Most growth teams that run multilingual video campaigns don't have a dubbing problem. They have a handoff problem. The dubbing itself increasingly happens through AI, but the work around it, downloading source files, uploading them to a vendor portal, emailing revision notes, checking a dashboard for status, downloading finished files, re-uploading them to a CMS or video host, still happens by hand. That manual layer is where campaigns lose days and where errors creep in.
The fix is AI dubbing API integration: treating dubbing as one automated stage in your existing content pipeline rather than a separate destination your team visits. This article walks through how to do that with Ollang, which exposes its dubbing workflow through API-key-authenticated REST endpoints, webhooks, a hosted MCP server, a TypeScript/Node.js SDK, and documented workflows for content systems including Vimeo, YouTube, Dropbox, Contentful, and Webflow.
Where Manual Handoffs Slow Multilingual Campaigns
Consider a typical multilingual launch. Marketing finishes a hero video. Someone downloads it, uploads it to a localization tool, selects six languages, and waits. When the dubs come back, someone else spot-checks them, flags two lines for revision through email or a shared doc, waits again, then manually uploads the final files to the video host and updates the localized landing pages in the CMS.
Every step in that chain has the same failure modes: it depends on a specific person being available, it produces no machine-readable status, and it doesn't scale. Ten videos in six languages is sixty deliverables to track. If your campaign calendar runs weekly, the tracking work alone becomes a part-time job.
Orchestration tools already solve this class of problem elsewhere in the growth stack, CI/CD for code, workflow automation for lead routing, publishing pipelines for content. Dubbing can plug into the same pattern, but only if the dubbing platform exposes the full lifecycle programmatically: ingestion, order creation, status, review, revision, and delivery. Ollang documents endpoints and integrations across that entire lifecycle, which is what makes it worth evaluating from an integration standpoint rather than only a quality standpoint.
AI Dubbing API Integration Step One: Uploading Assets and Creating Orders
Ollang's REST API authenticates with an API key, so integration starts the same way as any other service in your stack: provision a key, store it in your secrets manager, and call the endpoints from your orchestration layer or the TypeScript/Node.js SDK.
Ingestion is the first automation target. The direct-upload API accepts video (MP4, AVI, MOV, WMV, FLV, MKV) and audio (MP3, WAV, AAC, M4A, FLAC, OGG), with a documented 30 GB limit for video files. You can also upload subtitle files in SRT or VTT, or a plain-text script for TTS-first workflows that don't start from a source video. Beyond the primary media, the platform's asset model supports the supporting materials that determine dub quality: source subtitles, music-and-effects audio, character lists, guidelines, and glossaries. If your team maintains brand terminology, your integration can attach it to every order automatically instead of relying on someone to remember.
Once an asset exists, you create an order. The API exposes aiDubbing as a distinct order type. You specify one or more target languages and whether the order is AI-only or includes human review. One detail matters for how you model this in your own systems: each target language produces a separate order ID. That's the right granularity for a growth pipeline, French can finish, pass review, and publish while German is still in revision, but it means your database or workflow tool should track deliverables per language, not per source video.
Assets and orders sit inside a folder/project/order hierarchy, so a campaign can map cleanly to a project, with notes and instructions attached at upload time. That structure keeps automated orders organized for the humans who will eventually review them.
Monitoring Jobs with Status Endpoints and Webhooks
Polling a dashboard is the manual habit that integration should kill first. Ollang provides two mechanisms to replace it.
Status endpoints let your systems query order state on demand. This suits synchronous checks, a nightly reconciliation job that confirms every order in your campaign tracker matches Ollang's actual state, or a pre-publish gate that verifies a dub is complete before a page goes live.
Webhooks and callbacks handle the event-driven side. Instead of asking "is it done yet," your endpoint receives a notification when something changes, and your pipeline reacts: post a message to the localization channel, advance the record in your project tracker, or trigger the next stage automatically, pull the dubbed output and push it toward its destination without anyone touching a file.
In practice you'll want both. Webhooks drive the real-time flow; status endpoints provide the source of truth when you need to reconcile or when a webhook delivery fails. If you already run an event bus or workflow engine, the dubbing lifecycle becomes just another event stream feeding it.
On outputs: the platform's asset model includes AI-generated dubbed audio, dubbed vocals-only audio, dubbed audio mixed with the background track, and mixed localized video. Vocals-only output matters more than it first appears for growth work, it lets your own post-production or templating pipeline lay dubbed dialogue over campaign-specific music or sound design without a round trip.