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
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
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
- project-scoped quality review
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
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 checks Each subtitle was verified across Mac, Windows, mobile, iPad, and OTT for timing and rendering.
- Native review Reviewers native to each of the four languages judged readability and timing.
- One bar at volume Twenty-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 project-scoped quality review, 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 | project-scoped quality review |
| Team | 28 reviewers |
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
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
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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, 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 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.