Closed-loop neuromodulation provides a powerful paradigm for the treatment of diseases, restoring function, and understanding the causal links between neural and behavioral processes, however, the complexities of interacting with the nervous system create several challenges for designing optimal closed-loop neuromodulation control systems and translating them into clinical settings. Artificial Intelligence (AI) and Reinforcement Learning (RL) can be leveraged to design intelligent closed-loop neuromodulation (iCLON) systems that can autonomously learn and adapt neuromodulation control policies in clinical settings and bridge the translational gap between pre-clinical design and clinical deployment of neuromodulation therapies. We are developing an open-source AI platform, called Neuroweaver, to enable algorithm-software-hardware co-design and deployment of translatable iCLON systems. In this paper, we present the design elements of the Neuroweaver platform that are translatability of iCLON systems.