Most AI can describe a clip. Vivu understands what you want out of a clip.
Every company is sitting on years of recorded video its own software cannot read. Vivu reads it and connects it into one structured, queryable layer that teams and agents can reach. We call it the VCG.
Most tools index filenames.Vivu indexes what happened.
Video tools have always indexed the wrapper: the filename, the folder, the upload date. Vivu indexes the content. It reads each recording across signals that normally stay siloed: what is on screen, what is said, who is speaking, the text shown, and exactly when each happens, and connects them into one structure queried by meaning instead of by filename. It is built to reach past the video itself, drawing on the context of the business, so a plain request expands into a precise, intent aware search that returns the moments that fit, not only the ones that match a keyword.
Anyone can caption a clip.No one can rebuild the layer underneath it.
Sending one clip to a model and getting a caption back is a lookup, not a layer. The VCG is the connective tissue: it holds the relationships between moments across thousands of recordings, so a query resolves to the exact moment with its surrounding context, not a guess from a single frame. It compounds with every recording added, and it cannot be reconstructed from a single API call. What makes it defensible is the same thing that makes it useful: the connections, not the clips.
Not another app to log into.The layer the rest of the stack calls.
The VCG is not a closed app only. It is the layer the agents and tools a company already runs can call. A query in plain language returns exact, timestamped moments those workflows can act on, so footage that used to sit in storage flows into the campaigns, tools, and agents already in use. Vivu does not compete for a seat in the workflow. It sits underneath it.
Indexed once.The economics of a layer, not an app.
Vivu indexes each recording once instead of reprocessing it on every query. The cost of reading a video is paid at ingest, which is what lets one layer sit under an entire library instead of a handful of clips, fast and cheap at library scale. Footage stays private to each workspace, with access the customer controls.
Two curves finally crossed.
For years, the video a company recorded and the software it ran lived in separate worlds. Footage piled up faster than anyone could watch it, and the tools that could read a video returned one caption at a time. Both constraints just broke. Every team is wiring agents into daily work, and multimodal models can finally read what is inside a video, not just what it is labeled. The missing piece is the layer between them, the thing that turns raw footage into something an agent can reach and act on. Vivu is building that layer now, while teams are still deciding what their agents will be allowed to touch.
Talk to us.
Tell us about your library and what your team is trying to do with it.