Every AI plan review product claims to catch code violations before your drawings reach the building department. Almost none of them show you what a real finding looks like, including what the tool flags incorrectly or ambiguously. This walkthrough covers the anatomy of an AI plan check report, five realistic sample findings across disciplines, where automated flags go wrong, and what your licensed team must do with the output before making any correction.
About 14 min read
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The findings log is the operative section of any AI plan check report. Everything else is navigation.
InspectMind's report structure has four parts:
When evaluating any plan review software, the test is whether it provides sheet-level specificity and named code sections, not just category labels like "egress issue" or "electrical concern." Labels without citations shift the verification burden onto your team without giving them a starting point.
Getting the severity tiers wrong causes two opposite problems: treating every advisory as a confirmed violation wastes design hours, while ignoring advisories entirely leaves real AHJ questions unanswered.
This tier distinction is where most automated plan review tools provide no guidance at all. Knowing which findings demand a drawing revision versus which demand a professional review note is what separates a useful correction items list from a noise generator.
The anatomy of one finding entry shows whether the tool is giving your team something actionable. Here is what a realistic InspectMind finding looks like:
Every element in that entry has a purpose. The sheet reference tells the architect exactly where to look. The grid callout narrows it to a specific location. The code section tells the design team what provision applies. The violation description explains the gap between what is shown and what is required. The corrective action gives a resolution path without dictating the design decision. This is what differentiates a real AI plan check report from a tool that flags a category and leaves the next step to you.
The five findings below represent the range of confidence levels in automated plan review output. Not all of them are clear-cut violations. That range is what makes the walkthrough useful.
Discipline: Architectural / Fire and Life Safety | Severity: Critical | Sheet: A3.2 | Citation: IBC 2021 §1005.1
This is one of the highest-confidence finding types in automated plan review because the check is dimensional. The AI measures the corridor width shown on the floor plan, reads the occupant load from the occupancy data or schedule, and compares the result against IBC §1005.1's minimum clear width requirement of 44 inches for occupant loads exceeding 49. The drawing either shows the required dimension or it does not.
Egress width non-compliance is among the most frequently cited correction items on commercial permit submissions. Plan examiners check it routinely, and it is consistently missed when architects use drawing templates that predate a code cycle update. Your team's verification step here is confirming the occupant load basis, not debating the flag.
Discipline: Electrical | Severity: Critical | Sheet: E2.1 (panel schedule) cross-referenced to E1.0 (one-line diagram) | Citation: NEC Article 220 (Branch Circuit, Feeder, and Service Calculations)
Panel schedule overload flags are purely arithmetic. The AI reads the connected load totals from the panel schedule and compares them against the service entry rating shown on the one-line diagram. When the connected load exceeds service capacity, the discrepancy is flagged as critical.
This is a high-confidence finding because no interpretation is required. The AI compares two numbers from two sheets and reports the gap. Your verification step is confirming the load calculation methodology used, specifically whether demand factors under NEC Article 220 have been applied correctly. If they have, the flag may resolve through documentation. If they have not, the electrical engineer needs to revisit the service sizing.
Discipline: Architectural / Accessibility | Severity: Major | Sheet: A5.1 | Citation: ADA Standards for Accessible Design §604.3.1, Side Wall Grab Bar Location
Water closet centerline distance from the side wall and turning radius clearances in accessible restrooms are dimensioned requirements the AI can verify directly against the ADA Standards. Section §604.3.1 requires the water closet centerline to be 16 to 18 inches from the side wall. Standard fixture templates often place fixtures at nominal center positions that do not account for wall finish thickness, accessories, or door swing encroachment.
This finding type surfaces frequently on permit correction lists precisely because architects rely on templates. The actual dimensioned clearance once walls, accessories, and door swings are placed is often non-compliant even when the template looks correct. The ADA compliance checker covers this finding category across a full drawing set, not just restroom sheets.
Discipline: Fire and Life Safety | Severity: Major | Sheet: FP2.0 | Citation: NFPA 13 §8.5.5, Maximum Coverage Area per Sprinkler
Sprinkler coverage flags are moderate-confidence findings because the check depends on hazard classification. The AI compares ceiling plan sprinkler head locations against NFPA 13 maximum coverage area requirements, which differ by hazard category. If the hazard classification is clearly stated in the drawing set, the flag is reliable. If it is ambiguous or absent, the AI may apply a default assumption that does not match the AHJ's expected classification for the occupancy type.
The correct response to this flag is not to add sprinkler heads automatically. It is to verify which hazard classification the AI used and confirm it matches what the AHJ will apply. That determination belongs to the fire protection engineer. The fire and life safety checker provides the full coverage analysis across all fire protection sheets simultaneously.
