Mimir's integration with TwelveLabs brings human-like video understanding directly into the Mimir interface. Mimir is the system of record where content lives, gets organized, and moves through production; TwelveLabs is the intelligence layer that understands what's inside that content — every scene, word, face, object, and action, indexed and made searchable. Together, they turn a media archive from a place where content is simply stored into one where it can be found, understood, and repurposed.
TwelveLabs builds multimodal foundation models purpose-built for video understanding, powering the kind of semantic search, summarization, and metadata generation that used to require a person watching every frame. Customers can search using natural language, and not just on metadata tags, but on content, context, and meaning — surfacing footage that was previously undiscoverable without someone having already tagged it correctly.
Semantic search results for 'people running' in Mimir, powered by TwelveLabs
Inside Mimir, that means searching a full video archive for concepts like "stairs" or "people running" and getting results without a single human-created tag behind them, flagging compliance issues like violence or graphic content automatically, and finding specific people or moments — even a goal celebration from a named player — by uploading an image rather than relying on a name entered in advance. The result is an archive where organizations can find not just the content they were looking for, but the content they should have been looking for.
Image-based player search, shown here in TwelveLabs interface, returning ranked goal celebration results
Read more about the Mimir and TwelveLabs integration here.