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Q-Net Security designs and sells provably-secure hardware cybersecurity solutions to protect defense systems and critical infrastructure, such as utilities and communications networks. While most other cybersecurity solutions involve complex software that detects and responds to threats after an attack is underway, Q-Net`s hardware defends an entire system`s surface. By creating a physical barrier against would-be hackers, Q-Net offers a powerful defense that is superior to software detection models. Based in St. Louis, Missouri, Q-Net was founded in 2015 by a team of highly acclaimed technologists, engineers, and security experts.
Supremesoft Corporation is a Vienna, VA-based company in the Computers and Electronics sector.
Cobalt Iron delivers the industry`s first enterprise-class cloud backup SaaS offering. Cobalt Iron`s platform, Compass, scales from terabytes to exabytes and provides the simplicity not found in backup technologies and tools today. Cobalt Iron Compass is: • Delivered as a service and controlled via a web interface • A single, flexible deployment model scaling the range of private to hybrid to public • Leverages cloud investments in Amazon AWS, IBM SoftLayer, Microsoft Azure, and Google Cloud • Delivers industry leading features, functions, and platform/application support • Requires zero backup application expertise Cobalt Iron delivers innovative data protection solutions, enabling customers to transform their complex, backup-centric IT infrastructure into a recovery-centric, simple, scalable, flexible service. Cobalt Iron eliminates the burden of constantly monitoring, maintaining and upgrading in-house data protection environments, while allowing customers to retain full operational control. Cobalt Iron Compass is redefining enterprise data protection from a box of tools and technologies into a simple, flexible service.
Campus Mobility is a Ann Arbor, MI-based company in the Computers and Electronics sector.
The Synthetic Data Engine by Mostly AI allows to simulate realistic & representative synthetic data at scale, by automatically learning patterns, structure and variation from existing data. It leverages state-of-the-art generative deep neural networks with in-built privacy mechanism to retain the valuable information while rendering the re-identification of any individual impossible. This way it provides as-good-as-real, yet fully anonymous data, that can be freely processed, analyzed and shared further.