On June 16, PiLogic, the company building explainable artificial intelligence for complex space systems, announced that it has entered into a Cooperative Research and Development Agreement (CRADA) with the U.S. Air Force Research Laboratory (AFRL) to apply its AI technology to diagnosing and predicting electrical and power failures on spacecraft platforms.
The collaboration will focus on adapting PiLogic’s diagnostic models to analyze spacecraft electrical subsystems with significantly higher accuracy than traditional rule-based or machine learning approaches. PiLogic will integrate its “Exact AI” inference engine to detect anomalies, predict failure modes, and recommend corrective actions in spacecraft electrical subsystems. The goal is to reduce costly redesign cycles, improve satellite reliability, and strengthen space mission assurance.
“This collaboration accelerates our ability to make satellite systems smarter, safer, and more predictable,” said Johannes Waldstein, CEO of PiLogic. “Space missions demand both reliability and transparency. Our technology gives engineers a clear, explainable understanding of system behavior, essential not just for defense, but for the rapidly expanding commercial satellite economy.”
AFRL will provide access to its satellite testing platform, enabling PiLogic to validate and refine its models under operationally relevant conditions. The collaboration will deliver AI-driven diagnostic tools that enhance how satellites are designed, tested, and maintained, while helping AFRL improve real space missions.
“We at the Air Force Research Laboratory, Small Satellite Portfolio, are excited to evaluate the next generation of autonomy for satellite health monitoring with true causal understanding,” said Joseph Melville, PhD and satellite autonomy lead at the U.S. Air Force Research Laboratory. “Through agreements like this, we hope to build first-of-their-kind, highly autonomous satellite systems that are natively transparent and trustworthy to human operators.”
Unlike traditional AI models that often operate as opaque “black boxes,” PiLogic’s system uses mathematically rigorous, explainable reasoning. Engineers can see not only what might fail, but also why — and with measurable confidence levels. That transparency is critical in satellite systems, where failures can mean total mission loss and years of wasted investment.
Source: PiLogic
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