Construction drawing errors can lead to costly revisions, project delays, and coordination problems. A structured quality assurance and quality control (QA/QC) process helps architecture and engineering firms identify inconsistencies before issuing drawing sets. Pirros outlines a systematic approach that combines standardized checklists with artificial intelligence (AI) to improve drawing accuracy and review efficiency.
The process begins by freezing a dated drawing set, assigning reviewers, and examining drawings by series. Each sheet must be checked for accurate title blocks, revision information, references, scales, annotations, spelling, and compliance with company drafting standards.
Additional checks address discipline-specific requirements. General sheets must contain accurate building code information, while floor plans should align with structural drawings and schedules. Elevations, sections, and details require consistent dimensions, materials, and references. Coordination reviews verify that architectural, structural, mechanical, electrical, and plumbing systems agree.
AI can automate repetitive checks, including identifying missing references, inconsistent annotations, outdated code requirements, and discrepancies between drawings and specifications. However, decisions involving design intent, construction feasibility, and final approval remain the responsibility of qualified professionals.
Pirros offers an AI-powered Quality Management System that reviews drawing sets against selected building codes, company standards, specifications, and consultant documents. The platform identifies potential issues, provides supporting code references, assigns severity levels, and allows reviewers to document decisions.
At Number TEN Architectural Group, the system reportedly identified missing requirements involving sloped glazing and safety glass before a drawing set was issued for bidding.
Pirros also offers Mira, an AI agent for Autodesk Revit that checks drawing views against company templates, including line styles, dimensions, hatches, tags, and keynotes.
The article emphasizes that effective QA/QC requires consistent procedures throughout design development rather than last-minute reviews. Shared checklists, documented decisions, and AI-assisted verification can help firms reduce errors, improve accountability, and maintain construction documentation quality across projects.
