A computer-implemented method for selecting an on-demand computer network technology configuration, the computer-implemented method comprising: receiving a request from a computing device, the request comprising a plurality of properties, the request being a transfer request having two associated endpoints that can communicate with each other, wherein the plurality of properties comprises context properties, endpoint properties, endpoint capability properties, and transfer properties; processing the request using a plurality of classifiers to generate a plurality of request classes associated with the plurality of properties, the plurality of classifiers including a payload classifier, a destination classifier, a cost constraints classifier and a time constraints classifier; processing the plurality of request classes using a first neural network to identify one or more technologies relevant to the request, the first neural network being a technology selection layer including a plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5), each of the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5) of the technology selection layer representing a class of technology that may be utilized to optimize the transfer request based on constraints associated with the request, wherein the following activation function is used for each of the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5): ∑ i = 1 n a x i n ≥ θ, where a ( x) is an acceptance function that checks an ability of the class of technology to satisfy classified constraints of the request and returns a value for each of the plurality of classifiers, ∑ i = 1 n a x i n is an average acceptance, n is a number of the plurality of classifiers, and θ is a threshold, wherein each of the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5) is activated and can provide output to a configuration selection layer if the average acceptance ∑ i = 1 n a x i n for the n classifiers x i is greater than the threshold θ; processing each of the one or more technologies using a second neural network to identify one or more technology configurations for each of the one or more technologies, the second neural network being the configuration selection layer including a plurality of neurons (L2.1, L2.2, L2.3, L2.4, L2.5), each of the plurality of neurons (L2.1, L2.2, L2.3, L2.4, L2.5) of the configuration selection layer representing a configuration class, the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5) of the technology selection layer providing input to each of the plurality of neurons (L2.1, L2.2, L2.3, L2.4, L2.5) of the configuration selection layer; processing each of the one or more technology configurations to identify a target technology configuration; and providing the target technology configuration to a network management subsystem that is responsible for a setup and configuration of a network for the target technology configuration. receiving a request from a computing device, the request comprising a plurality of properties, the request being a transfer request having two associated endpoints that can communicate with each other, wherein the plurality of properties comprises context properties, endpoint properties, endpoint capability properties, and transfer properties; processing the request using a plurality of classifiers to generate a plurality of request classes associated with the plurality of properties, the plurality of classifiers including a payload classifier, a destination classifier, a cost constraints classifier and a time constraints classifier; processing the plurality of request classes using a first neural network to identify one or more technologies relevant to the request, the first neural network being a technology selection layer including a plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5), each of the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5) of the technology selection layer representing a class of technology that may be utilized to optimize the transfer request based on constraints associated with the request, wherein the following activation function is used for each of the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5): ∑ i = 1 n a x i n ≥ θ, where a ( x) is an acceptance function that checks an ability of the class of technology to satisfy classified constraints of the request and returns a value for each of the plurality of classifiers, ∑ i = 1 n a x i n is an average acceptance, n is a number of the plurality of classifiers, and θ is a threshold, wherein each of the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5) is activated and can provide output to a configuration selection layer if the average acceptance ∑ i = 1 n a x i n for the n classifiers x i is greater than the threshold θ; processing each of the one or more technologies using a second neural network to identify one or more technology configurations for each of the one or more technologies, the second neural network being the configuration selection layer including a plurality of neurons (L2.1, L2.2, L2.3, L2.4, L2.5), each of the plurality of neurons (L2.1, L2.2, L2.3, L2.4, L2.5) of the configuration selection layer representing a configuration class, the plurality of neurons (L1.1, L1.2, L1.3, L1.4, L1.5) of the technology selection layer providing input to each of the plurality of neurons (L2.1, L2.2, L2.3, L2.4, L2.5) of the configuration selection layer; processing each of the one or more technology configurations to identify a target technology configuration; and providing the target technology configuration to a network management subsystem that is responsible for a setup and configuration of a network for the target technology configuration.