AI Accuracy & Validation: What to Expect from AI Plan Checking
"How accurate is the AI?" is the most common question we get. Here's an honest answer: approximately 70-80% of flagged issues are actionable findings that require attention. That might sound low—but context matters. Here's why this is actually excellent performance.
Current Accuracy Metrics
Issues requiring attention or resolution
Flagged items that aren't actual issues
Where we're heading (within weeks)
These numbers are based on customer feedback and internal validation. Accuracy varies by project type, document quality, and the specific disciplines being checked.
Why 80% Accuracy Is Highly Valuable
At first glance, 80% accuracy might seem low. But consider the alternative:
The Math That Matters
Without AI, how many of those 80 issues would your team have caught manually? In our experience, even expert reviewers miss significant issues when reviewing hundreds of pages across multiple documents.
The False Positive Tradeoff
We could reduce false positives by being more conservative—only flagging issues we're 99% certain about. But that would mean missing real issues. We err on the side of flagging more, knowing you can quickly dismiss false positives. Missing a real issue costs far more than reviewing a false positive.
You Don't Have to Trust Blindly
Every issue AI flags comes with supporting evidence. You can verify each finding yourself:
See the Evidence
Each issue shows exactly where in the documents the conflict was found—the drawing page, the spec section, the code paragraph.
One-Click Verification
Click through to the source documents. See the exact detail, the exact spec requirement, the exact code citation.
Quick Dismiss
If an issue is a false positive, dismiss it in one click. The system learns from these dismissals to improve over time.
Professional Judgment Preserved
AI flags potential issues; you make the final call. Your professional expertise remains the decision-maker.
Continuous Improvement
Accuracy is constantly improving. Our target is 90%+ actionable findings within the next few weeks. Here's what's driving improvement:
- Enhanced understanding of construction terminology and context
- Better interpretation of drawing symbols and notation
- Refined code parsing for edge cases and exceptions
- Learning from customer feedback on dismissed issues
- Expanded training on diverse project types
Accuracy by Issue Type
Some issue types have higher accuracy than others:
The Right Mindset for AI-Assisted Review
Think of AI plan checking as a highly capable assistant, not a replacement for expertise:
Do Think of AI As...
- • A tireless assistant that never misses pages
- • A first pass that catches obvious issues
- • A way to focus your attention on flagged items
- • A safety net for human oversight
Don't Think of AI As...
- • A replacement for professional review
- • Perfect or infallible
- • The final word on any issue
- • A substitute for engineering judgment
See the Accuracy for Yourself
Try InspectMind on your own project. Review the evidence for each finding and judge the accuracy based on your documents.