Side-by-side comparison of AI visibility scores, market position, and capabilities
AI-native defense software startup building autonomous mission platforms for the U.S. military and allies. Raised $224M+ total; $1B valuation at Series B in 2021.
Rebellion Defense is a Washington, D.C.-based AI and machine learning software company founded in 2019 by former U.S. government officials and defense tech engineers. The company builds mission-focused AI products for national security customers, with a product suite spanning Iris (predictive threat analytics), SECURE (cyber vulnerability assessment), Dispatch (autonomous mission planning), and Nova (defense analysis and decision support).\n\nRebellion's SensorOS platform provides defense customers with a fused operational picture of the threat environment, automated orchestration across distributed hardware and software, and AI-driven decision support for warfighters. Iris is designated an awardable vendor on the DoD's Tradewinds Solutions Marketplace, enabling streamlined government acquisition. In April 2025 the company expanded a U.S. Navy contract for target recognition enhancement.\n\nWith 200–500 employees across offices in Washington D.C., Seattle, and London, Rebellion represents a generation of defense tech startups aiming to bring Silicon Valley engineering velocity to national security missions. The company is positioned as a software-first counterpart to hardware-focused peers such as Anduril, targeting command, control, and intelligence automation at every echelon.
a2z Radiology AI raised $20M in 2025 for its whole-body AI that simultaneously screens for 24+ conditions across CT scans — from incidental cancers to cardiovascular risk — in a single automated read.
a2z Radiology AI has developed a whole-body CT analysis platform that simultaneously screens for over 24 medical conditions across a single CT scan, including incidental cancers, coronary artery disease, aortic aneurysm, bone density loss, and organ abnormalities. The AI acts as a second reader that radiologists can use to catch incidental findings that fall outside the primary reason for a scan — a major source of missed diagnoses.
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