Citibank, N.a.

Ashutosh Pandey

Vaibhav Kumar

Linfeng Yu

Tanujit Ghosh

Leena Mary Francis

Miriam Silver

Hila Gardi

Hadar Karby

David Benamu

Mor Gelberg

Systems and methods for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph are disclosed. The system receives a user-specified query indicating a request to access a set of data objects. The system then performs natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query. The system then accesses a metadata graph to determine a node corresponding to the set of phrases. Using a location identifier corresponding to the determined node, the system determines a data silo storing at least one data object of the set of data objects. The system then generates for display, on a graphical user interface, a visual representation of the at least one data object.

Accessing siloed data across disparate locations via a unified metadata graph systems and methods

A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.

Automating efficient deployment of artificial intelligence models

A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.

Artificial intelligence sandbox for automating development of AI models

Systems and methods for reducing data retrieval times when accessing siloed data across disparate locations by generating a unified metadata graph via a Retrieval-Augmented Generation (RAG) framework are disclosed. The system selects a first Large Language Model (LLM) prompt corresponding to a first metadata identifier of the set of metadata identifiers. The system then augments the first LLM prompt with the first metadata identifier to be provided to an LLM, where the LLM is configured to generate a first intermediate output. The system then augments the first LLM prompt with the second set of metadata identifiers corresponding to the first metadata identifier to be provided to the LLM, where the LLM generates a second intermediate output. The system then generates a domain-specific unified metadata graph. Responsive to determining that first performance metrics fail to satisfy a performance criteria, the system performs an update process on the domain-specific unified metadata graph.

Generating a unified metadata graph via a retrieval-augmented generation (RAG) framework systems and methods

Systems and methods for performing data analytics on sensitive data, such as deduplication are disclosed herein. A first request to perform a first data analytics operation for a set of sensitive data instances may be received. A first set of image representations for the set of sensitive data instances may be retrieved. A first sensitive data instance and a second sensitive data instance may be clustered into a first cluster. The first data analytics operation may be performed on the first cluster.

Systems and methods for performing data analytics on sensitive data in remote network environments without exposing content of the sensitive data

Systems and methods for facilitating updates to data pipelines using modularly-generated platform-agnostic data pipeline portions are disclosed. The system receives a user selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes. The system then generates a modular-portion of the data pipeline architecture, via a transformation component, based on the user selection and stores the modular-portion of the data pipeline architecture in a remote database. The system then receives an update to at least one node of the set of nodes of the modular-portion of the data pipeline architecture. The system then updates at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes.

Facilitating updates to data pipelines using modularly-generated platform- agnostic data pipeline portions systems and methods

Systems and methods for generating a platform-agnostic data pipeline via a low code transformation layer are disclosed. The system receives one or more user selections of (i) nodes and (ii) links linking the nodes, indicating a data pipeline architecture of transfer/management/flow of data via a GUI. In response to receiving a user selection to implement the data pipeline, the system automatically identifies/generates a set of code portions, based on one or more software objects (e.g., JSON objects) associated with the user selections indicating the data pipeline architecture. The system then identifies a platform identifier associated with a remote server and generates a set of executable instructions (e.g., a script, executable program, or other file) associated with the data pipeline architecture by using a transformation component. The system then provides the executable instructions to the remote server to host the data pipeline.

Growing community of inventors

Brandon, FL, United States of America

Ashutosh Pandey