Stase
Stase builds embedded AI systems for environments where infrastructure fails and specialists don't reach. Our models run at the edge — on wearables, robots, and field vehicles — delivering real-time intelligence in health, environmental monitoring, and emergency response. We are a pre-incorporation AI and robotics company currently operating out of Ghana, with working prototypes across three product lines: VANCE — a cardiac AI model trained and deployed on embedded hardware, capable of real-time heart rhythm classification without connectivity or clinical infrastructure. Built to address the structural absence of cardiologists across African communities. ALUKA — a machine learning system for molecular and environmental signature prediction, originally developed for bacterial resistance profiling using k-mer feature engineering. Being expanded for deployment on ROVs and UAVs as a field-deployable environmental intelligence platform for pollution detection and biosurveillance. COURTNEY — a reconnaissance robot built for hazardous environments, with an embedded AI runtime that serves as the foundation for our broader field robotics platform. A medical intervention variant, deployable via drone to emergency scenes, is in active development. All three systems were built without GPU infrastructure as a deliberate reflection of our commitment to building technology that is resource-efficient, locally viable, and not dependent on the compute economies of the developed world. Going forward, we are pursuing seed funding to harden these products for commercial deployment, engage our first government and health system clients, and develop the mechanical capability required for next-generation COURTNEY variants. We are also exploring integration partnerships with established bioinformatics and environmental monitoring platforms seeking field-deployable intelligence modules.
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- 1cto - stan’s technologies | computational scientist | final year computer engineering candidate | embedded systems, mathematical modeling & applied algorithms
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