Training current AI models requires a lot of data. Our technology drastically reduces the amount and necessary quality for training current state-of-the-art networks for computer vision tasks, such as classification, detection, segmentation, and object pose estimation. We use a combination of rendering, simulation and modelling approaches to achieve competitive results with very little data. In addition we can drastically reduce the amount of manual labelling – in most cases needing no manual labelling at all.