AI Dubbing API Integration: Automating Orders, Review Gates, and Deliveries

Most localization teams that adopt AI dubbing hit the same wall within a few months: the dubbing itself is fast, but everything around it is still manual. Someone downloads source files from the CMS, uploads them to a dubbing tool, waits, checks the output, emails a reviewer, waits again, downloads deliverables, renames them, and pushes them to the distribution platform. Multiply that by twelve target languages and a weekly release schedule, and the team spends more time moving files than managing quality.

The fix is not a faster dubbing engine. It is an AI dubbing API that lets you wire ingestion, order creation, quality control, human review, and delivery into one automated pipeline, with review gates where humans need to intervene and machine handoffs everywhere else. This article maps how to build that pipeline using Ollang's API, based on its documented capabilities.

Designing the Dubbing Automation Architecture

Before writing integration code, decide what your pipeline actually needs to do at each stage. A workable architecture has five layers:

1. Ingestion, source video, audio, or scripts arrive from your CMS, MAM, or storage.
2. Order creation, each asset generates dubbing orders per target language, with the right configuration attached.
3. Processing and gating, AI dubbing runs, automatic QC scores the output, and orders that need human review are routed to reviewers.
4. Event handling, your systems react to status changes without polling.
5. Delivery, approved deliverables are exported and pushed downstream.

Ollang exposes the primitives for all five layers through a REST API authenticated with API keys. That authentication model matters for localization managers because it maps cleanly to how automation is usually deployed: a server-side service or CI job holds a key and acts on behalf of the team, without individual user logins in the loop.

For teams that prefer not to build against raw HTTP, Ollang publishes a TypeScript and Node.js SDK, which gives you typed request and response handling and reduces the amount of glue code your engineers maintain. Ollang also offers a hosted MCP server and Agent Skills, which let AI agents interact with the dubbing platform directly. If your organization is experimenting with agent-driven operations, for example, an internal assistant that creates orders or checks status on request, these give you an integration path that does not require custom API plumbing for every task.

The API supports programmatic file uploads, order creation, project management, revisions, QC, human-review requests, exports, and reruns. In other words, every step you would otherwise perform in a dashboard has an API equivalent, which is the precondition for full automation.

Uploading Assets and Creating Language Orders

Ingestion starts with file uploads. Ollang's documentation lists MOV and MP4 for video and WAV and MP3 for audio. Two additional input types are worth building into your pipeline:

• Timing references. SRT and VTT files can be supplied as supporting assets and used as timing references for dubbing. If your subtitling workflow runs ahead of dubbing, feeding those files in improves timing consistency between the two deliverables.
• M&E tracks. You can upload a clean music-and-effects track, or Ollang can extract or create one from the source. If your post-production team already produces M&E stems, uploading them directly avoids extraction artifacts and gives you cleaner final mixes.

There is also a TTS-first workflow that accepts a TXT script without a source video, useful for voiceover-only content where no source recording exists.

For catalog work, Ollang supports structured bulk uploads across project folders, the documented example permits up to 100 videos in a folder structure. Combined with the Folder → Project → Order hierarchy, this lets you mirror your own content organization: a series becomes a folder, an episode becomes a project, and each target language becomes an order.

Order creation is where per-language configuration happens. The create-order API supports explicit order types for AI dubbing, studio dubbing, closed captions, subtitles, and documents, so the same integration can drive your subtitle pipeline and your dubbing pipeline. Key parameters include:

• Target language, set per order, so a single ingestion event can fan out into one order per language.
• Translation-memory selection, attach the TM relevant to that content line, so recurring terminology and previously approved translations carry forward. For episodic content, this is the difference between consistent and drifting terminology across a season.
• Rush flag, mark time-sensitive orders for prioritization, which lets you encode your own SLA tiers into order creation logic rather than escalating by email.
• Callback URL, register where Ollang should send status events for this order (more on this below).

Projects can also carry glossaries, brand guidelines, voice instructions, character lists, and market-specific requirements as supporting assets, and instructions can be set globally, per folder, or per project. In an automated pipeline, you attach these once at the project level and every order inherits them.

Configuring Dubbing Style, QC, and Review Requirements

Three configuration decisions determine both output quality and pipeline behavior.

