Human evaluation of LLM output across 14 languages.
A global technology company needed human evaluators across 14 languages to judge a large language model output for accuracy, fluency, bias, and safety, where the work was judgment rather than translation.
14 (European and Indian) - 140 - 1,000+ hours of evaluation
14 (European and Indian)
Languages
140
Evaluators
Measured outcomes
Multilingual LLM output evaluation
The project
Multilingual LLM output evaluation
- Client
- confidential global technology company
- Service
- Multidimensional LLM output evaluation
- Languages
- 14 (European and Indian)
- Resources
- 140 evaluators
A global technology company needed human evaluators across 14 languages to rate large language model outputs for accuracy, fluency, bias, and safety.
The roster combined European languages with five Indian languages, so evaluators needed both linguistic expertise and the domain understanding to catch what automated checks miss.
The problem to solve
The work was judgment, not translation: evaluators had to catch cultural context issues, factual errors, and safety concerns that an automated system would pass over.
Holding a consistent standard across 14 languages and 140 evaluators meant calibration had to come before production, not after the ratings drifted.
What MoniSa changed
MoniSa calibrated every evaluator before production, then ran a multidimensional rating framework across all 14 languages with continuous quality monitoring.
Calibrate first
Each evaluator was calibrated against the standard before entering production rating.
Multidimensional framework
Ratings covered factual accuracy, fluency, bias, and safety rather than a single score.
Continuous monitoring
Quality was watched across all 14 languages so the standard held as volume grew.
Results
Measured outcomes from this engagement.
The company received 1,000+ hours of evaluation data across 14 languages at a reviewed acceptance rate, with each evaluator calibrated before production.
| Languages | 14 (European and Indian) |
|---|---|
| Evaluators | 140 |
| Volume | 1,000+ hours of evaluation |
| Acceptance | 90% |
What supported the result
Why the fit was real
Multidimensional evaluation across 14 languages needs calibrated human judgment, beyond bilingual reviewers.
What decided the result
Calibrating evaluators before production is what kept the standard consistent across 140 people and 14 languages.
What buyers can reuse
- LLM evaluation is judgment work: the value is in catching what automated checks pass over.
- Calibration before production kept 140 evaluators consistent across 14 languages.
- The evidence keeps the client details confidential and attributes the metrics only to this engagement.
Continue from this proof
Useful comparisons for the same problem.
Use these links to compare the case with the matching service, buyer guide, and language coverage.
Services and guides
Related services
Languages named
Examples referenced in the engagement.
- European languages
- Hindi
- Tamil
- Telugu
More proof
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case evidence
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Buyer questions
Common questions.
What was delivered on this engagement?
Languages: 14 (European and Indian). Evaluators: 140. Volume: 1,000+ hours of evaluation
What control kept the work stable?
Calibrating evaluators before production is what kept the standard consistent across 140 people and 14 languages.
Where should similar work go next?
Use AI data services for the delivery model, AI data annotation vendor guide for buyer-side evaluation, and the contact page for a scoped brief.
What happens if you cannot staff one of my language pairs?
For the proposed project, ask for pair-by-pair availability or a recruitment window in writing before agreeing a date. Define qualification and pilot approval for any new contributor before live work. A coverage claim should be checkable before the scope is signed.
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