Palantir Technologies Inc.

Matthew Levan

Nicholas Hall

Nathaniel Klein

Jason Free

Thomas Montague

James Schweiger

Hamza Muraj

John Montgomery

Kevin Whelan

Takashi Okamoto

Huanqi Deng

Arseny Bogomolov

Jack Grossman

Daniel Erenrich

Justin Streufert

Cooper Bills

Ryan Beiermeister

Eric Anderson

Brandon Marc-Aurele

Robert Giardina

Eric Raboin

Christopher Luck

Matthew Gerhardt

Kevin Simons

Timothy O'Brien

James Rosswog

Tim O'Brien

Example embodiments relate to a network-based workflow system, employed for receiving workflows, defining one or more data-object types based on the workflows, generating data-objects, assigning a workflow from among the one or more workflows to the data-object, and managing the data-object through various states of based on the workflow. As discussed, a 'workflow' refers to orchestrated and repeatable patterns enabled by a systematic organization of resources into processes that transform and modify presentations of data-objects based on corresponding data-object states. A workflow may therefore comprise a set of states, wherein each state is linked to another state by one or more transitions, and wherein the transitions are associated with a set of events which may occur at each state.

System to manage document workflows

Computer-implemented systems and methods are disclosed that query collections of documents. Disclosed embodiments may include receiving, via a user interface, a first search query comprising a text string. Disclosed embodiments may include, responsive to receiving the first search query, initializing a first query object based on the text string. Further, disclosed embodiments may include translating the first query object to match the formatting of a search engine, the translated first query object capable of searching a plurality of collections of documents. Disclosed embodiments may also include receiving aggregated query results from a search engine based on the translated first query object. Some disclosed embodiments may include reformatting the aggregated query results based on respective configuration files of the collections. Additionally, disclosed embodiments may include rendering a graphical user interface that facilitates user interaction with the reformatted aggregate query results.

Systems and methods for database investigation tool

Systems and methods are provided for providing continuous configuration deployment. A configuration definition object may be obtained from a remote datastore. The obtained configuration definition object may be stored in a local datastore. The configuration definition object may be obtained and stored in advance of a scheduled maintenance. The configuration definition object may be obtained in response to the configuration definition object being committed to the remote datastore. The configuration definition object may be used to generate different configuration objects. A configuration object may be generated from the configuration definition object and one or more system characteristics. A service request may be received, and the configuration object may be executed to perform a configuration of a computing system.

Systems and methods for continuous configuration deployment

Computer implemented systems and methods are disclosed for identifying and categorizing electronic documents through machine learning. In accordance with some embodiments, a seed set of categorized electronic documents may be used to train a document categorizer based on a machine learning algorithm. The trained document categorizer may categorize electronic documents in a large corpus of electronic documents. Performance metrics associated with performance of the trained document categorizer may be tracked, and additional seed sets of categorized electronic documents may be used to improve the performance of document categorizer by retraining the document categorizer on subsequent seed sets. Additional seed sets may and categorizations may be iterated through until a desired document categorization performance is reached.

Systems and methods for identifying and categorizing electronic documents through machine learning

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Arlington, VA, United States of America

Matthew Levan