Automate QA/QC across design teams, enforce drawing standards, and catch coordination errors before documents leave your office.
Three structural challenges make consistent quality hard to deliver across teams, offices, and milestones.
Multiple designers and engineers produce drawings with varying quality levels, and manual QA/QC reviews are inconsistent and time-consuming.
Firm-wide drawing standards, CAD protocols, and documentation requirements are difficult to enforce consistently across projects and offices.
Design errors found after document issuance cause change orders, delays, and liability, but thorough pre-issuance review is hard to scale.
Make QA/QC a repeatable system instead of a hero effort by your most senior reviewer.
Run automated quality checks on every drawing set before issuance to catch coordination errors, missing information, and code compliance gaps that manual review misses under deadline pressure.
Automatically verify that architectural, structural, mechanical, electrical, and plumbing drawings are coordinated, catching conflicts between disciplines before they become field problems.
Check drawing sets against your firm's documentation standards to identify formatting inconsistencies, missing details, incomplete schedules, and non-standard notations.
Track issues identified at SD, DD, and CD milestones to verify that previously flagged problems are resolved in subsequent submissions and do not persist through to construction.
Generate objective quality metrics across projects and teams to identify patterns, training needs, and opportunities to improve design processes and reduce errors.
The QA/QC findings that recur across milestones and that we surface automatically.
What automated drawing review delivers across deadlines, milestones, and team members.
Manas is the co-founder and CTO of Helonic, where he leads engineering and AI research for construction drawing analysis. He works directly with structural, MEP, civil, and fire protection engineers to translate the way they review drawings into AI systems that flag the issues that actually matter in the field. Before Helonic, he built machine learning pipelines for technical document understanding and has spent the last several years interviewing licensed design engineers and discipline leads to ground product decisions in real practice rather than industry assumptions.
How this page was researched: Guidance references internal QA/QC standards, drawing-standard enforcement, and milestone review design managers run. Examples are drawn from Helonic's review of in-progress sets across disciplines.
Last reviewed by Manas Gandhi · May 2026
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