The video AI agent that understands your whole library
Jockey is the first AI that understands your entire media library just like you do, searching by person, moment, or context across every photo and video you've captured. Powered by TwelveLabs' advanced model stack, Jockey improves automatically with every update. Whether you need to connect via MCP for Claude/ChatGPT or build custom applications using our API, Jockey makes your media library instantly searchable and accessible.
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
About Jockey by TwelveLabs on Product Hunt
“The video AI agent that understands your whole library”
Jockey by TwelveLabs launched on Product Hunt on July 21st, 2026 and earned 210 upvotes and 28 comments, placing #7 on the daily leaderboard. Jockey is the first AI that understands your entire media library just like you do, searching by person, moment, or context across every photo and video you've captured. Powered by TwelveLabs' advanced model stack, Jockey improves automatically with every update. Whether you need to connect via MCP for Claude/ChatGPT or build custom applications using our API, Jockey makes your media library instantly searchable and accessible.
On the analytics side, Jockey by TwelveLabs competes within Productivity, Artificial Intelligence and Video — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Jockey by TwelveLabs performed against the three products that launched closest to it on the same day.
Who hunted Jockey by TwelveLabs ?
Jockey by TwelveLabs was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
fmerian
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