As products become increasingly software-defined, functional safety evidence is growing exponentially. Modern hardware programs now generate thousands of interconnected requirements spanning electronics, software, diagnostics, cybersecurity, and compliance –and legacy review methods cannot scale to this complexity.
Consequently, hardware design review for safety-critical systems has emerged as a critical bottleneck. Schematic packages run to hundreds of pages, requirement specifications run to thousands of clauses, and the manual cross-referencing required by ISO 26262, IEC 61508 and DO-254 consumes weeks of senior engineering time per release cycle. Reviewers routinely miss coverage gaps, conflicting requirements, and component-level failure modes that only emerge under stress.
SchematicAI is an on-premise, AI-augmented review platform that ingests schematic PDFs and requirement documents into a retrieval-augmented (RAG) corpus, then runs seventeen automated analysis modules — from Bill-of-Materials extraction and Traceability through DFMEA, FMEDA, FTA, Functional Safety (FuSa), SI/PI and Worst-Case Circuit Analysis. Each module produces structured JSON that drops into existing safety-case templates, with citations back to the source schematic page and requirement line.
The current paper is a summary exploration of the problem, the architecture, the module catalog, and the typical deployment pattern for SchematicAI.