The Federal Aviation Administration (FAA) is seeking a scalable, data-driven system to provide near-real-time visibility into student performance across all phases of Academy training. The system should be able to integrate disparate training data sources into a unified analytics environment and support predictive modeling, performance standardization, and strategic decision-making. The FAA is looking for concept papers that identify real-world solutions and provide an implementation plan that addresses areas such as comprehensive data integration, objective-level mastery mapping, near-real-time analytics dashboards, error taxonomy and trend analytics, predictive analytics, instructor performance analysis, enterprise leadership reporting, technical and security requirements, scalability, and future expansion. Some key features of the desired system include: • Comprehensive data integration from various sources • Objective-level mastery mapping and tracking of mastery progression • Near-real-time analytics dashboards with drill-down capability • Error taxonomy and trend analytics • Predictive analytics to identify early indicators of student attrition risk and performance degradation • Instructor performance analysis and support for instructor calibration efforts • Enterprise leadership reporting with visibility into throughput metrics, attrition and failure rates, time-to-proficiency, and block-level performance risk • Technical and security requirements that adhere to FAA requirements for network connectivity and data ownership structure The FAA prefers that responses be limited to 25 pages and include a description of the solution, a plan for implementation, relevant experience, a high-level cost estimate, a proposed project schedule, and contact information.