Clari Inc.

Balasubramaniam Raju

Techniques for identifying similar companies based on profile data sets of the companies are provided. In one embodiment, a method comprises using a processing device to obtain a benchmark profile data set for a benchmark company and obtain a plurality of profile data sets, each of the plurality of profile data sets corresponding to a candidate company. The processing device may utilize a machine learning algorithm to determine the distance between each of the plurality of profile data sets and the benchmark profile data set and build profile graphs indicating the distance. The processing device may determine one or more of the plurality of profile data sets that are most similar to the benchmark profile data set based on the determined distance and identify the one or more candidate companies corresponding to the one or more profile data sets as companies most similar to the benchmark company.

Method, non-transitory machine-readable medium, and system for identifying comparable entities using machine learning and profile graphs

Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. The task database is in synchronization with a customer relationship management (CRM) system. The systems and methods described herein enable users to update the task database and enter information into the task database in a timely manner such that the task database can stay updated. The updated database can be used to construct a suggested task set at the beginning of a period of time to meet a preset target sales value for the end of the period of time. In one embodiment, a system includes a mobile application to capture contact information from a physical medium as an image, and to send the image to a cloud server, where a trained neural network model is to extract contact details and send the contact details back to the mobile application for editing and confirmation by a user. The confirmed contact details can then be persisted into the task database as a new task, part of a new task, or part of an existing task.

Method for capturing and storing contact information from a physical medium using machine learning

Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. In one embodiment, a method includes the operations of receiving, by one or more neutral network models on a cloud server, voice instructions captured by an interactive voice response (IVR) application on a mobile device, wherein the voice instructions are to update states of one or more tasks displayed in a voice interface of the IVR application; recognizing the voice instructions and constructing appropriate texts using the one or more trained neutral network models; presenting the texts to one or more voice interfaces provided by the IVR application for confirmation by a user; and storing the user confirmed texts to a repository on the cloud server.

Method for capturing and updating database entries of CRM system based on voice commands

Described herein are systems and methods for facilitating the information entry and task updates to a task database in a cloud server. The task database is in synchronization with a customer relationship management (CRM) system. The systems and methods described herein enable users to update the task database and enter information into the task database in a timely manner such that the task database can stay updated. The updated database can be used to construct a suggested task set at the beginning of a period of time to meet a preset target sales value for the end of the period of time. In one embodiment, a method includes the operations of receiving, by one or more neutral network models on a cloud server, voice instructions captured by an interactive voice response (IVR) application on a mobile device, wherein the voice instructions are to update states of one or more tasks displayed in a voice interface of the IVR application; recognizing the voice instructions and constructing appropriate texts using the one or more trained neutral network models; presenting the texts to one or more voice interfaces provided by the IVR application for confirmation by a user; and storing the user confirmed texts to a repository on the cloud server.

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Sunnyvale, CA, United States of America

Balasubramaniam Raju