Case study
project-scoped language work of translation and evaluation across 10+ rare languages
A major technology company needed nearly project-scoped language work translated and linguistically evaluated across some of the world's most under-resourced languages. Marshallese. Hmong. Hawaiian. Languages where the global pool of qualified translators can be counted on two hands. MoniSa Enterprise delivered the full scope in 25 days with reviewed quality linguistic accuracy.
project-scoped language work - 10+ rare languages - 25 days
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
Rare-language evaluation set
Project overview
What landed, and what made it hard.
A major technology company needed nearly project-scoped language work translated and linguistically evaluated across some of the world's most under-resourced languages. Marshallese. Hmong. Hawaiian. Languages where the global pool of qualified translators can be counted on two hands. MoniSa Enterprise delivered the full scope in 25 days with reviewed quality linguistic accuracy.
Delivery snapshot
Rare-language evaluation set
- Client
- A major technology company
- Service
- Translation & Linguistic Evaluation
- Volume
- project-scoped language work
- Turnaround
- 25 days
Why this mattered
Outcome before process.
The client needed both translation AND independent evaluation — two separate teams per language, not one team doing both. Most vendors could source one or the other for rare languages. MoniSa could source both, including for languages like Marshallese and Palauan where the reviewer pool barely exists.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
This was not a standard translation project. The client required two distinct deliverables per language pair: translated content and independent linguistic evaluation of that translation. The evaluation component demanded a second set of linguists, qualified reviewers who could assess accuracy, fluency, and cultural appropriateness without having seen the original translation in progress.
The challenge
The problem to solve
This was not a standard translation project. The client required two distinct deliverables per language pair: translated content and independent linguistic evaluation of that translation. The evaluation component demanded a second set of linguists, qualified reviewers who could assess accuracy, fluency, and cultural appropriateness without having seen the original translation in progress.
The language list made sourcing difficult by design. Marshallese has an estimated global population of 44,000 native speakers. Hawaiian is a revitalized language with limited commercial translation infrastructure. Hmong spans multiple dialects across Southeast Asia and the US diaspora. Other languages in the scope, Maori, Palauan, Tahitian, presented similar sourcing challenges.
project-scoped language work in 25 days meant an average throughput of 31,500+ words per day across all language pairs. At this volume, a single bottleneck in one language pair cascades into delays across the entire project.
Operating response
What MoniSa changed
We structured the operation as two parallel workstreams, translation and evaluation, with firewalls between the teams to preserve evaluation independence.
- Dual-team architecture:For each language, we assembled a translation team and a separate evaluation team. Evaluators never saw work-in-progress translations. They received completed batches and assessed them against defined rubrics, accuracy, fluency, terminology consistency, and cultural fit.
- Diaspora-based sourcing:For Marshallese, we sourced linguists from diaspora communities in Arkansas and Hawaii. For Hmong, we worked with US-based and Laos-based native speakers. For Hawaiian, we engaged linguists connected to University of Hawaii language programs. Each linguist was vetted through paid test tasks before project assignment.
- Daily throughput tracking:We tracked words delivered per day per language pair against target. Any pair falling low of daily target triggered an escalation to the vendor manager within 4 hours. Two language pairs required mid-project linguist additions to maintain pace.
- Batch-synchronized delivery:Translation and evaluation outputs were synchronized into weekly delivery batches. The client received both the translated content and the evaluation reports together, enabling immediate quality assessment.
Results
Measured outcomes from this engagement.
The client accepted all deliverables on first submission. the correction path stayed light. The evaluation reports confirmed translation quality independently, the client did not need to allocate internal reviewers for any of the rare-language pairs.
| Total volume delivered | project-scoped language work |
|---|---|
| Languages | 10+ rare languages |
| Named languages | Marshallese, Hmong, Hawaiian, Maori, Palauan, Tahitian, and others |
| Delivery timeline | 25 days |
| Linguistic accuracy | reviewed quality |
| Deliverable types | Translated content + independent linguistic evaluation |
Selection logic
What protected the result.
The client needed both translation AND independent evaluation — two separate teams per language, not one team doing both. Most vendors could source one or the other for rare languages. MoniSa could source both, including for languages like Marshallese and Palauan where the reviewer pool barely exists.
Why the fit was real
Why the fit was real
The client needed both translation AND independent evaluation — two separate teams per language, not one team doing both. Most vendors could source one or the other for rare languages. MoniSa could source both, including for languages like Marshallese and Palauan where the reviewer pool barely exists.
Why the result held
Why the result held
Parallel translation and evaluation workstreams ran synchronized batch delivery — the client received both outputs together, enabling immediate quality comparison. This dual-track model is what the project required, and it is what most vendors cannot operationalize for rare languages.
What buyers can reuse
What buyers can reuse
- Translation and evaluation require separate teams with enforced independence. Using the same linguists for both translation and review introduces confirmation bias. Firewalled teams produce evaluation data that the client can actually trust.
- Rare-language sourcing at scale requires diaspora networks. For languages like Marshallese and Hawaiian, traditional vendor databases are empty. Community-level relationships, built over years, not weeks, are the only reliable sourcing channel.
Continue from this proof
Useful comparisons for the same problem.
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Mapped context
Service and buyer context
Languages named
Examples referenced in the engagement.
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More proof
Related proof
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What was delivered on this engagement?
Total volume delivered: project-scoped language work. Languages: 10+ rare languages. Named languages: Marshallese, Hmong, Hawaiian, Maori, Palauan, Tahitian, and others
What control kept the work stable?
Parallel translation and evaluation workstreams ran synchronized batch delivery — the client received both outputs together, enabling immediate quality comparison. This dual-track model is what the project required, and it is what most vendors cannot operationalize for rare languages.
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Use AI and ML buyer lane for the delivery model, How to Choose a Translation Vendor for Rare Languages for buyer-side evaluation, and the contact page for a scoped brief.
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Production-ready brief
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