Bridging the AI Deployment Gap with MOSA.ic.AI: Your trained model, deployed for Deterministic Inference in constrained, safety-critical environments

AI innovation is accelerating rapidly, but deploying trained models into mission-critical embedded systems remains a significant challenge. While today's AI frameworks excel at model development, they were not designed for the deterministic inference, workload isolation, and certification requirements of aerospace, defense, and other safety-critical applications. LYNX MOSA.ic.AI bridges this AI deployment gap by optimizing AI inference for Size, Weight, and Power (SWaP)-constrained embedded systems, enabling validated models to execute predictably alongside real-time and safety-critical workloads.

In this webinar, you'll learn how to bring your own trained AI model into MOSA.ic.AI without changing your existing AI development workflow. We'll demonstrate how the platform separates model development from deployment while providing deterministic inference, unified CPU/GPU execution, mixed-criticality support, hardware portability, and open systems architecture to simplify integration and enable reliable AI deployment at the edge.

Attendees will learn how to:

• Bring existing AI models into MOSA.ic.AI without modifying established development workflows.
• Optimize AI inference for SWaP-constrained embedded systems while maintaining predictable, deterministic behavior.
• Deploy AI alongside real-time and safety-critical applications using a unified CPU/GPU architecture.
• Reduce integration risk with an open, hardware-portable platform designed for long-term mission evolution.

This session is part of the The LYNX MOSA.ic.AI Webinar Series. Registration/event page here.