The deployment of AI at the edge is often driven by performance constraints, with safety  considerations addressed only at later stages. Aidge challenges this paradigm by providing an open-source framework in which confidence, and traceability are first-class design objectives alongside performance optimization. 
It offers a transparent and auditable toolchain for deploying inference on edge platforms, relying on explicit intermediate representations and controlled graph transformations to ensure traceability from training models to implementation artifacts. Through the integration of the ACETONE approach, enabling traceability and worst-case execution time analysis, Aidge is well suited to aeronautical certification standards such as DO-178C and the forthcoming ML-specific standard ARP6983. In addition, Aidge supports inference testing under hardware fault conditions and pioneers the adoption of the Safety ONNX standard, contributing to robustness assessment, standardized model exchange, and strengthened assurance arguments for safety-critical AI systems.