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
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.
How AI data services was delivered
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.
What was delivered on 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 this team fit the assignment
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.
Work connected to Multilingual LLM output evaluation
Related services, buying guidance and language coverage
AI data services and buyer guidance
Languages listed
- European languages
- Hindi
- Tamil
- Telugu
More proof
Compare this case with Prompt safety evaluation and Rare-language evaluation at scale to judge whether the operating pattern fits your brief.
Compare Multilingual LLM output evaluation with other projects
These project records show other scopes and delivery decisions for comparison.
Cross-lingual similarity evaluation
The challenge. A global AI research lab needed similarity evaluation for Santali and Oriya paired with Hindi, where trained evaluators are scarce.
What we did. MoniSa deployed validated native linguists, shared feedback before production, and resolved QA the same day.
The result. 5,000+ prompts evaluated across two rare pairs, accepted through the agreed review path.
Discuss a similar project
Start a projectAI data services15,000 categorized retail images delivered ready for object detection and visual search training.
Visual search image data
Problem. A computer-vision team needed shelf and storefront imagery with enough real-world variance to train models that generalize.
Action. MoniSa collected across multiple locations, captured lighting and configuration variance deliberately, and organized by category on delivery.
Result. 10,000 supermarket and 5,000 storefront images, structured for direct pipeline ingestion.
AI data servicesA single voice data project became a recurring relationship, including rare-language work.
Voice data, 500 hours
Problem. A client needed ~500 hours of US English voice data with diversity and privacy requirements, inside a fixed budget.
Action. MoniSa collected inside the client's own app environment with privacy controls, two-layer QC, and terms that protected speaker diversity.
Result. ~500 hours at reviewed quality satisfaction, extended by the client into follow-on rare-language engagements.
Multilingual LLM output evaluation questions
What was delivered on this engagement?
Languages: 14 (European and Indian). Evaluators: 140. Volume: 1,000+ hours of evaluation
Which control supported the Multilingual LLM output evaluation result?
Calibrating evaluators before production is what kept the standard consistent across 140 people and 14 languages.
Scope work like Multilingual LLM output evaluation
For a comparable project, include the language mix, review requirements, deadline and evidence your team needs.
Send a brief