● Entertainment·media group
Content metadata auto-tagging·translation pipeline
Validated manual tagging·translation in a 4-week PoC and put it into production. People review only exceptions.
10k+/mo
auto-processed
−70%
human review
multilingual
parallel
● THE CHALLENGE
Why it was hard.
A large volume of content was tagged and translated by hand. Handling multiple languages at once capped speed and consistency. The ask was “automate it with AI,” but the operational data and quality bar weren't defined yet.
Constraints
- Multiple languages in parallel
- Quality bar — rights and accuracy
- The goal was production, not a demo
● OUR APPROACH
What we did.
- DiagnoseDefine content, workflow, and quality bar
- PoC (4 weeks)LLM tagging·translation pipeline + eval for quality
- Move to productionPeople review only exceptions; automation scales up
- MonitoringTrack and improve quality and cost
● OUTCOME
Outcome.
A 4-week PoC validated auto tagging and translation against production data before going live. Tens of thousands of items are processed monthly, with people reviewing only exceptions — cutting review load sharply. Multiple languages run in parallel.
● RELATED INSIGHT
The detailed record, from diagnosis through execution.
● STACK
Stack.
LLM OrchestrationRAGEval PipelineAWS BedrockSageMaker
● RELATED SERVICE
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