Most AI can describe a clip. Vivu helps you find the moments you want from it.
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.
Results are candidate moments. Review the source footage before relying on a result.
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 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 an intent aware search that helps retrieve candidate moments that fit what was asked, 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 comes back with candidate moments and their surrounding context, not a guess from a single frame. It compounds with every recording added, and it cannot be reconstructed from a single model call. What makes it defensible is the same thing that makes it useful: the connections, not the clips.
Not only an app to log into.The layer the rest of the stack calls.
The VCG is not a closed app only. It is a layer that agents can call: Vivu runs a remote MCP server that any MCP client can connect to. Claude is the main setup, added under Settings → Connectors, and other clients such as ChatGPT use the same address. A query in plain language returns candidate time ranges those workflows can open, check against the source footage, and 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.
Upload once.Search it from every session after.
Video goes into a Vivu project through a secure upload page in the browser, opened from the web app or by Claude when you ask it to. Vivu indexes each file once in its cloud, and from then on the project can be searched again from the web app or from a new Claude conversation, without uploading the footage a second time. Deleting a video or a project removes its media from Vivu's cloud.
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.
Further reading
All pagesSee it on your own video, or talk to us about the library.
Start free and upload a few videos, or bring us the workflow you are trying to fix.