Learn how InferTrust™ AEC creates sealed inspection records for AI-powered quality assurance, including concrete inspection, weld quality, and finish verificati
Construction quality assurance requires systematic inspection of materials, workmanship, and installed systems against project specifications and code requirements. AI-powered computer vision and sensor analysis tools are transforming quality inspection by detecting defects that human inspectors might miss and by enabling continuous monitoring rather than periodic spot checks. InferTrust™ (Patent Pending) AEC creates sealed inspection records for every AI-assisted quality evaluation, providing cryptographic proof of what was inspected, when, and what the AI found.
AI computer vision tools can evaluate rebar placement (spacing, cover depth, lap splice length, tie wire patterns) before concrete pours and analyze concrete surface conditions after placement. For each AI-assisted concrete inspection event, InferTrust™ AEC seals a record containing the image hash of the inspection photo or scan, the AI model version and the ACI 318 or project specification requirements it evaluated against, the specific elements checked (bar spacing, cover, splice length, embedment), and the finding (QA_PASS, QA_FLAG, QA_DEFECT, or QA_HOLD_POUR). These records provide verifiable documentation that pre-pour inspections were performed and that rebar placement met specification requirements, supporting both jurisdictional inspection requirements and long-term structural warranty claims.
AI-assisted weld inspection uses computer vision and, in some cases, ultrasonic or radiographic sensor data to evaluate weld quality against AWS D1.1 structural welding requirements. InferTrust™ AEC seals each weld inspection record with the image or sensor data hash, the weld joint identification (location, member, connection type), the AI assessment against applicable acceptance criteria (visual quality, profile, undercut, porosity indications), and the inspector's subsequent determination. For structural steel connections where weld quality is critical to load path integrity, these sealed records provide the verifiable inspection documentation that building officials and special inspection agencies require.
AI tools for finish inspection can identify surface defects, alignment issues, color inconsistencies, and incomplete installations across architectural finishes, flooring, painting, and millwork. InferTrust™ AEC captures these evaluations as sealed quality records, documenting the defect type, location, severity, and the subsequent corrective action. During the punchlist phase, this creates a verifiable record of every identified deficiency and its resolution status, replacing the informal punchlist processes that are difficult to audit and easy to dispute.
Drones equipped with high-resolution cameras and LiDAR sensors enable AI-powered progress monitoring and quality assessment across the entire project site. InferTrust™ AEC integrates with drone survey workflows by sealing each survey event with the drone flight log hash, the sensor data fingerprint, the AI analysis results (progress percentage, identified deviations from BIM, potential quality issues), and the survey timestamp and GPS coordinates. This creates a verifiable time-series record of construction progress and quality that supports schedule claims, progress payment verification, and quality trend analysis across the project lifecycle.
Many jurisdictions require special inspections for structural concrete, structural steel, fireproofing, and other critical building systems. When AI tools assist special inspectors in performing these evaluations, InferTrust™ AEC provides the sealed records that document exactly what was inspected and what was found. As jurisdictions increasingly accept technology-assisted inspections, these cryptographically verifiable records position firms and inspectors to meet evolving requirements for inspection documentation integrity.