VoltGuard AI

• Automate the labor-intensive review of electrical plan sets against jurisdiction-specific codes
• Eliminate human error and inconsistent interpretation in compliance review
• Build a system that stays current as electrical codes are amended across different jurisdictions
• Provide inspectors with AI-generated annotations that cite specific code violations with line-level accuracy
• Support multi-state expansion without a linear increase in compliance review staffing

Electrical plan reviews are a bottleneck: one inspector manually cross-referencing hundreds of pages against thousands of jurisdiction-specific code amendments. Slow, inconsistent, and impossible to scale. Making it worse, electrical codes vary not just by state but by county and municipality, each on its own amendment schedule.

We built a pipeline with three specialized stages: document ingestion, code vectorization, and compliance reasoning. No single monolithic model. The RAG retrieval layer was tuned for legal and technical regulatory text, where precision matters far more than recall. Every flagged violation had to be traceable to an exact code reference.

The RAG pipeline uses LangChain and OpenAI to chunk and embed electrical code standards so the AI can cite specific regulations when it flags a violation. An interactive redlining system overlays annotations directly onto plan documents. A jurisdiction-aware database schema switches rule sets based on project address. Computer vision handles the PDF and CAD parsing before text-based reasoning kicks in.

The prototype proved the core loop works: upload a plan set, enter the project address, get annotated markup with code citations in minutes instead of days. It automated about 80% of standard code checks, with the remaining 20% flagged for human review. The jurisdiction database indexed over 1,200 code standards across multiple states.