The pharmaceutical industry is currently facing the challenge of optimizing and streamlining the drug discovery process. Despite the increasing use of artificial intelligence (AI) in drug target identification, compound exploration, and optimization, most non-clinical animal experiments still rely on human manual assessments. In each company and laboratory, animal experiments involve varied systems and observation criteria relying on visual assessments, resulting in discrepancies in test outcomes and imprecise evaluations. To address these challenges, we propose standardizing experimental systems and implementing AI-driven video analysis technology. Leveraging collaborative research with The University of Tokyo, Revamp Corporation. is now developing these AI-based animal behavior analysis systems, including environmental designs. Our company aims to optimize and improve the efficiency of drug development by building animal behavior analysis  systems with high accuracy, reproducibility, and versatility. In this presentation, I would like to introduce some of those attempts with specific examples.