Abstract: Systems and methods of generating an agent, including: generating a plurality of contextual relationships that exist between data points in a dataset by applying a large language model (LLM) to the dataset, determining at least one insight based on the dataset with the generated plurality of contextual relationships, determining at least one query for each determined at least one insight, receiving a question for the dataset, if the received question is associated with the determined at least one insight, applying, by the processor, the LLM on the determined at least one query for the associated determined at least one insight, generating the agent by the LLM based on the determined at least one query, and updating the LLM based on performance of the generated agent.

Abstract: Systems and methods may implement database technology using distributed logical unit repositories (DLURs). DLURs may use a database structure related to a specific logical unit such as a customer, employee, or the like. Information used in DLUR database structures may include data, database structure, functions, and the like that helps form a complete model for a logical unit. In one embodiment, queries to a system concerning entities can be answered immediately by accessing a database using DLURs, which obviates the need to consult a number of databases in parallel and greatly reduces memory and time required to provide the requested information.

Abstract: The invention comprises systems and methods for implementation of database technology using ‘Distributed logical unit repositories’ (DLUR). This is a new type of database which contains information and database structure related to a specific logical unit such as a customer, employee or the like. The information comprises data, database structure, functions, and any other data necessary to form a complete model of the information concerning the logical unit. Queries concerning entities can then be answered immediately by accessing this database, obviating the step of consulting a number of databases in parallel and greatly reducing memory and time required.