AI dubbing goes
commercial for Japan B2B
ElevenLabs partnered with Tooin, Japan's veteran translation firm, to offer end-to-end enterprise dubbing: transcription → translation → TTS → delivery in one contract. This turns video localization from a multi-vendor headache into a single-vendor service.
Tooin handles translation QA —
one contract covers everything
The ElevenLabs and Tooin partnership eliminates the multi-vendor coordination that companies previously had to manage when localizing video content at scale. Transcription, translation, voice generation, and final delivery are handled under a single contract.
Translation quality assurance is handled by Tooin, a veteran firm in Japanese localization. That means the service can handle industry-specific terminology and nuance that pure AI translation still struggles with. ElevenLabs brings the TTS technology; Tooin brings the quality guarantee that makes enterprise procurement straightforward.
Enterprise AI dubbing
just became turn-key
Until now, Japanese enterprise video localization meant either DIY assembly or expensive studio recording. This partnership fills that gap with a managed, quality-guaranteed service.
ElevenLabs partnered with Tooin (Japan's veteran translation firm) for end-to-end enterprise dubbing: transcription → translation QA → TTS → delivery under one B2B contract. Covers both localizing Japanese content into other languages and localizing foreign content into Japanese.
going multilingual
The ElevenLabs × Tooin service fits best for organizations that: ① have large libraries of video content, ② are planning multilingual expansion, and ③ need professional translation quality assurance for compliance or brand reasons. Primary use cases include training videos, product explainers, and marketing content.
The procurement advantage is real: "backed by a professional translation firm" is an argument that tends to clear internal approval processes faster than "we're using AI alone." A practical approach is a small-scale pilot on a non-critical content batch first, then evaluate quality and cost-effectiveness before committing to full-scale deployment.