This is not a single project. It is a standing transcription operation serving multiple AI-focused companies through LSP partners. 15,000+ hours of audio transcription across 60+ languages, 60+ of them rare, delivered in weekly batch cycles at 98.7% accuracy. The operation runs continuously, with new languages onboarded through a templated process that gets production-ready within days.
The Challenge
AI companies building speech recognition and natural language processing models need transcribed audio data in hundreds of languages. The high-resource languages, English, Spanish, Mandarin, have mature transcription infrastructure. The rare languages do not. When a client needs transcribed audio in Fanti, Chadian Arabic, Tok Pisin, or Teso, the typical vendor response is silence or a 6-week lead time.
The challenge is not just scale. It is consistency at scale. 15,000+ hours across 60+ rare languages means managing hundreds of transcribers working in different scripts (Latin, Arabic, Bengali, Cyrillic), different audio quality conditions, and different transcription conventions. A single transcriber using the wrong orthographic convention in Chadian Arabic can contaminate an entire training dataset.
Clients need weekly delivery cadence. Not monthly. Not “when ready.” Every week, a batch ships. If a language pair cannot meet the weekly window, the client’s ML training pipeline stalls.
Our Approach
We built this operation for repeatability. Every new language pair follows the same onboarding template. Every batch follows the same QA sequence. The system runs whether the language is Fanti or French.
- Templated new-language onboarding: When a new language is requested, we follow a documented playbook: source 3-5 candidate transcribers, run a paid test batch (2-3 hours of audio), evaluate against accuracy and formatting benchmarks, select the top performers, and brief them on project-specific guidelines. This process takes 3-5 business days for most languages. For extremely rare languages, up to 10 days.
- Script-specific QA checklists: We maintain four separate QA frameworks — one each for Latin, Arabic, Bengali, and Cyrillic scripts. Each checklist covers script-specific risks: diacritical mark accuracy for Arabic, conjunct character validation for Bengali, transliteration consistency for Cyrillic-to-Latin pairs, and tone marking for applicable Latin-script languages.
- Double-blind review for first batches: The first two batches from any new transcriber go through double-blind review. Two independent reviewers assess the same audio segment without seeing each other’s output. Disagreements are resolved by a senior linguist. This catches calibration issues before they become systemic.
- Weekly batch delivery with quality gates: Every weekly batch passes through three checkpoints before delivery: transcriber self-review, independent QA reviewer check, and project manager sign-off with spot-check sampling. Batches that fail any checkpoint are held and reworked before the next delivery window.
- Partner coordination layer: Since this operation serves multiple end clients through LSP partners, we maintain a coordination layer that manages project-specific requirements (annotation guidelines, formatting specs, metadata fields) per partner without cross-contaminating data between clients.
MoniSa holds ISO 9001:2015, ISO 27001:2013, and ISO 17100:2015 certification — this operation runs under those governance standards, with separate secure workspaces per project matching each client’s data handling requirements.
Results
| Metric | Result |
|---|---|
| Total volume | 15,000+ hours transcribed |
| Total languages | 60+ (majority rare/low-resource) |
| Script systems | 4 (Latin, Arabic, Bengali, Cyrillic) |
| Accuracy | 98.7% |
| Delivery cadence | Weekly batch cycles |
| New-language onboarding | 3-5 business days (templated) |
| QA methodology | Script-specific checklists + double-blind first-batch review |
The operation continues to expand. New language pairs are added regularly through the templated onboarding process. Partner feedback consistently cites two things: the ability to add rare languages without extended lead times, and the consistency of output quality across batch cycles.
Why MoniSa Was Selected
Why chosen: LSP partners needed a production backbone that could handle rare languages they could not staff in-house — Fanti, Chadian Arabic, Tok Pisin, Teso — without the partners losing control of the client relationship. MoniSa operates as a white-label production layer: the partner’s brand, our production.
Why successful: Templated onboarding for new languages (3-5 days for most, 10 for extremely rare) plus script-specific QA frameworks meant the operation could absorb new language requests without rebuilding the pipeline each time. That repeatability is what turns a one-off project into a standing operation.
Key Takeaways
- Standing operations require templated processes, not hero efforts. The difference between a one-off transcription project and a 15,000+ hour standing operation is repeatability. Documented onboarding, standardized QA checklists, and weekly delivery rhythms turn rare-language transcription from a sourcing problem into an operational process.
- Script-specific QA catches errors that generic checklists miss. A single QA template across Arabic, Bengali, Cyrillic, and Latin scripts would miss half the errors. Each script system has its own failure modes. Separate checklists per script system are not optional at this scale.
- Double-blind review on first batches prevents downstream data contamination. For AI training data, a calibration error in batch 1 that goes undetected propagates through every subsequent batch. The cost of double-blind review on the first two batches is a fraction of the cost of reprocessing contaminated training data.
- Partner coordination at this scale requires project-level data isolation. Serving multiple end clients through LSP partners means annotation guidelines, formatting specs, and metadata fields cannot bleed across projects. Separate secure workspaces per client are not a security nicety. They are an operational requirement when one mis-routed file can violate an NDA.
Related guide: How to Choose an AI Data Annotation Vendor
Need audio transcription across rare languages?
MoniSa Enterprise operates a standing transcription capability across 300+ languages with ISO 9001:2015, ISO 27001:2013, and ISO 17100:2015 certification. Send us your language list and volume requirements — we will confirm capacity and timeline within 48 hours.
