Integrating AI Dubbing Into a Localization Stack: Ollang API, Webhooks, SDK and MCP

Most localization managers do not struggle to find an AI dubbing vendor. They struggle to fit one into an existing operation: a media asset repository on one side, a publishing system on the other, and a review process in between that involves internal editors, external linguists and sign-off gates. A dubbing tool that only works through a web dashboard forces someone to download files, upload them manually, watch a progress bar and re-upload the results. That does not scale past a handful of titles.

This is where AI dubbing API integration becomes the actual buying criterion. The question is not "can the tool dub a video" but "can I create orders programmatically, get notified when they finish, insert a human review step when quality demands it, and push finished assets back into my delivery pipeline without manual handling." Ollang exposes several programmatic surfaces, a REST API, a TypeScript/Node.js SDK, webhooks and a hosted MCP server, and this guide maps each one to a stage of a real localization workflow.

Choose Between Dashboard and Programmatic Workflows

Ollang supports both modes, and most teams end up using both. The dashboard handles uploads (media files or URLs), order creation, editing and review. It is the right place for editors and linguists to work, and it is where you should run your first pilot orders so you understand the order lifecycle before you automate it.

The programmatic layer has three entry points:

• REST API with API-key authentication. This is the core surface: programmatic file uploads, order creation and monitoring, human-review requests, revisions, reruns, and access to projects and folders. If you have an existing orchestration layer, a media asset manager, a TMS, or internal tooling, this is what you integrate against.
• TypeScript and Node.js SDK. If your integration code runs on Node, the SDK wraps the REST endpoints so your engineers are not hand-writing HTTP calls and response parsing. For a localization team with limited engineering capacity, this shortens the build noticeably.
• Hosted MCP server using OAuth 2.0 with PKCE. MCP (Model Context Protocol) lets AI assistants and agent tooling interact with Ollang directly. Ollang also publishes Agent Skills for AI coding tools. This matters if your team is experimenting with agent-driven operations, or if you want a program manager to be able to trigger and query orders through an assistant rather than a custom UI. Because it uses OAuth 2.0 with PKCE rather than a shared API key, access is granted per user session, a meaningful difference discussed in the key-management section below.

A practical split: editors and reviewers use the dashboard; ingestion, order creation and delivery run through the API or SDK; ad-hoc queries and lightweight automation go through MCP.

Upload Assets and Create AI Dubbing Orders

Ollang's ingestion documentation covers direct video uploads up to 30 GB, with MP4 explicitly listed among supported formats, and audio and document uploads up to 100 MB, with MP3 explicitly listed. The public documentation says the upload service supports "MP4, MP3 and more" but does not publish an exhaustive codec and container list, so confirm your specific delivery formats, frame rates, channel layouts, mezzanine codecs, with Ollang before committing a large catalog.

Three ingestion patterns are documented:

1. Direct file upload, via dashboard or programmatically through the API.
2. URL-based ingestion through the dashboard, plus documented YouTube and Vimeo workflows through Ollang's MCP integrations.
3. Script-only input: a text-to-speech-first dubbing flow accepts a .txt script with no source video, useful for narration content where the script is the source of truth.

Orders are not just a file and a language pair. Ollang's order model accepts supporting assets that directly affect output quality: source subtitle files, background/accompaniment audio, guidelines, character lists and glossaries. If you already maintain glossaries and style guides in your TMS, plan to attach them at order creation rather than fixing terminology in review.

The workflow itself: you request an aiDubbing order with a source and target language; the API can create separate orders for multiple target languages from one source. Ollang transcribes the source speech (orchestrating multiple speech-to-text providers), translates it, with support for model selection, custom instructions, terminology memories and project-level guidelines, generates localized speech, and synchronizes and mixes the result. The order model also recognizes a separate accompaniment track, so dialogue can be handled apart from the underlying audio bed.

Deliverables documented in the order model include dubbed audio, a vocals-only dub track (created_ai_dub_vocals_only_audio), a processed background track, and mixed video. Lip sync is documented as an optional capability, configured through Ollang's Visual Translation capability in more recent documentation; if lip sync is a requirement, confirm its packaging commercially. Exact output file formats and encoding profiles are not comprehensively listed publicly, so specify these in your evaluation.

Track Status and Receive Completion Webhooks

Polling an API for order status works at small volume and becomes a liability at scale. Ollang supports both patterns. The API exposes order IDs, language pairs, status, project associations, timestamps and the associated order documents, so you can query state on demand. For event-driven pipelines, Ollang provides webhooks for workflow completion and status changes.

