Case study
LLM training data across 131 languages.
A large language model team needed production-grade multilingual training data across 131 languages, including 110 rare or indigenous languages.
131 - 110 - 1,800+ hours
131
Languages
110
Rare or indigenous languages
1,800+ hours
Transcript output
Measured outcomes
LLM training data coverage
Project overview
What landed, and what made it hard.
A large language model team needed production-grade multilingual training data across 131 languages, including 110 rare or indigenous languages.
Delivery snapshot
LLM training data coverage
- Client
- confidential AI platform
- Service
- Transcription, labeling, annotation, and segmentation
- Languages
- 131 languages
- Volume
- 1,800+ hours of transcript output
Why this mattered
Outcome before process.
The project was not a simple language-list exercise. Many languages required custom annotation instructions, native-speaker validation, and reviewer escalation before production could move.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
The buyer needed data for languages with uneven spelling conventions, limited digital resources, and limited trained annotation supply.
The challenge
The problem to solve
A standard suppliers pool could not make the work consistent across 131 language tracks without language-specific protocols.
Operating response
What MoniSa changed
MoniSa expanded sourcing through academic contacts, diaspora communities, cultural organizations, and direct in-country recruitment.
- Language protocols Each language received its own annotation notes, acceptance examples, and escalation route.
- Reviewer control Native-speaker reviewers checked transcripts and labels before delivery moved forward.
- Batch discipline Work moved in controlled batches so hard languages did not lag behind the broader program.
Results
Measured outcomes from this engagement.
The buyer received 1,800+ hours of transcript output across 131 languages, with production data structured for model training use.
| Languages | 131 |
|---|---|
| Rare or indigenous languages | 110 |
| Transcript output | 1,800+ hours |
| Services | Transcription, labeling, annotation, segmentation |
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
The engagement needed rare-language sourcing and reviewer control in the same operating model.
What decided the result
What decided the result
Coverage was useful only because each language track had its own protocol and review path.
What buyers can reuse
What buyers can reuse
- Large-language-model coverage breaks when rare languages are handled like commodity language pairs.
- Native-speaker validation and language-specific instructions kept the data usable for training.
- 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.
- Rare and indigenous languages
- Low-resource language tracks
- Multilingual transcript output
More proof
Related proof
Compare this case with Prompt safety evaluation and AI audio data pipeline 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: 131. Rare or indigenous languages: 110. Transcript output: 1,800+ hours
What control kept the work stable?
Coverage was useful only because each language track had its own protocol and review path.
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