Orbem, founded in 2019 and headquartered in Munich with a presence in Houston, builds AI-powered imaging systems that compress the physics of magnetic resonance imaging into something fast and industrial-grade. The core technical stack is a fusion of rapid MRI acquisition and deep learning inference - think adversarial networks and reconstruction models trained to pull signal from minimal scan data. The company's Genus product family applies this platform across non-obvious domains: sexing poultry eggs non-invasively, grading fruit and nuts by internal quality, and extending into human health imaging. The threat model here isn't traditional cybersecurity, but data integrity and adversarial robustness in safety-critical inference pipelines - misclassified eggs, misgraded produce, misread scans carry real economic and biological consequences.
The team exceeds 200 people, spanning MRI physics, ML engineering, embedded systems, and production deployment. The engineering challenge sits at the intersection of sensor hardware control, real-time data acquisition, and model serving at scale - deep learning models running against live imaging data in food production environments means latency, reliability, and input validation aren't theoretical concerns. Orbem positions its work as aligned with both business efficiency and sustainability, processing biological insights at volumes traditional imaging never approached.
