The work on Lipschitz constant estimation for neural networks can serve as a foundational component in the development of end-to-end frameworks for verifiable and robust neural network design in safety-critical applications. By integrating the Lipschitz constant estimation algorithm into a broader framework, we can establish a systematic approach to ensure the safety and reliability of neural network controllers in real-world systems. This framework could include modules for training neural networks with Lipschitz constraints, verifying the robustness of the trained models, and deploying them in safety-critical applications. By incorporating techniques for Lipschitz constant estimation alongside other verification and validation methods, such as formal verification and testing, we can create a comprehensive framework for certifying the safety and reliability of neural networks in critical domains like autonomous vehicles, robotics, and control systems.