Explainable AI for Construction
AI that explains its reasoning and recommendations.
Definition
Explainable AI for Construction provides transparency into how AI systems reach their conclusions. This is critical for construction where professionals must understand AI recommendations before acting on them. Explainable AI shows the reasoning, highlights relevant evidence, and helps users trust and verify AI outputs.
In Depth
Explainable AI shows its reasoning — not just the conclusion but the evidence and logic that led to it. In construction, explainability is not optional because professionals need to understand and verify AI findings before acting on them.
When AI flags a code compliance issue, explainability means showing the specific code section, the specific measurement or condition on the drawing, and the specific requirement that is not met. "Corridor width of 42 inches on Sheet A2.1, Room 204, does not meet the 44-inch minimum per IBC Section 1020.2" is explainable. "Potential egress issue detected" is not. The former gives the architect specific information to act on; the latter gives them nothing.
Examples
Showing code sections behind compliance checks
Explaining risk score calculations
Highlighting evidence for recommendations
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Explainable AI for Construction provides transparency into how AI systems reach their conclusions. This is critical for construction where professionals must understand AI recommendations before acting on them. Explainable AI shows the reasoning, highlights relevant evidence, and helps users trust and verify AI outputs.
Showing code sections behind compliance checks. Explaining risk score calculations. Highlighting evidence for recommendations.
Automated Code Compliance: Check drawings against 380+ building codes and standards with cited answers. Automated Drawing Review: Automatically review drawings against building codes, internal standards, and client requirements.