AI for Construction Quality Control

Quality control on construction projects traditionally relies on periodic inspections, testing, and the vigilance of superintendents and foremen. AI adds systematic monitoring that covers more of the project more of the time, catching quality issues earlier when correction costs are lowest.

Installation verification uses AI computer vision to compare installed conditions against the design documents. Photos of completed rough-in work are analyzed against the coordination model to verify that ductwork routing, piping layout, and electrical conduit paths match the approved shop drawings. Deviations are flagged before the work is concealed behind drywall, when correction is still feasible.

Documentation quality control verifies that the project's quality records are complete and consistent — test reports, inspection records, material certifications, and commissioning data. AI tracks which quality documents have been received, which are still outstanding, and which contain results that do not meet the specification requirements. This tracking prevents the closeout delays that occur when quality documentation gaps are discovered months after the work was completed.