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 - project-scoped quality review
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
- project-scoped quality review
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 | project-scoped quality review |
| 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
More proof
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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: 500+ hours. Languages: Tamil, Indian English, Maghrebi Arabic, English. Quality: project-scoped quality review
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.
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.