The PSP design technique that we present is, at its core, the generalization of structured job data via neural network learning techniques. While current PSP design focuses on the optimal use of capital equipment as its primary motivation, the essential competitive advantage of digital presses and workflow is its ability to adapt to different types of content with highest robustness to failure and minimal component-level change; these characteristics are also the same for neural networks. By generalizing the fulfillment order with a representative neural network, we can automatically identify redundancy between jobs and optimize the infrastructure for a particular content mix. By adaptively changing the neural network in the face of different job fulfillment demands, the neural network can also indicate how to transform the current PSP infrastructure to handle a new mix of jobs requests. We apply a structural learning technique based on a subset of Hidden Markov Models, Directed Acyclic Graphics, and then map these neural structures into print shop infrastructure. We will demonstrate our results with real world PSP data, and compare and contrast the current real world PSP design with its neurally designed counterpart.