Every demo in our Lab runs entirely in your browser — no server, no sign-up, nothing leaves the page. That is the same engineering discipline we apply to phones, wearables and embedded hardware: make the model small enough, fast enough and reliable enough to live where the user is.
The problem with cloud-only AI
Cloud inference is the default because it is easy — but it means every interaction pays a network round-trip, every byte of user data travels to someone else’s computer, and the feature dies when connectivity does. For health signals, camera feeds and always-on assistants, that trade is often unacceptable.
What we do
We take models — off-the-shelf or fine-tuned on your data — and engineer them onto real devices: quantization and distillation to fit the memory budget, hardware-aware optimization for the target chip, and the product engineering around the model (caching, fallbacks, update paths) that makes it dependable in users’ hands.
We run this discipline on ourselves first: the Lab demos on our homepage run entirely on-device in your browser — the same stack and the same care we bring to client work.
The capability ladder
Teams usually start smaller: an AI-native product build, then assistants and agents, then fine-tuned and on-device models as the product’s edge sharpens. You can enter the ladder at any rung.
Building a device, or exploring a hardware AI use case? Tell us about it — we reply within one business day.