Abstract: A method, computer program product, and system are provided. A first natural language text is received via a user interface. A generative pretrained transformer machine learning model processes the first natural language text and a context to identify a first intent. The processing is based in part on a syntax determined from a set of natural language completion paradigms. The generative transformer machine learning model maps the first set of parameters to a first query. The mapping is associated with a first confidence. The generative transformer machine learning model processes the first set of parameters and the first query to generate a set of execution steps. The processing is performed when the first confidence satisfies a threshold. The set of execution steps is parsed into a query object that is forwarded to a reporting service.

Abstract: A method for making binary predictions for a subject involves obtaining historical data for multiple subjects, the historical data including, for each subject, a feature set and a binary outcome, generating training data from the historical data, and training a predictive model using the training data to predict the outcomes based on the feature sets. The method further includes obtaining historical data including a feature set for a subject under consideration, and predicting a binary outcome for the subject under consideration, based on the feature set associated with the subject under consideration.

Abstract: A method of automating a workflow may include obtaining a completeness graph including conditions applied to attributes of an entity, determining that a missing attribute subset of the attributes lacks a corresponding value, determining that a condition corresponding to a missing attribute of the missing attribute subset is satisfied, and obtaining a value for the missing attribute. The value may be a result of performing a task in the workflow. The method may further include modifying a state of the entity by assigning the value to the missing attribute to obtain a modified state of the entity.

Abstract: Certain aspects of the present disclosure provide techniques for automatically guiding transaction performance. Embodiments include receiving first input from a first user identifying a term of a transaction between the first user and a second user. Embodiments include receiving second input from the second user confirming the term. Embodiments include deploying a smart contract that corresponds to the term on a hash chain. The smart contract may comprise a program that guides performance of the term, and the hash chain may be resistant to modification. Embodiments include receiving, from a management component of the hash chain, a notification that the smart contract has verified through a trusted authority that the term is satisfied.