C3.ai, Inc.

Romain Florian Juban

Thomas M Siebel

Nikhil Krishnan

Louis Poirier

Michael Haines

Adrian Conrad Rami

Anton Rubisov

Riyad Sabir Muradov

Chih-Hsu Lin

Franklin Q Ye

Yushi Homma

Bryan C Anderson

A method includes obtaining information associated with multiple collocation events. Each collocation event is associated with an occurrence where multiple entities were collocated with one another. The information associated with each collocation event identifies the entities associated with the collocation event, a duration of the collocation event, one or more locations associated with the collocation event, and a time associated with the collocation event. The method also includes identifying strengths of different relationships involving different ones of the entities based on the information. The strengths are determined using one or more features of the collocation events.

Entity relation strength identification using spatiotemporal data

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view of profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements, and provide traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to access information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

Generative artificial intelligence enterprise search

Systems and methods managing, by an orchestrator, a plurality of agents to generate a response to an input. The orchestrator employs one or more multimodal models such as a large language models to process or deconstruct the prompt into a series of instructions for different agents. Each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt. The system generates, by the orchestrator, a natural language summary of the structured and unstructured data records. The system formulates output and transmits the natural language summary of the data records.

Enterprise generative artificial intelligence architecture

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

Artificial intelligence transaction risk scoring and anomaly detection

Systems and methods for anti-money laundering analysis

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San Francisco, CA, United States of America

Romain Florian Juban