We develop autonomous, interpretable AI systems for space missions — from onboard inference on resource-constrained CubeSats to formal verification of mission-critical decision-making. Pre-deployment validation, not post-incident response.
Our methodology centers on interpretable AI validated before launch. We engineer systems that explain their decision pathways — not after something goes wrong, but before it ever could.
From ML model architecture through deployment pipelines — covering onboard inference, satellite health monitoring, and Earth observation analytics across hardware constraints and radiation-hardened computing.
Formal verification for AI in life-critical systems. Mars rover decision-making, satellite collision avoidance — mathematical proofs of behavior bounds, not just security hardening.
XAI in space isn't academic — when a satellite autonomously changes orbit, operators need to understand why. We're building the language for human-AI communication in space operations.
Our QML division explores quantum algorithms for remote sensing and optimization in orbital mechanics — at the intersection of theoretical computer science and practical space engineering.
Real hardware, real launches, real data. Debugging edge computing on 10cm³ satellites, optimizing power budgets for neural networks, handling intermittent ground station contacts.
Reliability. Explainability. Accountability. We are not building AI that impresses — we are building AI that you can trust when the margin for error is zero.
We don't build AI to replace humans in space exploration. We build AI to help them operate in the most vulnerable environment humanity has ever attempted — where there is no margin, no fallback, and no second chance.
Spacecraft Autonomous Telemetry Intelligence and Safety Handling — a modular AI safety system for CubeSats that detects failures, flags anomalies, recommends corrective actions, and explains every decision.