This paper proposed a novel methodology for optimizing carbon emissions and achieving decarbonization goals. It leverages the data analysis capabilities of the JARVIX platform in conjunction with a large language model (LLM), specifically ChatGPT. The study utilizes a carbon inventory dataset from a well-established Taiwanese factory with extensive manufacturing experience, providing a robust foundation for carbon footprint analysis. Our approach consists of two key phases. First, we employ ChatGPT to analyze the company's carbon emissions and existing policies, generating potential carbon reduction strategies. Second, the JARVIX platform is used to calculate, analyze, and monitor carbon emissions in real-time. This creates a complementary workflow. Initial strategy formulation with ChatGPT is followed by emission calculation, analysis, and tracking with JARVIX. These real-time data are then compared to historical emissions to assess the effectiveness of the strategies. If decarbonization targets are not met, JARVIX's root cause analysis function can pinpoint any underlying issues. Subsequently, the updated carbon inventory data from JARVIX is fed back into ChatGPT, allowing for continuous refinement of the strategies. This iterative process ensures the strategies remain aligned with the company's specific context, maximizing the likelihood of achieving decarbonization goals.