Prepare assets
Pin the catalogue slice, transcripts and metadata; hold out reviewed samples per language.
Industry blueprint
Media localization
and tagging
Streaming and publishers. Transcribe, translate, dub and tag whole catalogues, with a QC gate before anything is published.
More than half of streaming platforms now use AI dubbing, and catalogues run to tens of thousands of hours in dozens of languages. The pipeline is multi-stage and compute-heavy, per-language models are fine-tuned for brand voice, and a QC reviewer signs off before publication. This design makes each stage a governed leg with a budget and a record.
Illustrative pipeline
Fine-tune per language
Fine-tune speech and translation models for voice and terminology; compare on held-out samples.
Run the catalogue
Transcribe, translate, synthesise and tag as batch jobs, priced before they start.
QC approves
Reviewers approve per title and language; only approved assets are published.
What the record keeps
The catalogue slice, the fine-tuned models and their scores, each batch job's estimate and spend, QC decisions, and the published asset versions.
Partner brings
- Catalogue assets and rights metadata
- Speech, translation and voice models
- Connector to the media asset system
Cairn brings
- Per-language fine-tune with held-out comparison
- Batch jobs with the cost gate
- QC approval gate
- A record of every published asset
Business value to test
Cost per finished hour, QC rejection rate and time to publish per language.
Keep the decision explicit
Nothing is published without a recorded QC approval per title and language.
Close the feedback loop
QC rejections become evaluation samples for the next fine-tune.
Reference design, not a shipped domain pack. Speech and translation operators are the partner's; Cairn's training, evaluation and batch pipelines carry them.
See how failure can trigger a reviewed improvement cycle →Bring a catalogue and a language list.
Talk about your QC process, voice requirements and volumes.