About vfrog

Vision is the oldest sense. Long before language evolved, animals were using sight to navigate their environment, identify threats, and make split-second decisions. It is the foundational layer of intelligence — the one everything else is built on top of.

AI is only now beginning to catch up.

Before vfrog, we built and deployed computer vision applications for retail — 120 production models, 95%+ accuracy, real deployments in real environments. We know what the process looks like from the inside. And it's broken.

There are 30 million developers worldwide. Fewer than 300,000 specialize in computer vision. The 99% who want to build with vision — who have the use cases, the domain knowledge, and the motivation — hit a wall before they've written a line of code.

There are 50 billion cameras deployed globally today. The vast majority of the visual data they capture is never analyzed — not because the use cases aren't clear, but because building with computer vision has been too expensive, too complex, and too dependent on a small pool of specialists that most teams don't have.

vfrog is the platform we wished had existed when we were building.

Computer vision isn't a niche capability. It's the interface layer for the next era of computing.

Smartglasses are shipping. Humanoid robots are entering production environments. Autonomous systems are moving from research to reality. Every one of these platforms depends on the ability to see, understand, and act on the physical world in real time.

The gap between where physical AI is headed and what most development teams can actually build is enormous. And it's going to define which applications get built — and which don't — over the next decade.

We think the answer isn't larger models pushed harder. It's the right model for the right task — trained on the right data, small enough to run at the edge, accurate enough to be trusted.

That conviction is at the core of everything we build.