Georgi
INDEPENDENTLY AUDITEDISO 27001SOC 2 Type II

Edge AI for
the physical world.

Georgi builds inference models, optimized to run across different devices, that make sense of what’s happening around them, as it happens.

Most devices
just collect data.

Cameras record. Sensors measure. Then it all goes to the cloud to be analysed: slow, costly, and the data leaves the device. Or nobody looks at it at all. What if the device could make sense of it on its own?

Data, understood on the spot.

Some moments can’t wait for a round trip to the cloud. With Georgi, the AI runs on the device itself, right where things happen.

CLOUD AIEDGE AI · GEORGI
SPEEDCLOUDSends data to a server, then waits for the answer.GEORGIDecides on the device, the moment it happens.
CONNECTIONCLOUDStops working when the network drops.GEORGIKeeps working offline.
PRIVACYCLOUDRaw video and audio leave the device.GEORGIRaw data stays on the device. Only results are shared.
COSTCLOUDConstant streaming and cloud computing.GEORGILess bandwidth, lower cloud costs.

One model, across chips.

Device makers want AI in their products. But every chip is different, and most don’t have an AI team. Here’s how Georgi fills the gap.

01 · BUILDInference model

We build a compact model for one job, then quantize and optimize it for inference within a device’s memory and power.

02 · ADAPTChips

Chip makers provide the chip and the software that runs it. We adapt the model to each one, so it runs across chips.

03 · SHIPDevices

Device makers put it in their cameras, sensors and appliances, without an AI team of their own.

Building a smart device?

Whether it’s a camera, a sensor or a connected appliance, add AI without building an AI team, and without being locked to one chip.

Portable

One inference model, many chips. Switch chips, keep your model.

Small

Devices that were too small or too cheap for AI can finally have it.

Open

What the device sees can plug into the AI tools people already use, through open standards.

A partner for chip makers.

Georgi adapts its inference models to your chips,
so your hardware comes with AI that’s ready to use.

  • Tuned to your hardwareEach model is adapted to your NPU and toolchain.
  • A dedicated model teamAn independent team focused on models, so you don’t need one.
  • Raw data stays on the deviceOnly what the model finds is shared.
  • Built in TaiwanRight next to the chip and hardware makers.

See edge AI at work.

We’re starting with a device of our own, so teams can see edge AI at work before building it into their own products.

Q1 2027
Georgi Device

Built on NVIDIA GB10 to support Georgi’s edge AI models. It connects cameras, sensors and business systems, and gives every site a memory: what happened, what was decided, and what worked.

Bring AI to your devices.

Tell us a little about your company, and we’ll get back to you.

Common
questions.

What is Georgi?

Georgi builds edge AI models that run on the device itself. Each model is quantized and optimized to run across chips from different vendors, so cameras, sensors and other devices can make sense of what’s happening around them on the spot.

What is edge AI?

Edge AI runs AI models on the device where things happen, instead of sending everything to a distant cloud. The device can react in the moment, keep working without a constant connection, and keep raw data where it was captured.

What is an inference model?

It’s the part of AI that makes the call. A trained model takes in something new, like a camera frame or a sound, and decides what it is. Georgi’s inference models run right on the device, quantized and optimized to work within a chip’s limited memory and power.

Who is Georgi for?

Device makers bringing AI into cameras, sensors and other connected products. Chip makers work with us as partners, so our models run well on their hardware.

Why does it matter that one model runs across chips?

Every chip is different. With one inference model that runs across chips, device makers can switch chips and keep their model, without building an AI team of their own.

What happens to the data?

The model runs on the device, so raw video and audio don’t need to leave it. What the model finds can be passed to other AI systems through open standards.