Case study
Five hundred hours of long-form transcription across four locales at project-scoped quality review.
A model program needed 500+ hours of long-form transcription across four locales, including Maghrebi Arabic and Indian English, where dialect and length both work against accuracy.
500+ hours - Tamil, Indian English, Maghrebi Arabic, English - Reviewed per engagement rules
110,000+
verified language specialists
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
brands served since 2015
Measured outcomes
Long-form transcription
Project overview
What landed, and what made it hard.
A model program needed 500+ hours of long-form transcription across Tamil, Indian English, Maghrebi Arabic, and English, delivered through a top-100 LSP for AI training.
Delivery snapshot
Long-form transcription
- Client
- A model program (via a top-100 LSP)
- Service
- Long-form transcription
- Languages
- Tamil, Indian English, Maghrebi Arabic, English
- Volume
- 500+ hours
- Quality
- Reviewed per engagement rules
Why this mattered
Outcome before process.
Long-form audio compounds error: a transcriber who drifts over a long file produces data that quietly degrades a model, and dialects like Maghrebi Arabic narrow the qualified pool.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Long-form transcription fails when transcribers tire over length, when dialect handling is inconsistent, or when QA samples too little of each file.
The challenge
The problem to solve
The program needed accuracy held across long files and four locales, including a hard Arabic dialect.
Operating response
What MoniSa changed
MoniSa assigned dialect-matched transcribers per locale and ran QA across each long file, full-file review instead of spot samples, to hold accuracy over length.
- Dialect-matched source Maghrebi Arabic and Indian English were handled by transcribers native to those varieties.
- Full-file QA QA covered each long file end to end, not a short sample, so accuracy did not drift over length.
- Locale consistency Each locale held to its own conventions across the 500+ hours.
Results
Measured outcomes from this engagement.
The program received 500+ hours of long-form transcription across four locales at project-scoped quality review, with accuracy held over long files and dialect-specific varieties.
| Volume | 500+ hours |
|---|---|
| Languages | Tamil, Indian English, Maghrebi Arabic, English |
| Quality | Reviewed per engagement rules |
| Content | Long-form transcription |
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
Long-form transcription needs dialect-matched source and full-file QA, not a generic pool sampling short clips.
What decided the result
What decided the result
Holding accuracy over length and across a hard Arabic dialect mattered more than raw hours.
What buyers can reuse
What buyers can reuse
- Long-form audio compounds transcriber drift, so QA has to cover the whole file, not a sample.
- Hard dialects like Maghrebi Arabic need native transcribers, not a generic Arabic pool.
- The evidence keeps the client and 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.
- Maghrebi Arabic
- Indian English
- Tamil
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Buyer questions
Answers in writing, before you ask for a call.
The questions buyers send before a scope conversation, answered on the page rather than in a meeting. Take them to your team, then send us the one we did not answer.
What was delivered on this engagement?
Volume: 500+ hours. Languages: Tamil, Indian English, Maghrebi Arabic, English. Quality: Reviewed per engagement rules
What control kept the work stable?
Holding accuracy over length and across a hard Arabic dialect mattered more than raw hours.
Where should similar work go next?
Use AI data services for the delivery model, the case studies hub for buyer-side evaluation, and the contact page for a scoped brief.
What happens if you cannot staff one of my language pairs?
You are told before a date is agreed, not after. Coverage is reported pair by pair as staffed today or needing a recruitment window, with the window stated — in writing, while the scope is still being agreed. Nobody new goes onto live work until a pilot batch has been reviewed and signed off. A coverage claim you cannot check before signing is not coverage.
Similar brief
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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