Shalini Ananda, an AI researcher from San Francisco, transformed her backend engineering expertise into a significant real-world impact by creating Fire Fairness, an "insurance equity engine" designed to assist non-profit legal aid groups in processing wildfire insurance claims more efficiently. Despite having no frontend experience, Ananda developed this tool to address the bias in insurance claim denials, particularly highlighted during the 2025 Los Angeles fires. Fire Fairness utilizes AI to process claims 360 times faster than traditional methods, incorporating data from CAL FIRE, satellite imagery, and bias detection to ensure equitable outcomes. The tool has already processed 2,847 claims, reducing costs by 99.8% and providing legal aid groups with a powerful, user-friendly application for combating insurance bias. Built through Lovable, a platform that helped translate complex backend capabilities into accessible solutions, Fire Fairness exemplifies how AI can be leveraged for advocacy, offering a robust model for using technology to address social inequalities. Ananda plans to maintain the tool's security and scalability to continue supporting communities affected by wildfires, emphasizing the importance of building solutions that are both impactful and accessible to non-technical users.