PiLogic partners with Air Force lab to test satellite fault-prediction software
PiLogic, a startup developing artificial intelligence software to identify faults and predict failures in satellites, has signed an agreement with the U.S. Air Force Research Laboratory to test whether its technology can improve the way spacecraft operators detect and diagnose problems in orbit.
The Los Angeles-based company said it will work with AFRL under a two-year Cooperative Research and Development Agreement, or CRADA, focused on spacecraft electrical and power systems. Engineers will use an AFRL cubeSat experiment launched in 2022 through the Defense Department's Space Test Program as a platform to evaluate PiLogic's software.
The company's software is designed to analyze onboard sensor data, detect anomalies, predict potential failure modes and recommend corrective actions. PiLogic argues that its approach, based on probabilistic reasoning and automated causal analysis, could be used as an alternative to traditional monitoring systems that rely on predetermined rules and thresholds.
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Satellite operators typically use rules-based systems that trigger alerts when specific parameters move outside prescribed limits, such as battery voltage dropping below a threshold or temperatures exceeding allowable ranges. PiLogic contends that such systems can struggle when multiple symptoms occur simultaneously or when the source of a problem is ambiguous.
Instead of evaluating individual conditions in isolation, the company's software attempts to determine the most likely underlying cause by combining engineering models, physics-based relationships and probability theory.
Johannes Waldstein, PiLogic's co-founder and chief executive, said the technology is intended to provide engineers with an explainable understanding of spacecraft behavior rather than simply generating alerts.
AFRL plans to use the project to explore new approaches to spacecraft autonomy and health monitoring.
"We are excited to evaluate the next generation of autonomy for satellite health monitoring with true causal understanding," said Joseph Melville, satellite autonomy lead at AFRL.
The agreement comes as defense agencies and aerospace companies are examining how artificial intelligence can be applied to military space operations. Much of the investment in AI has focused on large language models and machine-learning systems trained on vast datasets. PiLogic is pursuing a different approach.
The company describes its technology as "exact AI," emphasizing probabilistic reasoning rather than generative AI. Waldstein said many aerospace and defense applications lack the large datasets required to train conventional machine-learning models and demand greater predictability than consumer AI systems.
"Large language models are very powerful, and machine learning is very powerful, where you have a big body of data, and where you can afford to be wrong," Waldstein said. In aerospace and defense, he added, operators need assurance that AI systems will not hallucinate, behave unpredictably or make decisions without a clear basis.
PiLogic says its software can identify component failures and forecast emerging issues in spacecraft power systems while requiring less computing power than many machine-learning approaches.
The technical foundation of the company was developed by co-founder Mark Chavira, a former Raytheon and Google engineer who earned a doctorate focused on probabilistic artificial intelligence.
Waldstein said the Space Force's Space Systems Command supported the CRADA because military organizations are seeking new ways to identify potential problems in aging satellite fleets before they become mission-threatening failures. He said the agreement includes government personnel support over two years and sponsorship for a security clearance that would allow PiLogic to pursue classified work.
The company raised a $4 million seed financing round in 2025 and is pursuing opportunities with NASA and the U.S. Space Force. Waldstein said PiLogic has signed multiple customers but declined to identify them.
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