The process of translating new materials into practical devices of benefit to society typically requires substantial time and capital investment. By virtue of their unique geometries and material properties, devices based on nanomaterial structures have unique (opto)electronic characteristics enabling applications not possible with conventional bulk materials. When creating a device based on an individual nanostructure, that structure's exact position needs to be known. Fabricating and measuring nanoscale devices is notoriously labour-intensive, involving searching and alignment before manual routing of electrode layout, or manually performing pick-and-place to transfer these nanostructures onto existing electrode configurations. In a research setting, this need for human intervention is a significant bottleneck that slows the development of new nanomaterials-enabled technologies. Worse still, the slow throughput of this approach precludes its application in any manufacturing setting. We have developed a three-pronged approach - together known as NanoMation - to remove the human intervention required during inspection, research and manufacturing. The first is a system of fiducial markers, "LithoTags", which are optimised for lithography processing - photo-, electron beam-, or nanoimprint lithography. These markers can be easily read by automated microscopy processes. The second is a computer-vision system that can find, sort and filter nanostructures depending on desired properties. Third is a system of computer-adjustable electrode designs where a machine-learning algorithm automatically routes the supporting electrodes to form an entire circuit. These processes will enable a rapid transition from individual prototype devices to high performance integrated systems (e.g. single-unit nanomaterial photodetectors, transistors, or LEDs respectively - to image sensors, integrated circuits, and displays).