Dubbing style. The API's dubbingStyle parameter accepts overdub, lipsync, or audioDescription. Overdub replaces the dialogue track without visual matching; lip sync matches generated speech to the original speaker's lip movements using visual analysis; audio description produces an accessibility track. Encode this choice in your order-creation logic by content type, for example, lip sync for on-camera presenter content, overdub for training narration, audio description where accessibility requirements apply.

Automatic QC. Orders can be created with automatic QC enabled. Ollang's AI QC evaluates output across four default dimensions: accuracy, fluency, tone, and cultural fit. In an automated architecture, these scores become your first gate: outputs that score well can proceed toward delivery, while low-scoring orders are flagged for human attention. The platform also tracks QC-score progression and language-pair analysis, which gives you data for deciding which language pairs need standing human review and which can run AI-only.

Human-review level. Ollang distinguishes Level 0 (AI-generated) from Level 1 (human review added), and human-review requests can be made programmatically. Reviewers, Ollang-managed linguists, internal reviewers, external LSPs, or dubbing studios, can edit translation and dialogue, adjust timing and pacing, rerun synthesis, and deliver the final order. Importantly, AI-only outputs remain editable and can be assigned to reviewers later, so a low automatic-QC score can trigger a review request after the fact rather than forcing you to decide the review level upfront for every order.

Editor visibility can be restricted to assigned orders, and manager approval and sign-off are supported, so the review gate in your pipeline has real access control and accountability behind it rather than a shared inbox.

Using Webhooks to Advance Downstream Workflows

Polling an API for order status is fragile and wasteful at scale. Ollang supports webhooks and callback URLs, with callbacks configurable at order creation. This inverts the integration: instead of your systems asking whether an order is done, Ollang notifies your endpoint when state changes.

Practical uses of callback events in a dubbing pipeline:

• When dubbing completes, trigger your QC-gate logic: check the automatic QC result and either advance the order or create a human-review request.
• When a review is completed and the order is approved, trigger the export and push deliverables to your CDN, MAM, or publishing platform.
• When any order stalls or fails, open a ticket in your project-tracking system automatically, so exceptions surface without anyone watching a dashboard.

For localization managers, the operational effect is that status meetings stop being about "where is the German episode", the pipeline knows, and the tracking system reflects it.

Managing Revisions, Reruns, and Deliverable Exports

No dubbing pipeline is one-shot. Client feedback, terminology corrections, and pacing issues all require changes after first delivery, and this is where many automations break down into manual work.

Ollang exposes revisions and reruns programmatically. Editors can revise translated dialogue, modify localized text, adjust timing and pacing, and rerun speech synthesis after corrections, the resynthesis applies to corrected segments rather than restarting the entire job manually. When feedback arrives through your own review portal or ticketing system, your integration can create a revision on the affected order and route it back through the same QC and approval gates.

Exports are also programmatic. Documented AI-dubbing deliverables include the mixed master video with localized audio, the dubbing audio track, a vocals-only dubbing track, a created M&E track, a source vocals-only track, and embedded-subtitle video, along with dubbing scripts or subtitle exports where configured. For subtitle deliverables, Ollang exports formats including SRT, VTT, STL, ITT, SCC, DFXP, and ASS. Because your downstream platforms have different requirements, a streaming platform may want the audio track alone while a training portal wants the mixed master, build your delivery layer to request the specific deliverables each destination needs, per order, on the approval callback.

Getting Started: A Practical Evaluation Path

Evaluate the integration with a scoped pilot rather than a document review:

1. Pick one content line and two target languages. Choose content representative of your real volume, including at least one asset with an existing M&E track and one without.
2. Build the minimal loop first: API-key authentication, one upload, one order per language with target language, dubbing style, TM, and callback URL set, and a webhook receiver that logs events. The TypeScript/Node.js SDK shortens this step.
3. Test the gates. Enable automatic QC, then deliberately route one order through a human-review request and a rerun to confirm the revision path works end to end.
4. Verify deliverables against your downstream specs. Confirm the exported deliverable types match what your distribution platforms require, and confirm any details the public documentation does not fully specify, such as final container and codec details, directly with Ollang.
5. Confirm governance requirements relevant to your organization, including voice-related consent processes and deployment specifics, before expanding beyond the pilot.

If the pilot loop runs cleanly, asset in, orders out, QC gate, review gate, callback-driven delivery, scaling to your full language set and catalog is a configuration exercise, not a rebuild.