Falkonry awarded Phase 1 AFRL SBIR contract
Falkonry, Inc., based in Sunnyvale, CA, announced on April 9 that it has been awarded an AFWERX Phase I Small Business Innovation Research (SBIR) contract from the US Air Force Research Laboratory (AFRL). Receiving this contract qualifies Falkonry’s “pre-packaged” machine learning system, Falkonry LRS, to be used by any government agency that wants to deploy predictive analytics without requiring data scientists. Key selection criteria for this contract include technical merit, ability to solve key operational challenges and proven commercial viability and success outside of the government. In the commercial space, Falkonry LRS has saved companies millions of dollars and delivered 5-10 times return on investment (ROI) through improvements in the uptime, yield, quality and safety of their operations, the company said.
“This contract validates Falkonry’s proven success in the commercial market, and highlights the readiness of our technology for government agencies who want to deploy predictive analytics in the cloud, on-premises or at the edge,” said Dr. Nikunj Mehta, founder and CEO of Falkonry. “Falkonry LRS is also highly secure and scalable, which means agencies can deploy from one machine or process to multiple lines and sites, providing the best results and the fastest return on their investment.”
To ensure the highest level of security for government requirements, Falkonry is providing an “air-gap” version of its Falkonry LRS system. An air-gap is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. It means a computer or network that is electrically disconnected (with a conceptual air gap) from all other networks.
Falkonry LRS enables operations teams to discover, explain and predict behaviors that matter, without requiring data scientists. The automated feature learning solves the most complex problem of applying machine learning on time series data saving time and building accurate predictive models. The system has been successfully deployed in a range of industries such as semiconductor, automotive, oil & gas, mining & metals, and energy.