Invision AI enables edge devices to interpret the world around them by recognizing objects, tracking their movements, and identifying anomalies. Invision’s AI software is so efficient that it runs on mainstream processors, making deployment to hundreds, or hundreds of thousands, of devices technically and financially viable today. The past decade has seen tremendous progress in the application of neural networks to computer vision. These networks are trained with millions of manually annotated images. When presented with a new image, the trained network reliably locates and identifies objects. Behind the scenes billions of calculations are being made on each video frame: a perfect fit for the huge compute resources of cloud farms. Unfortunately, network bandwidth, latency and privacy concerns make cloud deployments a non-starter for most applications. Running standard deep learning frameworks at the edge requires expensive specialized hardware. Invision has developed a number of technical innovations that collectively make edge AI viable for mainstream applications.