A product team ships a feature video, a release note explainer, or an in-app onboarding clip, and the localization step still looks like this: export the final cut, email it to a vendor, wait for a quote, wait again for turnaround, receive a folder of subtitle files back, and manually verify they sync before anyone pushes them to the CDN. Every other stage of the release, encoding, transcoding, CDN distribution, QA, is automated and instrumented. Localization is the one step still running on file attachments and human coordination.
That gap is the argument: media localization, including subtitles, captions, dubbing, and audio description, has to be modeled as a pipeline stage a product team can call programmatically, not a side process a localization team manages by hand after the fact. This is an architecture issue. If your release pipeline treats localization as an external, manual dependency, your global launch dates will always trail your domestic ones by however long the vendor round-trip takes.
What Ollang's media surface actually covers
Ollang is not a subtitle tool bolted onto a translation memory system. It is built as an execution layer that takes a source asset, video, audio, document, image, subtitle file, or strings file, and produces reviewable localization outputs through the same order and project structure across every modality. For a PM, the relevant part of that surface breaks into four order types.
Subtitles and closed captions. This is the widest end of the funnel. Ollang can generate captions from source audio, translate them, and export SRT, VTT, ASS, STL, SCC, DFXP, ITT, DOCX, XLSX, and broadcast-compatible formats. That range matters operationally. SRT and VTT cover web and app players; SCC and DFXP cover broadcast and OTT delivery requirements that many modern subtitle tools drop support for once you move past YouTube-style use cases. DOCX and XLSX exports exist because localization QA and legal review teams still work in spreadsheets and documents, not subtitle editors, so the execution layer must support both workflows.
AI dubbing. Ollang's AI dubbing order type produces multilingual dubbed audio and mixed video with optional lip sync or audio description. It also supports script-only input, TTS-first flows that accept a .txt script with no source video, which is useful when a team generates narrated product tours or training content before the final video cut exists.
Studio dubbing. This is a distinct order category, not a tier of the AI dubbing flow. It routes through Ollang's network of professional voice talent and dubbing studios, and outputs are synchronized, mixed with the original audio bed, and output in broadcast-ready format, including M&E track, stereo, and 5.1 surround. A PM choosing between AI dubbing and studio dubbing is choosing between turnaround speed and production-grade voice acting, and the fact that both are addressable as order types in the same system, rather than one being a product and the other a manual services add-on, makes the choice a pipeline decision instead of a procurement decision.
Audio description. Audio description is treated as its own order type alongside subtitles, captions, and dubbing rather than a checkbox inside one of them. This matters for teams under accessibility compliance obligations who need it addressable independently of whether a video is also being dubbed.
Ollang's own order taxonomy confirms this structure: the platform's documented order types are AI Dubbing, Subtitle Translation, CC, Document, Studio Dubbing, Visual Translation, Website Translation, and the media-specific documentation goes deeper into subtitle export formats, AI dubbing outputs, audio description, and studio dubbing. For a PM the practical read is that these are not features nested inside a video localization product tier. They are independently addressable order types with their own inputs, outputs, and workflows, which is what you need if your release pipeline has to route different video assets differently, for example a marketing launch video that needs studio dubbing and audio description, while an internal training clip might only need machine-generated captions in three languages.
