Case study
Subtitle QC across five device types so the viewer experience holds everywhere.
A media catalog needed subtitle QC that held up across Mac, Windows, mobile, iPad, and OTT, because a subtitle that reads fine on one device can break on another.
500+ hours - Tamil, Malayalam, Kannada, Telugu - Mac, Windows, mobile, iPad, OTT
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
Multi-device subtitle QC
Project overview
What landed, and what made it hard.
A media catalog needed subtitle QC across four South Indian languages, verified across Mac, Windows, mobile, iPad, and OTT, because rendering, timing, and line breaks differ by device.
Delivery snapshot
Multi-device subtitle QC
- Client
- A media catalog
- Service
- Subtitle QC across device types
- Languages
- Tamil, Malayalam, Kannada, Telugu
- Volume
- 500+ hours
- Quality
- reviewed quality
Why this mattered
Outcome before process.
A subtitle that passes on a laptop can overflow on a phone or mistime on an OTT box; QC that only checks one device misses what most viewers actually see.
The problem to solve
Why the work was difficult, and what MoniSa changed in-flight.
Subtitle QC fails when it is device-blind, when reviewers are not native to the language, or when 500+ hours dilute the standard.
The challenge
The problem to solve
Subtitle QC fails when it is device-blind, when reviewers are not native to the language, or when 500+ hours dilute the standard.
The catalog needed QC that verified each subtitle across five device types and four languages, held to one bar.
Operating response
What MoniSa changed
MoniSa ran QC against a per-device checklist with native reviewers per language, so timing, rendering, and readability were confirmed on every target screen.
- Per-device checksEach subtitle was verified across Mac, Windows, mobile, iPad, and OTT for timing and rendering.
- Native reviewReviewers native to each of the four languages judged readability and timing.
- One bar at volumeTwenty-eight reviewers worked to the same checklist so quality held across 500+ hours.
Results
Measured outcomes from this engagement.
The catalog received 500+ hours of subtitle QC across four languages and five device types at reviewed quality, with the viewer experience held consistent on every screen.
| Volume | 500+ hours |
|---|---|
| Languages | Tamil, Malayalam, Kannada, Telugu |
| Device types | Mac, Windows, mobile, iPad, OTT |
| Quality | reviewed quality |
| Team | 28 reviewers |
Selection logic
What protected the result.
Device-aware subtitle QC needs native reviewers and a per-device checklist, not a single-screen spot check.
Why the fit was real
Why the fit was real
Device-aware subtitle QC needs native reviewers and a per-device checklist, not a single-screen spot check.
What decided the result
What decided the result
Consistency across devices and languages mattered more than raw QC throughput.
What buyers can reuse
What buyers can reuse
- Subtitle QC is device-specific: a cue that passes on a laptop can break on a phone or an OTT box.
- Native reviewers per language plus a per-device checklist are what keep the viewer experience consistent at volume.
- 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.
- Tamil
- Malayalam
- Kannada
- Telugu
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.
AI data servicesBalanced 20-language assistant data at 85,000 recordings, client details confidential.
AI assistant prompt data
The challenge. A top-10 technology company needed 85,000 prompt recordings across 20 languages for an assistant launch.
What we did. MoniSa sourced diverse speakers across 20 languages and regional variants with per-recording QA.
The result. The company received 85,000 prompt recordings across 20 languages and regional variants.
AI data servicesNatural Hindi-English code-switching speech data, client details confidential.
Bilingual live-speech data
Problem. A voice AI program needed 100 hours of natural Hindi-English bilingual conversation with code-switching.
Action. MoniSa sourced genuinely bilingual speakers and captured unedited conversation with per-recording QA.
Result. The program received 100 hours of bilingual speech from 20 speakers at strong acceptance.
AI data servicesVoice data with a strong first-pass acceptance rate, client details confidential.
Voice data recording
Problem. A speech program needed 150 hours of spec-compliant voice recordings across three languages.
Action. MoniSa ran per-recording QA on every sample for script, audio, and format before submission.
Result. The program received 150 hours across Polish, Dutch, and Australian English with a strong first-pass acceptance rate.
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, Malayalam, Kannada, Telugu. Device types: Mac, Windows, mobile, iPad, OTT
What control kept the work stable?
Consistency across devices and languages mattered more than raw QC throughput.
Where should similar work go next?
Use Multimedia 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 approval