Spinal cord injury affects approximately 40 million people every year and 30-70% of upper limb assistive devices get rejected by the user within the first year due to complexity and lack of adaptability. We designed an AI feedback pipeline that takes patient profiles, including sizing and movement limitations to create the optimal personalized hand grasper design. Our solution incorporates simulated tasks in a MuJoCo environment and a three-phase LLM-guided optimization pipeline. Our goal is to accelerate the development of personalized and effective assistive technologies.