evaluate flagged segments for meaning preservation, factual accuracy, and added information not present in the source. Content that exceeds risk thresholds is automatically escalated to subject-matter experts before delivery.
Yes. Our AI quality orchestration system maintains a complete audit trail for every piece of AI-assisted content: which AI engine was used, what quality scores were assigned, whether human review occurred, and what decisions were made at each stage. This documentation satisfies the transparency and human oversight requirements of the EU AI Act for AI-generated content workflows.
Four automatic triggers: low confidence scores from the AI engine, hallucination flags from semantic alignment checks, brand-critical or regulatory content classification, and quality scores below established thresholds for any metric. The system is designed to know what it does not know. Approximately 30% of content is escalated to human experts, though this varies by content type and risk level.
Client-specific terminology databases are integrated into the AI translation pipeline. Approved terms are enforced during translation, and terminology accuracy is measured as a specific metric on every deliverable. New terms are flagged for client approval before being added to the database. Terminology consistency is tracked over time, with current accuracy at 97% across all client accounts to date.
Yes. Our quality evaluation pipeline is engine-agnostic. We can assess output from any MT or LLM engine using the same metrics framework. This allows you to compare quality scores across engines for specific content types and language pairs, and make data-driven decisions about which engine performs best for which use case.
Monthly quality reports include per-deliverable scores across all five metrics, trend lines showing quality movement over time, hallucination incident logs, human escalation rates and outcomes, and terminology accuracy by content type. Reports are delivered as structured dashboards with exportable data for your internal governance documentation.
Baseline metrics are delivered within one week. Send a sample of your AI-translated content in any language pair. We run it through our quality evaluation pipeline and deliver a report covering terminology accuracy, hallucination instances, cultural flags, and context adherence scores. No commitment required to get your baseline.
Share a sample of your AI-translated content. We will run it through our quality evaluation pipeline and show you terminology accuracy, hallucination detection, and cultural appropriateness scores — on your actual content.
Prefer email? ricard@kobaltlanguages.com