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.
These related cases keep the next click close to the same kind of work.
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What we did. MoniSa grouped files by script, validated structural labels, and escalated disagreements.
The result. The buyer received an annotated dataset prepared for Document AI model training.
AI output reviewSafety annotation stabilized across multilingual batches.
Multilingual content safety
Problem. A content-safety team needed consistent risk labeling across languages and cultures.
Action. MoniSa tightened examples, retrained reviewers, and tracked recurring error patterns.
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Multilingual audio intelligence
Problem. A speech AI buyer needed continuous multilingual audio throughput while adding hard languages.
Action. MoniSa moved new languages through sourcing, pilot work, training, and review before scale.
Result. The buyer kept a rolling audio-data program moving across a wider language footprint.
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 approvalCapability at a glance
The answers most briefs open by asking for.
Buyers rarely start with who we are. They start with a list of fields to fill. Here are ours, so the first email can be about the work instead.
- Languages and locales
- 300+ languages and 4,500+ dialects, quoted per locale rather than per language — because the dialect decides whether a dataset is usable, whether a market accepts a release, and which specialist the work goes to.
- Specialist network
- 110,000+ verified language specialists — linguists, annotators, and reviewers — plus voice talent and subtitlers, matched to the language, domain and task before assignment.
- Capacity and mobilisation
- Named availability confirmed per pair before scoping. Coverage is reported as staffed today or needing a recruitment window, in writing, before a launch date or release window is agreed.
- Sourcing constraints
- Specialists can be sourced against geographic, residency, locale and demographic requirements — including native-only, in-country, and speaker-diversity quotas where a data programme demands them.
- Deliverables and specs
- Work is delivered to the receiving specification: structured formats and schemas for data and annotation work, and timed-text, audio and platform conformance for media — subtitle reading speed, line limits, cue timing, channel and sample-rate requirements included.
- Comparable work
- 62 documented case studies stating the scope, the constraint that made it difficult, and the measured result — across AI data programmes, partner overflow, and media releases. 2,000+ AI projects delivered and 1,000+ brands served since 2015.
- Certifications
- ISO 9001:2015 quality management, ISO 27001:2022 information security, and ISO 17100 translation services — scoped to translation specifically, and stated that way rather than implied across every line.
- Commercial basis
- Quoted in the unit the work is measured in — per word, per audio hour, per approved hour, per finished minute, per batch, per item — with what the unit includes stated alongside it, whether the quote is for you or for a client you quote onward.
Need this against your own template? Convert your scope between units and check the deadline, then send the brief with your language list, content type, volume and deadline, and the acceptance criteria you will judge the output against — those four decide feasibility, and the reply addresses them directly.