Introducing Primate Vision · Primate Intelligence

Today we’re launching Primate Vision — a real-time video intelligence API built on Darwin, our JEPA video model.

If you build computer vision systems for a living, you already know the choice we’ve all been stuck with. YOLO-class detectors are reliable and fast, but they only see objects — never what those objects are doing. VLMs can describe a scene in natural language, but ask the same question twice and you’ll get two different answers, because generative models sample from a distribution instead of computing a function. You’ve been picking your poison: rigid and trustworthy, or flexible and unreliable. Never both.

We don’t think that’s a law of nature. We think it’s an artefact of the wrong architecture — pixel-space prediction — and we’ve spent the last year building the alternative.

What Primate Vision actually does

Primate Vision takes video — a clip or a live stream — and returns structured, deterministic output: what entities are present, what they’re doing, how long they’ve been doing it, and how confident the model is. Not bounding boxes you have to interpret. Not a paragraph of prose you have to parse. JSON your alerting logic can act on directly.

{
  "entity": "person",
  "action": "loitering",
  "location": { "zone": "entrance", "relative_position": "door_adjacent" },
  "temporal": { "duration_seconds": 94, "first_seen": "T-00:01:34" },
  "confidence": 0.97,
  "deterministic": true
}

That deterministic: true isn’t a marketing flourish. Run the same clip through the API twice and you get the same JSON, byte-identical, because Darwin predicts in representation space, not pixel space — there’s no sampling step to introduce variance in the first place. Why that matters more than raw accuracy.

Why JEPA, and why now

Joint Embedding Predictive Architecture — the approach Yann LeCun’s team pioneered at Meta — has spent the last two years attracting the kind of investment usually reserved for entire new categories: AMI at a $3.5B valuation, World Labs closing $1B, NVIDIA backing Cosmos. We wrote about what that means for CV developers specifically, but the short version is: the research community has converged on JEPA-style representation learning as the right path to models that understand physical scenes rather than pattern-match on pixels. We agree, and we built Darwin to prove it works in production, not just in a paper.

Darwin is our video JEPA model — trained on 8 H100 GPUs over three weeks, roughly 100x less data than comparable V-JEPA 2 efforts, and it still matches or exceeds those benchmarks: ~73% on Something-Something V2, ~78% on Kinetics-700, ~88% on ImageNet-1K. Full numbers. Because it never generates pixels, it’s light enough to run in real time on a commodity CPU — not just a datacenter GPU, which matters a great deal once you start thinking about edge deployment on the camera itself instead of a cloud round-trip.

Built for the pipeline you already have

Primate Vision isn’t asking you to rip out your stack. If you’re running YOLO for detection today, that’s still a reasonable choice for pure “is there a person in frame” questions. Primate Vision slots in for the part that’s currently held together with custom glue code: action classification, temporal state tracking, loitering timers, alert deduplication. Here’s what that integration actually looks like — the short version is it collapses dozens of lines of bespoke tracking logic into one API call.

Measured, not estimated: 45ms p50 per-frame inference, 11.8 fps sustained analysis, live on /performance against the real production API, refreshed against real traffic — not a slide from a pitch deck.

Try it now

No signup, no sales call, no waitlist. Get a sandbox key with one request and run it against your own footage right now:

curl -s -X POST https://api.primateintelligence.ai/v1/sandbox

We built Primate Vision because we were tired of watching good CV teams choose between trustworthy and capable. You shouldn’t have to. Go try it, and tell us what breaks.

Matt Miesnieks is CEO and co-founder of Primate Intelligence. Previously co-founder of 6D.ai (acquired by Niantic).