Case study
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
Project overview
What landed, and what made it hard.
A global technology company needed human evaluators across 14 languages to rate large language model outputs for accuracy, fluency, bias, and safety.
Delivery snapshot
Multilingual LLM output evaluation
- Client
- confidential global technology company
- Service
- Multidimensional LLM output evaluation
- Languages
- 14 (European and Indian)
- Resources
- 140 evaluators
Why this mattered
Outcome before process.
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
Why the work was difficult, and what MoniSa changed in-flight.
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.
The challenge
The problem to solve
Holding a consistent standard across 14 languages and 140 evaluators meant calibration had to come before production, not after the ratings drifted.
Operating response
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% |
Selection logic
What protected the result.
The selection came down to whether MoniSa could source and review the work at standard, and whether that would hold across the full run.
Why the fit was real
Why the fit was real
Multidimensional evaluation across 14 languages needs calibrated human judgment, beyond bilingual reviewers.
What decided the result
What decided the result
Calibrating evaluators before production is what kept the standard consistent across 140 people and 14 languages.
What buyers can reuse
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.
Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
- European languages
- Hindi
- Tamil
- Telugu
More proof
Related proof
Compare this case with Prompt safety evaluation and Rare-language evaluation at scale to judge whether the operating pattern fits your brief.
case evidence
Nearest proof pattern.
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Buyer questions
Answers in writing, before you ask for a call.
The questions buyers send before a scope conversation, answered on the page rather than in a meeting. Take them to your team, then send us the one we did not answer.
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?
You are told before a date is agreed, not after. Coverage is reported pair by pair as staffed today or needing a recruitment window, with the window stated — in writing, while the scope is still being agreed. Nobody new goes onto live work until a pilot batch has been reviewed and signed off. A coverage claim you cannot check before signing is not coverage.
Similar brief
Send the constraint behind the metric.
A useful follow-up to a case study names the language mix, review model, deadline, and what proof your buyer team needs before approval.
Production-ready brief
01Closest matching challenge from this case02Language pair, dialect, and script coverage03Volume, cadence, or hours to deliver04Reviewer model and acceptance criteria05Security or platform constraints06Proof needed for stakeholder approval