Video understanding
We turn unstructured content into structured, queryable artifacts.
Niravi reads every second of a video, what is seen, said and heard, and writes it down for people, apps and AI agents to search.
The problem
Search reads what someone typed about a video, never what happens in it.
An hour of narration, places, animals and sound
Niravi estimates, October 2026: new video across YouTube, TV, raw footage and AI training sets; YouTube’s public library; the cost of logging an hour by hand. The file and tags above are illustrative.
The solution
Niravi reads a video once and writes down everything in it: who is there, what is happening, what is said and heard, time-coded and searchable.
A ten-second excerpt from one record, simplified. A full record also holds people and speakers, on-screen text, a summary, subtitles and search vectors.
Our innovation
Picture, speech, sound and text together, in a single pass, with the right model for each moment. Two products sit on the same core: Niravi ACE, our creator app, and Niravi Arrow, our enterprise engine.
Shots and scenes, objects, faces grouped into people, a sentence describing every shot.
Word-timed transcripts in the language spoken, with speakers told apart.
Speech, music and silence on one timeline, and the sound events between them.
Lower thirds, tickers, slides and burned-in captions, read off the frame.
Summaries and search vectors, so a plain-language question finds the moment.
Cuts where the picture changes meaning, not where the pixels change.
Looks only as hard as each moment needs, then points at the answer with a box and a timecode.
Set up for a new industry by configuration. No new code, no new model.
Proof
On four standard text-to-video retrieval benchmarks Niravi is level with the category leader, TwelveLabs Marengo 3.0: ahead on MSR-VTT, within three points on the rest. Every query and every result is published.
Explore the results →Applications
One record of every video, three businesses on top of it.
Creators find, cut and publish from their own library, with an agent.
Creators, studios, agencies
Search years of footage by what happens in it, not by the tags.
News, media, sports, advertising
Video that cars and robots, and the models behind them, can learn from.
Automotive, robotics, AI labs
Two ways to use it
Creators use the app. Companies plug the engine into their own systems. Both run on the same harness.
App · for creators
A Mac app to find, edit and publish your own video. You ask Nira, the AI agent, and it does the work. The harness runs on your Mac and your files stay there.
Free to start · macOS 15+, Apple silicon
If you choose, Niravi ACE can use your own Google Cloud credit to make pictures and clips, and publish the videos you make to your YouTube channel, only after you confirm. How Niravi ACE uses Google and YouTube data.
API · for enterprises
Send a video library once, then search it and read its records from any system or AI agent. A REST API, a TypeScript SDK, and an MCP server so your agents can use it with no glue code. Runs in our cloud, or on-premises inside your own infrastructure.
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