The primary problem we identified is that the vast majority of surgical instruments lack machine-readable labels, creating a persistent blind spot in hospital tracking. This forces Central sterile services department staff to rely on manual, time-consuming, and error-prone reconciliation to ensure instrument sets are complete. This operational inefficiency leads to these major pain points: relying on manual and visual only identification of tools, missing or extra instruments in sets, persistent workflow bottlenecks, and weak accountability across handoffs, all of which disrupt the high-throughput reality of modern hospital sterilization departments. For this experienced nurses are 24/7 required for sorting 50,000+ instruments daily. Our solution leverages each instrument’s intrinsic physical "fingerprint" to provide fast, automated tool identification. By combining scanner hardware, AI classification, and workflow software, the system identifies tools instantly to automate set completeness verification and discrepancy resolution directly at the CSSD packing station. Therefore automation of this process reduces errors and decrease costs.