CADY Solutions uses AI-driven schematic analysis to identify design risks early, reduce costly PCB re-spins, and accelerate time-to-production. In this session, Gilad Shapira shares how intelligent automation is transforming electrical engineering workflows and improving development efficiency.
Gilad Shapira, co-founder and CEO of CADY Solutions, presents how AI-driven schematic analysis can help hardware engineers identify design errors earlier, reduce PCB re-spins, and accelerate time to market. CADY acts as an automated design-review layer between schematic capture and PCB layout, providing engineers with a “copilot” similar to the debugging and assistance tools available to software developers.
The platform combines information from millions of component datasheets with electrical engineering knowledge and user feedback. It can identify incorrect pin connections, communication protocol errors, missing protections or pull-ups, voltage problems, capacitor derating issues, and company-specific design-rule violations. It also supports multi-board systems and a wide range of ECAD environments.
Shapira shared that CADY is designed to assist engineers, not replace them. Human judgment remains essential because many design decisions depend on engineering intent that cannot be determined from a schematic and BOM alone. Instead, CADY provides another layer of defense, catching common mistakes before they become expensive manufacturing or field problems. Contact them for special PATCA consulting pricing.
<p”>Future plans include expanded BOM analysis, component recommendations, layout analysis, and eventually live design assistance—bringing more software-like automation to complex hardware development.
About the Speaker: Gilad Shapira, CoFounder and CEO of CADY Solutions
He holds an M.Sc. in Statistics and Data Science from Tel Aviv University and has spent the past several years applying artificial intelligence and machine learning to complex real-world challenges across research and industry.
Prior to founding CADY, Gilad worked on statistical emulation of neural simulators for advanced biological systems, including neocortical basket cells – research that strengthened his expertise in modeling, precision, and intelligent automation.