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Rendered.ai and Quadridox Inc. Announce Partnership Agreement on Synthetic Data Common Application Framework

Rendered.ai Common Application Framework for Synthetic Data

Quadridox Q-Sim X-ray simulation

Rendered.ai and Quadridox Inc announce a partnership that will bring Quadridox's QSim X-ray simulation tool to Rendered.ai's Common Application Framework

The challenge for synthetic simulation tools is not just the physics realism of the synthetic data, It is also about how the synthetic data can easily assist the AI process.””
— Dr. Nathan Kundtz, Rendered.ai’s CEO,

BELLEVUE, WASHINGTON, USA, February 11, 2021 /EINPresswire.com/ -- Rendered.ai and Quadridox Inc. Announce a Collaboration and Partnership Agreement

Rendered.ai and Quadridox Inc. announced today a partnership that will bring Quadridox’s “QSim” X-ray simulation tool to Rendered.ai’s Common Application Framework for synthetic data generation.

Rendered.ai (www.rendered.ai)— the leading Common Application Framework supplier for physics based synthetic data—and Quadridox, Inc. (www.quadridox.com) - the leading provider of physics-based X-ray simulation and information tools for system design and analysis, announced today a game changing collaboration and partnership whereby the QSim X-ray simulation package will be hosted on the Rendered.ai Common Application Framework.

“The challenge for synthetic simulation tools is not just the physics realism of the synthetic data” said Dr. Nathan Kundtz, Rendered.ai’s CEO, “It is also about how the synthetic data can easily assist the AI process.” He continued, “Synthetic data assists AI by precisely creating data to be used in testing AI models, by broadly creating sets that can be used for training, and quickly iterating for object discovery, data labeling, cold start, and addressing other data deficit issues. This is why we built the Framework.”
“Quadridox’s QSim is the state of the art in X-ray simulation: it enables the design and analysis of new X-ray systems, algorithm development and optimization (including automated detection, feature selection, AI training), component evaluation and quality control, system of systems analysis, and system evaluation against designed, virtual test sets, and Rendered.ai is honored to host them on our Framework” said Dr Kundtz.

“The focus of Quadridox (www.quadridox.com) is to build the world’s most advanced and comprehensive X-ray simulation and synthetic data tool for markets as diverse as food safety, transportation security, medical, and automotive,” said Dr. Joel Greenberg, President and CEO of Quadridox, Inc, “This framework allows us to bring these tools to our customers anywhere in the world with sophisticated interfaces, compute management, and data pipelines,” he continued.

The Rendered.ai Common Application Framework includes a universal simulation description language, a cloud-native high performance compute platform, extensive and extensible interface features, and multiple shared libraries into which a Third-Party synthetic data generation tool, like QSim, can be hosted for synthetic data generation. “We believe data accessibility is one of the important gaps in AI and are thrilled to be building partnerships to address this gap across new industries,” continued Dr. Kundtz.

For more information regarding pricing and availability visit us at Rendered.ai, Quadridox.com or LinkedIn.

About Rendered.ai
Rendered.ai is a common application framework for synthetic data generation which uses physically accurate sensor modelling and a closed loop data engineering workflow. Founded by Physicist and former Kymeta CEO, Nathan Kundtz, Rendered.ai hosts a wide variety of simulated environments which include Visible wavelength, Infrared (SWIR, MWIR, and Thermal), and Synthetic Aperture Radar (SAR) imagery.

Rendered.ai is a privately held company based in Bellevue, Washington.

About Quadridox
Quadridox applies physics-based simulation and analysis to the design and evaluation of X-ray hardware and data processing algorithms. Our unique simulation tool, QSim, rapidly and accurately models a variety of X-ray interactions (including multi-energy photo-absorption and scatter) for arbitrary system configurations (including radiography, CT, and X-ray diffraction imaging). This, combined with our extensive experience in developing and building next-generation X-ray systems, enables us to generate sophisticated, relevant data at scale and across industries.


Media Inquiries or Business Inquiries for Rendered.AI:
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Head of Customer Innovation
www.Rendered.ai
Tom@Rendered.ai
+1 206.650.9068

Thomas Freeman
Rendered.ai
+1 206-650-9068
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