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
110,000+
verified language specialists
Language specialist network
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
projects delivered since 2015
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
The buyer needed data for languages with uneven spelling conventions, limited digital resources, and limited trained annotation supply.
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 protocolsEach language received its own annotation notes, acceptance examples, and escalation route.
- Reviewer controlNative-speaker reviewers checked transcripts and labels before delivery moved forward.
- Batch disciplineWork 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 engagement needed rare-language sourcing and reviewer control in the same operating model.
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
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case evidence
Nearest proof pattern.
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Buyer questions
Ask the questions weak vendors avoid.
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
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.
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