Vitra AI

The platform detects text inside an image and replaces it with the target language while preserving position, size, and visual hierarchy. It works as an AI translator for images rather than a design template, so you can translate image text across a run of assets instead of opening each file. Editable layers mean a designer can still adjust the result afterward.

Videos get a translated script, a native-sounding voice track, subtitles, and timing matched to the original cut. Lip sync is adjusted per language, which matters for on-camera presenters. The output keeps the source edit, so you don't re-edit the timeline for each market.

Vitra TMS stores approved wording, glossaries, style guides, and product context next to the work. A term approved for a web page is reused in an image, a document, and a dub. Back-translation checks flag meaning that shifted, which is the failure mode that plain machine translation hides.

A workflow chains image, video, language, and format steps into one run, then repeats it as a template. Agents handle the repeat work while people review the exceptions. Runs are logged node by node against an append-only ledger, so you can trace what changed and when.

Every asset gets a check for quality and regional compliance, with a verdict of approved, review, or blocked plus the evidence behind it. Sensitive steps wait for a named approver while other branches keep running. Approvals can be handled from a phone.

Every capability is callable, so a localization platform can sit inside your existing stack instead of beside it. Teams trigger runs over MCP, REST, SDK, CLI, or connectors such as n8n, Make, and Zapier. An AI agent can discover the same skills and call them as tools.