Case study
Forty-one projects, eight languages, 303,500 words held to one quality bar in one quarter.
A global LSP partner needed overflow production capacity that could absorb 41 projects across eight languages in a quarter without dropping quality or exposing a sub-vendor.
41 in one quarter - 303,500 words - 8 (major Indic and rare pairs)
41 in one quarter
Projects
303,500 words
Volume
Measured outcomes
LSP overflow partnership
Project overview
What landed, and what made it hard.
A global LSP partner needed a production partner who could absorb a high project count across eight languages, major Indic languages alongside rare pairs, without quality slipping or the end client seeing a sub-vendor.
Delivery snapshot
LSP overflow partnership
- Client
- A global LSP partner
- Service
- White-label translation production
- Projects
- 41 in one quarter
- Volume
- 303,500 words
- Quality
- Independently reviewed
Why this mattered
Outcome before process.
Overflow partnerships fail quietly: the partner ships the volume but the quality or the turnaround drifts, and the end client feels it.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Forty-one projects in a quarter across eight languages is a coordination problem first; each project has its own files, terminology, and deadline, and rare pairs cannot wait on the major languages.
The challenge
The problem to solve
The partner needed white-label production held to their standard, delivered under their brand, with no quality gap between the major and rare languages.
Operating response
What MoniSa changed
MoniSa ran the work as white-label production through a shared translation management system, with per-language assignment and senior review, so the partner could route projects without managing the bench.
- White-label delivery Work shipped under the partner brand, with production handled invisibly behind it.
- Per-language routing Each language and project got its own assignment and review path so rare pairs kept pace.
- Shared tooling A shared translation management system kept terminology and handoffs clean across concurrent projects.
Results
Measured outcomes from this engagement.
The partner ran 41 projects across eight languages, 303,500 words on this engagement, delivered white-label without a quality gap between major and rare languages.
| Projects | 41 in one quarter |
|---|---|
| Volume | 303,500 words |
| Languages | 8 (major Indic and rare pairs) |
| Quality | Independently reviewed |
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
LSP overflow needs a partner who delivers white-label and holds rare pairs to the same bar as the major languages.
What decided the result
What decided the result
The partner kept their brand in front of the end client while production scaled behind it.
What buyers can reuse
What buyers can reuse
- LSP overflow capacity is only useful if it is white-label and holds rare pairs to the major-language standard.
- High project counts across many languages are a coordination problem solved with per-language routing and shared tooling.
- The evidence keeps the partner 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.
- Major Indic languages
- Rare language pairs
- Multilingual project routing
More proof
Related proof
Compare this case with adjacent MoniSa proof before deciding 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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Trust and safety moderation
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What we did. MoniSa committed dedicated daily hours per language with native, context-aware review.
The result. The platform received 250+ hours of safety review on a weekly, 24-hour cadence.
Media and metadataThree-year streaming subtitling and QC held to one bar, client details confidential.
Streaming subtitling and QC
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AI evaluationFifty languages evaluated in a compressed sprint at project-scoped quality review, client details confidential.
LLM fine-tuning evaluation
Problem. A model team needed 20,000 prompts evaluated across 50 languages under a compressed decision window for a fine-tuning decision.
Action. MoniSa sourced five pre-calibrated evaluators per language across all 50 tracks in parallel.
Result. The team received ~20,000 evaluations across 50 languages during the compressed sprint at project-scoped quality review.
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?
Projects: 41 in one quarter. Volume: 303,500 words. Languages: 8 (major Indic and rare pairs)
What control kept the work stable?
The partner kept their brand in front of the end client while production scaled behind it.
Where should similar work go next?
Use LSP partner buyer lane for the delivery model, the case studies hub 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.