A method comprising: receiving, by one or more processors, an input dataset, the input dataset comprising natural language data; executing, by the one or more processors, a first set of rules against the input dataset, the executing of the first set of rules configured to generate an intermediate dataset and a first set of features; executing, by the one or more processors, a second set of rules against the intermediate dataset, the executing of the second set of rules configured to generate a second set of features; generating, by the one or more processors, a set of outputs, wherein the set of outputs is generated using an artificial intelligence process, the artificial intelligence process configured to generate the set of outputs as a function of the first set of features and the second set of features, wherein the artificial intelligence process comprises a long short term memory (LSTM) model, and wherein the first set of features comprises a set of numeric features and the second set of features comprises a set of categorical features, and wherein the LSTM model is configured to generate the set of outputs based on the set of numeric features and the set of categorical features, the set of outputs including a set of input parameters, a set of output parameters, and a set of classes; configuring, by the one or more processors, a testing process, the testing process configured based on the set of outputs, and the testing process configured to test at least a portion of a test dataset using a set of test conditions; executing, by the one or more processors, the testing process; identifying test data based on the set of input parameters and the set of output parameters; and generating a set of test conditions based on the set of input parameters, the set of output parameters, and the set of classes, wherein the test data identification prioritizes identification of data suitable for testing multiple test conditions for inclusion within the test data. receiving, by one or more processors, an input dataset, the input dataset comprising natural language data; executing, by the one or more processors, a first set of rules against the input dataset, the executing of the first set of rules configured to generate an intermediate dataset and a first set of features; executing, by the one or more processors, a second set of rules against the intermediate dataset, the executing of the second set of rules configured to generate a second set of features; generating, by the one or more processors, a set of outputs, wherein the set of outputs is generated using an artificial intelligence process, the artificial intelligence process configured to generate the set of outputs as a function of the first set of features and the second set of features, wherein the artificial intelligence process comprises a long short term memory (LSTM) model, and wherein the first set of features comprises a set of numeric features and the second set of features comprises a set of categorical features, and wherein the LSTM model is configured to generate the set of outputs based on the set of numeric features and the set of categorical features, the set of outputs including a set of input parameters, a set of output parameters, and a set of classes; wherein the set of outputs is generated using an artificial intelligence process, the artificial intelligence process configured to generate the set of outputs as a function of the first set of features and the second set of features, wherein the artificial intelligence process comprises a long short term memory (LSTM) model, and wherein the first set of features comprises a set of numeric features and the second set of features comprises a set of categorical features, and wherein the LSTM model is configured to generate the set of outputs based on the set of numeric features and the set of categorical features, the set of outputs including a set of input parameters, a set of output parameters, and a set of classes; wherein the artificial intelligence process comprises a long short term memory (LSTM) model, and wherein the first set of features comprises a set of numeric features and the second set of features comprises a set of categorical features, and wherein the LSTM model is configured to generate the set of outputs based on the set of numeric features and the set of categorical features, the set of outputs including a set of input parameters, a set of output parameters, and a set of classes; configuring, by the one or more processors, a testing process, the testing process configured based on the set of outputs, and the testing process configured to test at least a portion of a test dataset using a set of test conditions; the testing process configured based on the set of outputs, and the testing process configured to test at least a portion of a test dataset using a set of test conditions; executing, by the one or more processors, the testing process; identifying test data based on the set of input parameters and the set of output parameters; and generating a set of test conditions based on the set of input parameters, the set of output parameters, and the set of classes, wherein the test data identification prioritizes identification of data suitable for testing multiple test conditions for inclusion within the test data.