Case study
607,000 words, 17 languages.
An LSP partner needed rare-language translation, editing, and proofreading at two very different tempos: a 10-day surge, then a four-month sustained programme.
607,000 words - 17 - 5 — Latin, Bengali, Arabic, Devanagari, Cyrillic
607,000 words
Volume delivered
17
Unique languages
Measured outcomes
Rare-language TEP, two phases
Project overview
What landed, and what made it hard.
An LSP partner needed rare-language translation, editing, and proofreading at two very different tempos: a 10-day surge, then a four-month sustained programme.
Delivery snapshot
Rare-language TEP, two phases
- Client
- confidential language-service partner
- Service
- Translation, editing, proofreading
- Volume
- 607,000 words across 17 languages
- Phase 1
- 8 languages, 257,000 words, 10 days, project-scoped quality review
- Phase 2
- 12 languages, 350,000 words, 4 months, project-scoped quality review
Why this mattered
Outcome before process.
Phase 1 was compression — 8 rare languages, 4 scripts, 257,000 words, 10 calendar days. Phase 2 was endurance — 12 languages over four months, adding materially harder ones.
Across both phases the work covered 17 unique languages and 5 script systems: Latin, Bengali, Arabic, Devanagari, and Cyrillic.
MoniSa handled the work under its Triple ISO operating context: ISO 9001:2015 for process control, ISO 27001:2022 for information handling, and ISO 17100:2015 for translation-service discipline, which governs the second-review principle this work depended on.
The two phases returned different accuracy figures — project-scoped quality review and project-scoped quality review — and that difference is reported rather than averaged, because it is the most informative number in the case.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Phase 2 was harder than Phase 1 in a way the word counts do not show. It added Tuvanian, written in Siberian Cyrillic; Navajo, which has almost no standard translation infrastructure; and Kashmiri, which is written in two scripts.
The challenge
The problem to solve
Dual-script Kashmiri is the clearest example of why a language count understates rare-language difficulty. It is one entry on a language list and two production workflows, each needing its own QA.
Navajo carries a different problem: the professional translation infrastructure most languages take for granted — glossaries, style references, an established reviewer pool — barely exists, so the workflow has to supply what the ecosystem does not.
The 10-day Phase 1 window left no room for sequential sourcing. Eight rare languages across four scripts cannot be recruited, briefed, produced, and reviewed one after another inside two working weeks.
Five script systems means five sets of rendering, encoding, and QA requirements. A translation that is linguistically correct and breaks in its script is not delivered.
The buyer-side risk across both phases was single-point failure. In rare languages the qualified pool can be very small, and losing one linguist mid-phase can stall an entire language.
Operating response
What MoniSa changed
A pre-built rare-language bench was activated on demand rather than recruited at project start, which is the only way an 8-language, 4-script, 10-day surge is deliverable at all.
- Pre-built bench, activated The rare-language bench existed before the brief. Ten-day delivery across eight rare languages is a sourcing outcome, not a production one.
- Staggered parallel delivery Languages ran in parallel with offset delivery dates so review capacity was not all demanded at once.
- QA per script system Five scripts got five QA treatments, because rendering and encoding failures are script-specific and invisible to a combined check.
- Kashmiri as two workflows Dual-script Kashmiri was produced and reviewed separately per script rather than converted mechanically from one to the other.
Results
Measured outcomes from this engagement.
607,000 words were delivered across 17 unique languages and 5 script systems, in two phases with different tempos and different difficulty profiles.
| Volume delivered | 607,000 words |
|---|---|
| Unique languages | 17 |
| Script systems | 5 — Latin, Bengali, Arabic, Devanagari, Cyrillic |
| Phase 1 accuracy | project-scoped quality review — 8 languages, 257,000 words, 10 days |
| Phase 2 accuracy | project-scoped quality review — 12 languages, 350,000 words, 4 months |
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 work required a rare-language bench that already existed, plus script-specific QA across five writing systems and two very different delivery tempos.
What decided the result
What decided the result
Reporting the phase-to-phase accuracy difference honestly told the partner more about future capacity than a blended figure ever could.
What buyers can reuse
What buyers can reuse
- Ask for accuracy by phase and difficulty tier, never as a project average. An average conceals the hardest languages, which are the ones you are buying the vendor for.
- A language count understates rare-language difficulty. Dual-script Kashmiri is one language and two production workflows.
- Compressed multi-language delivery is a sourcing outcome. If the bench is built after the brief lands, the date is already at risk.
- QA must run per script system. Rendering and encoding failures are script-specific and survive a combined check.
- In rare languages, ask what happens if one linguist becomes unavailable mid-phase. The qualified pool may be small enough that there is no answer.
- Zero missed deadlines over four months is a stronger signal than a high accuracy figure over ten days. Sustained delivery is the harder test.
- A useful rare-language brief names the scripts, the review standard, the per-language deadline, and the contingency for linguist unavailability.
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.
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Compare this case with Rare-language TEP sprint and Rare-language evaluation at scale to judge whether the operating pattern fits your brief.
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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?
Volume delivered: 607,000 words. Unique languages: 17. Script systems: 5 — Latin, Bengali, Arabic, Devanagari, Cyrillic
What control kept the work stable?
Reporting the phase-to-phase accuracy difference honestly told the partner more about future capacity than a blended figure ever could.
Where should similar work go next?
Use Translation services for the delivery model, Rare-language translation buyer 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.
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- 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.