Quality Control AI
AI systems that automatically check documents and designs for errors and compliance.
Definition
Quality Control AI automates the review and verification processes that traditionally require experienced engineers and architects. These systems check drawings for completeness, verify specification compliance, identify coordination issues between disciplines, and flag potential errors or omissions. By leveraging AI trained on building codes and best practices, firms can perform more thorough QA/QC reviews in less time while maintaining consistency across projects and reducing the risk of costly errors.
In Depth
Quality control in AEC is traditionally labor-intensive and inconsistent. A senior architect reviews a set of drawings before issuing, checking for coordination between plans and sections, verifying that keynotes match the specifications, confirming that code requirements are met. The review quality depends entirely on the reviewer's experience, attention, and available time — which is always limited.
AI-powered QC applies consistent checking criteria across every sheet, every time. It does not get tired at 5 PM, it does not skip the mechanical sheets because it is an architectural firm, and it does not miss the code violation on sheet A3.2 because it was buried in a revision cloud. The checks run automatically and produce a report of findings with specific references to sheets, details, and requirements.
The key is that AI QC does not replace the experienced reviewer — it amplifies them. Instead of spending four hours hunting for issues across 200 sheets, the senior architect reviews a prioritized list of AI-flagged items in 45 minutes. Their expertise is applied to evaluating findings and making judgment calls, not to the tedious scanning work. This means better review quality with less time, and it frees senior staff for the design decisions that actually require their expertise.
Examples
Automatically checking that all referenced drawings exist and are current
Verifying that dimensions and scales are consistent across drawing sets
Identifying missing or incomplete specifications sections
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Compatible Platforms
Nomic integrates with these platforms so you can use quality control ai across your existing project data:
Frequently Asked Questions
Quality Control AI automates the review and verification processes that traditionally require experienced engineers and architects. These systems check drawings for completeness, verify specification compliance, identify coordination issues between disciplines, and flag potential errors or omissions. By leveraging AI trained on building codes and best practices, firms can perform more thorough QA/QC reviews in less time while maintaining consistency across projects and reducing the risk of costly errors.
Automatically checking that all referenced drawings exist and are current. Verifying that dimensions and scales are consistent across drawing sets. Identifying missing or incomplete specifications sections.
Automated Drawing Review: Automatically review drawings against building codes, internal standards, and client requirements. Automated Submittal Review: Let AI do the first-pass review of submittal packages against your drawings and specs.