Netlist.io is an LLM-powered assistant built specifically to catch PCB schematic mistakes before they hit your board house. Instead of relying only on manual reviews, rule decks, and late-stage lab debugging, you can upload your schematic and let an AI trained on electronics patterns highlight potential issues in minutes. The tool analyzes connectivity, reference designators, power and ground networks, component usage, and common design conventions to surface suspicious wiring, missing connections, and unusual configurations. It doesn’t just flag rule violations; it explains why something might be risky, so engineers can quickly judge whether it’s an intentional design choice or a real defect. Netlist.io is ideal for hardware startups, solo engineers, and established teams who want an extra, always-available reviewer. It reduces costly board spins, shortens design cycles, and gives you more confidence before committing to fabrication. Because it’s powered by large language models, the system can understand context, handle a wide range of design styles, and continuously improve as more patterns are learned. Use Netlist.io as a last-mile check before tape-out, an automated peer reviewer during design iterations, or a teaching aid for junior engineers learning best practices. By combining AI reasoning with practical EDA workflows, it helps you find subtle schematic mistakes early—when they’re still cheap to fix.
Pre-fabrication schematic review: Run an automated AI check on your final schematic before sending Gerbers or netlists to the PCB manufacturer.
Design iteration sanity checks: Use the tool after each major schematic change to quickly spot regressions or newly introduced mistakes.
Team design reviews: Complement human peer reviews with an AI pass to catch subtle connectivity or naming issues that are easy to overlook.
Onboarding junior engineers: Let less-experienced designers get instant feedback on their schematics and learn common pitfalls in context.
Legacy design validation: Re-scan older or inherited projects to identify hidden risks before reuse or derivative designs.