Ollang scales media localization with 76% less manual processing after integrating AssemblyAI Voice AI
Ollang's multi-agent localization platform required exceptionally accurate transcription as its foundational first step, but existing cloud providers delivered insufficient accuracy for non-English audio—with poor punctuation and capitalization handling that cascaded errors through every downstream workflow and threatened Ollang's value proposition with streaming and broadcast clients.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Client video submitted
Media clients with extensive video libraries submit content requiring production-ready multilingual localization.
Tools used
AssemblyAIUniversal Speech-to-Text API
Outcome
After integrating AssemblyAI's Universal Speech-to-Text API, Ollang achieved a 76% reduction in human-in-the-loop effort, a 30-40% improvement in overall platform accuracy, 97%+ production-ready results for most content types, and a 25% increase in autonomous service orders.
What failed first
Prior cloud transcription providers delivered insufficient accuracy for non-English audio, with poor punctuation and capitalization handling that cascaded errors throughout Ollang's entire multi-agent workflow.