Compute for Every Building.
Intelligence for Everyone.

Compute is following the path of electricity — from centralized plants to distributed grids. From something you go to, to something that's simply there.

Most people are building bigger power plants. We're building the grid.

We started from Taiwan and set our sights on the world. Today, Orban operates globally — because the problem we're solving doesn't have borders.

What We See Differently

When AI took off, the world made a collective assumption: AI needs bigger data centers. So the race began. Hyperscalers hoarded GPUs. Nations pledged billions for supercomputing facilities. The consensus became: centralize everything, scale up, build bigger.

We see the opposite.

AI workloads are inherently parallel. Training can be split across nodes. Inference can be batched and sharded. Models can be distributed across layers and devices. These workloads don't demand a single massive cluster. They demand coordination across many.

Distributed isn't a compromise for AI. It's the native architecture.

The technology is ready — distributed computing has moved from academic papers to production deployments. Demand is exploding — every organization is asking how to add AI capabilities. And across the world, massive GPU compute power sits idle, waiting to be activated. Supply and demand have never been more mismatched.

These conditions don't come together often. When they do, infrastructure gets rebuilt from the ground up.

Our First Step

Make AI absurdly simple to build.

A mission this big starts with removing every barrier between an idea and a working AI.

Before we connect every building to a global compute grid, we have to answer a simpler question: why is building AI still so hard?

Today, training a custom AI model requires a team of ML engineers, weeks of infrastructure setup, and a six-figure cloud bill. That's not an AI revolution. That's a bottleneck.

So we built the opposite.

On Orban, you describe what you want your AI to do — answer customer questions, write code, analyze documents. You upload your data. The platform handles everything else: which model to use, how to train it efficiently, where to run it, and how to deploy it as a live API.

Three steps. Thirty seconds.
No machine learning expertise required.

This isn't simplicity for simplicity's sake. It's a distribution strategy. The easier it is to build AI, the more people build. The more people build, the more compute flows through the platform. The more compute flows, the stronger the network becomes.

The company that became the backbone of internet payments didn't do it by building the most powerful processing engine. They did it by making integration seven lines of code. We're doing the same for AI.

We believe compute will become as universal as the cities we live in. But infrastructure only matters if everyone can use it.

Making AI simple isn't our compromise. It's our weapon.

The Future We See

We don't see more mega data centers. We see micro data centers — in every home, every office, every building. The GPU in a workstation. The idle compute in a network storage device. The processing power sitting in smart infrastructure, doing nothing 90% of the time.

Millions of nodes. Already deployed. Already powered on. Waiting to be connected.

This isn't a prediction. This is the trajectory. And Orban is the orchestration layer that makes it work.