Personalized cancer vaccines have long promised precise, patient-specific immunotherapy, yet real-world impact has been limited by slow design timelines, manufacturing complexity, and high costs. Current approaches typically require 8–10 weeks—often too late for patients with aggressive disease. In this talk, we present an AI-mediated, end-to-end platform that compresses the entire personalized cancer vaccine workflow—from tumor sequencing and neoantigen prioritization to mRNA design and manufacturing—into just 14 days. The platform integrates multi-omics data with AI-driven decision-making and a conserved synthetic template architecture to deliver speed, reproducibility, and regulatory readiness. We will share preclinical validation across multiple solid tumor models, discuss how speed itself becomes a biological advantage in immunotherapy, and outline the path toward IND and first-in-human studies. This session will highlight how AI can move beyond discovery to enable clinically executable precision oncology, redefining what is possible for personalized cancer vaccines and setting a new benchmark for translational cancer immunotherapy.