The integration pattern for a localization stack:

• Register a webhook endpoint in your orchestration layer.
• On order creation, store the returned order ID against your internal job record.
• When the completion webhook fires, match it by order ID, fetch the order documents, and route deliverables to the next stage, QC, review or publishing.

Standard AI dubbing turnaround is described by Ollang as minutes or hours rather than days, though no published SLA by video length or configuration was found, so build your pipeline to tolerate variable completion times rather than assuming a fixed window.

Insert Human Review, Revisions and Reruns

For most enterprise content, fully automated dubbing is not the end state, a review gate is. Ollang treats this as a first-class part of the order lifecycle rather than an external process:

• AI-only orders remain editable, rerunnable and assignable. An AI-only workflow is not a dead end; you can pull an order back into editing after delivery.
• AI-plus-human workflows and Level 1 review gates let you require review before final delivery.
• Reviewer sourcing is flexible. Documented options include Ollang-managed linguists, your own internal editors, and external agencies, LSPs and dubbing studios, so you can keep your existing linguist relationships inside the platform.
• Segment-level editing and resynthesis. Editors can refine translated dialogue, pacing, timing and speaker assignments, then rerun speech synthesis for individual segments rather than regenerating the whole order.
• Revisions and reruns are available through the API, which means your escalation logic can live in your own systems: if a stakeholder rejects a deliverable, your tooling requests a revision programmatically instead of opening a ticket.

Ollang also documents configurable QC criteria and escalation rules, for example, routing an order to a linguist when a quality score falls below a threshold. Note that the detailed AI QC scoring and structured annotation features are documented specifically for subtitle translation orders; dubbing-specific review is documented as human review of translations, dialogue, pacing, speaker management and regenerated speech. If you need structured, scored QC on dubbed audio specifically, confirm that with Ollang directly.

Connect Content Repositories and Publishing Systems

Ollang documents workflows connecting with YouTube, Vimeo, Dropbox, Airtable, Notion and Strapi. This is the piece that turns dubbing from an isolated tool into part of a content operation:

• YouTube and Vimeo cover ingestion from video platforms where your source content already lives, documented through Ollang's MCP integrations.
• Dropbox covers file-based exchange with production teams and vendors who work out of shared storage.
• Airtable and Notion cover the operational layer, content calendars, localization trackers and request queues that many teams already run in these tools.
• Strapi covers headless CMS delivery, relevant if your localized video is published through a CMS-driven site or app.

Because these workflows are documented rather than left as an exercise, your integration effort shifts from "build a connector" to "configure a documented pattern." Alongside the dub itself, Ollang's subtitle capabilities, exports including SRT, VTT, ASS, STL, SCC, DFXP and ITT, plus burned-in subtitles, mean the same pipeline can deliver caption files to the same destinations.

Plan Authentication and Account-Scoped Key Management

Ollang's REST API uses API-key authentication, and the documentation is explicit about the scope: API keys are account-scoped and can access every folder, project and order in the account. For a localization manager, this has direct governance consequences:

• Treat the key as an account-level credential. Store it in a secrets manager, never in client-side code or shared documents.
• Do not distribute the key to vendors or contractors who should only see a subset of projects, the key does not enforce that boundary. Use Ollang's role- and assignment-based visibility in the platform for per-user access control instead.
• For user-facing or agent-facing access, prefer the MCP server's OAuth 2.0 with PKCE flow, which authenticates individual users rather than sharing a master credential.

Ollang states SOC 2 certification and positions the platform around enterprise security and governance, with projects, folders, roles, approval gates and workflow-level controls. Details such as SOC 2 report type and specific controls like SSO or data residency are not publicly specified, so include them in your security questionnaire.

How to Evaluate and Get Started

Run a pilot in three passes. First, use the dashboard: upload one representative asset with your glossary and guidelines attached, create an aiDubbing order for one target language, and put a reviewer through the editing and segment-resynthesis workflow. Second, reproduce the same order through the REST API or the TypeScript/Node.js SDK, register a webhook endpoint, and confirm you can retrieve the vocals-only, background and mixed-video deliverables programmatically. Third, connect one documented integration, the repository or CMS closest to your real pipeline, and run an end-to-end job from source system to published asset.

Before contracting, get written confirmation on the items the public documentation leaves open: exact input and output format specifications, the dubbing-specific language pairs you need, lip-sync packaging, rate limits at your expected volume, and turnaround expectations for your content lengths. The programmatic surfaces are documented; those commercial specifics are what your pilot should pressure-test.