Automated code transpilation using Large Language Models (LLMs) offers an efficient, less error-prone alternative to tedious manual code rewriting for addressing memory safety and runtime inefficiencies. Our research presents a generic framework for automated, validated transpilation using LLMs, demonstrating its feasibility for industry adoption. We validate our approach through two applications: CToRust, which transpiles C code to Rust, and PandaX, which optimizes Jupyter Notebooks for GPU and CPU backends.