Case study
Six years of app localization across 21 languages.
A global social platform needed continuous, consistent product localization across 21 languages, including several low-resource European and South Asian languages, sustained across years of releases rather than one launch.
21 - 4,000,000+ words - Continuous since 2020
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
Enterprise app localization at scale
Project overview
What landed, and what made it hard.
A global social platform needed continuous product localization across 21 languages, sustained across years of releases rather than a single launch.
Delivery snapshot
Enterprise app localization at scale
- Client
- confidential global social platform (via a top-100 localization partner)
- Service
- Continuous product and content localization
- Languages
- 21 languages
- Engagement
- Multi-year, continuous since 2020
Why this mattered
Outcome before process.
The work ran under a top-100 localization partner on a white-label basis. MoniSa supplied the multilingual production capacity behind that partner-facing relationship.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
A consumer platform that ships continuously cannot absorb terminology drift. The same feature name, label, or policy phrase has to read consistently across every release and all 21 languages.
The challenge
The problem to solve
A consumer platform that ships continuously cannot absorb terminology drift. The same feature name, label, or policy phrase has to read consistently across every release and all 21 languages.
The language mix combined high-resource languages with several low-resource European and South Asian languages, where holding trained linguist supply steady over a multi-year engagement is harder.
Operating response
What MoniSa changed
MoniSa sourced dedicated linguist teams per language and kept the same linguists on the account over time, so terminology and product voice carried forward from one release to the next.
- Dedicated language teamsTwo to three linguists per language stayed on the account and built product familiarity instead of restarting each batch.
- Terminology continuityShared glossaries and review notes kept feature names and policy language consistent across releases.
- White-label deliveryProduction ran under the partner brand, so the platform experienced one steady localization relationship.
Results
Measured outcomes from this engagement.
Across a multi-year engagement the platform received more than 4,000,000 words of localization across 21 languages, with the same dedicated teams carrying terminology forward across releases.
| Languages | 21 |
|---|---|
| Volume | 4,000,000+ words |
| Engagement | Continuous since 2020 |
| Model | Dedicated linguist teams per language, white-label |
Selection logic
What protected the result.
This engagement rewarded retention and consistency over one-off throughput, which is what dedicated long-term teams provide.
Why the fit was real
Why the fit was real
This engagement rewarded retention and consistency over one-off throughput, which is what dedicated long-term teams provide.
What decided the result
What decided the result
Holding the same linguists per language kept product voice and terminology stable across continuous releases.
What buyers can reuse
What buyers can reuse
- Continuous product localization is a retention problem first: value compounds when the same linguists stay on the account.
- A 21-language mix stays consistent only when terminology continuity is built into the workflow.
- 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.
- Low-resource European languages
- South Asian languages
- Continuous multilingual releases
More proof
Related proof
Compare this case with Rare-language translation at scale and Cultural adaptation across indigenous languages to judge whether the operating pattern fits your brief.
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: 21. Volume: 4,000,000+ words. Engagement: Continuous since 2020
What control kept the work stable?
Holding the same linguists per language kept product voice and terminology stable across continuous releases.
Where should similar work go next?
Use Localization 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 approval