Edge AI runs models on or near the device where data is created, such as a phone, camera, sensor, vehicle, or factory machine, rather than shipping everything to a remote data center. The intelligence lives at the edge of the network.

It matters for speed, privacy, and resilience. Decisions happen in milliseconds with no round trip, sensitive data can stay on the device, and the system keeps working even when connectivity drops. The cost is that edge hardware is constrained, so models usually have to be made smaller through techniques like quantization or distillation.

At arosplatforms we consider edge AI when latency, data residency, or unreliable connectivity rule out a pure cloud design. We often pair a compact on-device model with a more capable cloud model, putting the right work in the right place rather than forcing one pattern everywhere.