Structural information is critical for AI-driven biologics design, yet current methods either impose rigid constraints (X-ray, cryo-EM) or lack sufficient resolution (HDX, FPOP) to meaningfully inform predictive models. Immuto Scientific addresses this gap with high-throughput, residue-level mapping that captures subtle structural changes driving function. These data provide the empirical constraints needed to improve RMSD, reduce false positives, and sharpen affinity maturation and engineering workflows. In this presentation, we will share practical demonstrations of how our structural insights integrate with AI pipelines to accelerate discovery cycles and reduce costly wet-lab iterations.