AIR Program

The AIR Program serves as the primary contracting lead for the Advanced Research Projects Agency for Health (ARPA-H), overseeing the acquisition of high-risk, high-reward biomedical and health technologies aligned with the agency’s mission to accelerate transformative innovations. Responsible for managing complex research and development procurements, they focus on agile, milestone-driven contract vehicles that support breakthroughs in agentic AI for cardiovascular care, protein aggregation modulation, immunological reprogramming, and autonomous medical systems. Their acquisitions are structured to foster rapid prototyping, iterative testing, and scalable deployment of next-generation health technologies, often utilizing Other Transaction Authorities and flexible contract mechanisms to expedite innovation. The AIR Program maintains a strategic partnership with TEMPUS AI, INC, a leading provider of AI-driven clinical analytics and precision health platforms. This relationship reflects a pattern of engaging cutting-edge technology firms capable of delivering scalable, data-intensive solutions for complex biomedical challenges. The contracting approach emphasizes performance-based outcomes, technical agility, and rapid iteration, favoring vendors with proven expertise in AI/ML systems, computational biology, and real-world health data integration. There is no set-aside preference; all procurements are open competition, reflecting ARPA-H’s priority on technical excellence and speed over socioeconomic categorization. The program does not prioritize 8(a), HUBZone, or SDVOSB set-asides, instead focusing on capability-driven selection to ensure the most innovative solutions are rapidly deployed to address urgent health threats. The AIR Program’s procurement activity is exclusively focused on NAICS 541715—Research and Development in the Physical, Engineering, and Life Sciences. They procure advanced computational tools, AI-enabled diagnostic platforms, automated medical systems, and biologically informed machine learning frameworks, demonstrating deep technical expertise in AI-driven clinical decision support, biomolecular modeling, and autonomous health technologies.