Jockey by TwelveLabs

Was lucky enough to get access to Jockey a week or so ago (thank you team!), and have been blown away at the use cases we've already uncovered. It's changing the way we look at creative strategy across our roster of clients, and we've found some novel ways to extrapolate learnings that are informing some of our performance marketing campaigns that are already showing positive uplift.Go TwelveLabs!

Finally gave this a spin on a few clips and the semantic search actually nails what's happening in the scene, not just matching keywords. Impressed it picked up on subtle actions without me tagging anything.

A live collaboration mode would be huge, letting a team tag and comment on different timestamps together while the AI pulls those notes into a shared summary report.

Honestly impressed by how well it picks up on subtle visual cues in long videos, not just obvious keywords. Searched a 40 minute documentary for a specific gesture and it nailed it in seconds.

One thing that would make this way more useful for me: a timeline-based search view where I can scrub to the exact moment a concept appears, instead of just getting text hits. Right now it sounds like the results are summaries, but for video editing workflows I really need frame-accurate jumps. Would love to see that built in.

honestly the search across hours of footage feels almost scary good, like you throw in a rough idea and it actually pulls the right moments out without much fuss.

honestly the search is way better than i expected, threw in some random clips and it actually pulled out the exact scenes i was thinking of. pretty wild that you can basically ask it questions about what's happening in the video.

A browser extension that lets you right-click any video and instantly get a chapter breakdown or summary using your Pegasus model would be huge, especially for longer YouTube content or lectures where I don't always want to scrub manually.

finally a video ai that actually finds the moment i describe instead of just dumping timestamps. tried it on some old footage and the semantic search picked out exactly the scene i was thinking of.

would be cool if you added a built-in timeline view so you can jump straight to the exact moment a search result happens in the video. right now i think you only get timestamps in text, which is helpful but still requires manual scrubbing. basically a visual scrubber with the matched segments highlighted would save a lot of clicks.

the corpus-level reasoning is the interesting jump here, most video search tools stop at "find the clip" and Jockey is going further to "reason across everything and build the reel." one thing I'm curious about that's different from the privacy/consistency questions above: as you keep adding new footage to a library over time, does Jockey only need to embed the new additions, or does growing the corpus mean periodically reprocessing the whole thing to keep the reasoning layer coherent?

Great name Aiden! The "no tags needed" pitch is the part I'd want to/ am going to stress test. Natural language search over video is great for discovery, but for compliance/legal use cases you usually want deterministic, auditable categories, not a model's best guess at a scene. Curious how consistent Pegasus's segmentation is run to run on the same clip.

I'm Aiden, co-founder and CTO at TwelveLabs. Super excited to share Jockey with you today.Most media search is still metadata search: filenames, timestamps, maybe some object tags from an older computer vision model. None of that captures what's actually happening in your footage: who's in a scene, what they're doing, the context, dialogue, on-screen text. For that you need models that natively understand time and space in video, not a bag of sampled frames.That's our stack. Marengo, our embedding model, resolves a query like "the moment we almost missed the flight" to real retrieval across video and images, not keyword matching. Pegasus, our video-language model, segments an entire video on a schema you define and returns structured, timestamped moments. Jockey is a unified agentic system that reasons across your videos and images: a reasoning model plus a memory layer that builds a knowledge store from your corpus, so it can decompose a query, retrieve, segment, and reason across the whole thing.The point is a corpus-level understanding you can act on. Point Jockey at thousands of videos and images, say "cut me a highlight reel" or "pull the best viral moments," and it comes back with timestamped cuts you can use. The model-only approach can't do that as dumping one video into a context window is bound to a single file, and a single forward pass runs out of room fast. It can tell you about one video; it can't reason across your catalog or build a reel from thousands.Because the models are what we ship and improve continuously, Jockey's reasoning and retrieval quality improve as we push new versions, meaning no re-integration on your end.Two ways in:MCP server: connect Jockey as a tool in Claude and query your library directly. ChatGPT coming soon.API: full programmatic access to build custom retrieval or agent workflows on your own library.This is a research preview, so if you hit edge cases (ambiguous queries, retrieval misses, latency) I want to hear about them. Let us know anytime! Best,Aiden 

The "understands your whole media library" angle is really compelling. I already have product screenshots, launch videos, screen recordings, demos, and random clips scattered everywhere, and finding one specific moment usually depends on remembering the filename or roughly when it was created.Searching by person, moment, or context feels much closer to how people actually remember media. connecting that through MCP is interesting too, because an agent could finally find the exact clip or screenshot needed for a task instead of asking me to dig through folders.. :) Curious how Jockey handles privacy and indexing for very large personal or company libraries.