SBIR-STTR Award

The Air Force (AF) has yet to leverage broad and operator-based machine learning (ML) into their maintenance, mission, or operational environments. Falkonrys commercially demonstrated Data Scientist in a Box, is a pre-packaged operational ML solution that empowers teams with pattern discovery, predictive analytics, and explanation to improve uptime, performance, quality and safety.machine learning,artificial intelligence,signal processing,predictive maintenance,ELINT

Falkonry LRS is your "Data Scientist in a Box,"� pre-packaged operational machine learning (ML) empowers your teams with pattern discovery, predictive analytics and explanation to improve uptime, performance, quality and safety. Falkonry believes its AI-enabled LRS could add significant value to the processing of electronic intelligence (ELINT) data, at scale and for significantly lower cost. We propose to pilot this in both unclassified and classified Air Force and joint DoD units, with the Joint Warfare Analysis Center in STRATCOM as our lead customer. The target user is the operations/intelligence analyst. The analyst has access to large amounts of operational and other electronic signal data coming from sensors and other forms of instrumentation. The technical challenge is to exploit that data in order to get early warning of adverse events, detect anomalies, and provide some level of confidence and explanation of why the analyst reached a particular judgment. By combining the analyst's subject-matter expertise with Falkonry's automated ability to "see"� patterns in complex signal data, the analyst can create, test and validate predictive models that perform more accurate prediction and provide transparency into how confident the system is and explanation of which signals are most highly correlated with a particular judgment.