Discipline: Structural / MEP | Severity: Advisory | Sheet: M2.1 | Citation: ASCE 7-22 §13.3, Seismic Demands on Nonstructural Components
Nonstructural component anchorage under ASCE 7-22 §13.3 requires that mechanical and electrical equipment be anchored per the project's seismic design category. The AI flags equipment shown on mechanical or electrical plans without a corresponding anchorage detail or specification callout. This is an advisory finding, not a confirmed violation.
The equipment may be addressed in a general note on a specifications sheet the AI did not associate with this element. The anchorage may be a correctly noted deferred submittal. Or the callout may exist on a structural sheet that was not directly cross-referenced against the MEP plan. Your team must determine whether the requirement has been addressed elsewhere in the document set.
The number of advisory findings in a report does not indicate tool failure. It indicates the document set has areas of ambiguous or incomplete coordination where an AHJ will likely ask questions regardless of whether the AI flagged them.
AI plan check output includes false positives. Pretending otherwise produces tools that AEC professionals stop trusting after the first project.
Three conditions account for most false positives in automated plan review:
The practical workflow instruction is the same across all three cases: treat every finding as a review prompt, not a confirmed violation. The AI has identified something worth checking. The licensed professional determines whether a violation exists.
An AI plan check report does not transfer professional liability under state architecture or engineering licensing laws. The licensed design professional who seals the drawings retains full responsible charge regardless of what the AI flagged or failed to flag.
NCARB's position on responsible charge requires that a licensed professional exercise independent professional judgment. Using an AI report as a pre-submission QA tool is consistent with that obligation. Relying on an AI report in lieu of professional review is not.
The correct framing for AEC teams using automated plan review is this: the AI report identifies candidates for design team review, not confirmed violations for automatic correction. A finding the design team reviews and determines to be a false positive should be documented as reviewed. A finding the team confirms as a real issue should be corrected before the building permit application goes in.
For structural findings specifically, the reducing plan check comments guide covers the pre-submission coordination steps that reduce the correction items list at the AHJ. Understanding the distinction between plan check and plan review also matters here: what the AI does is pre-submission code compliance analysis, not a substitute for the formal permit approval process.
InspectMind returns a numbered findings log with sheet references, code citations, severity tiers, and corrective action prompts within hours of upload. The platform reviews the full document set simultaneously, covering architectural, structural, MEP, civil, and specifications in a single pass. Cross-discipline conflicts appear in the same report alongside single-sheet violations, because the AI plan check engine reads the entire construction document set together.
Pricing starts at $50 per upload with no per-user fees. Invoice is available for enterprise teams. The issue guarantee is 5 or more issues or a full refund. For firm-level workflows and pre-submittal QA protocols, the pre-permit QA guide covers how design teams integrate AI review into their submission process.
A numbered findings log with sheet references, grid or detail callouts, applicable code sections, violation descriptions, severity classifications, and suggested corrective actions. The report also includes a summary section showing issue counts by discipline and severity tier, and a caveat block stating the professional review obligation. The findings log is the operative section.
Most platforms, including InspectMind, apply model code editions as the baseline for code compliance analysis. Local amendments and jurisdictional requirements vary by AHJ and are not always captured in the AI's code library. Your team should cross-reference the findings against the adopted local code edition and any published amendments before relying on the report for a specific building permit application. ICC permit processing performance benchmarks vary significantly by jurisdiction, which is one reason pre-submission review matters more in high-volume AHJ queues.
No. The report is a pre-submission QA document for the design and construction team, not a document the AHJ expects or accepts as part of the permit application package. Some AHJs are evaluating AI-assisted plan review as an internal tool, but that is a separate workflow from what a private-sector design team uses for pre-submission review.
For dimensional checks against specific code thresholds, automated plan review performs consistently and with high confidence. For findings that depend on hazard classification, occupancy interpretation, or performance-based design alternatives, AI flags require professional review before acting on them. The most accurate framing is that AI and human review catch different categories of issues, and pre-submission AI review reduces the correction items list a plan examiner would otherwise generate.
Review it as a question worth answering before submission, not as a confirmed violation requiring a drawing change. Check whether the requirement is addressed in the specifications, in a general note on another sheet, or in a correctly noted deferred submittal. Document the team's determination either way. Ignoring advisories entirely leaves the same questions open for the plan examiner.
It does not reduce or transfer liability. The licensed professional who seals the drawings retains full responsible charge under state licensing law regardless of what the AI flagged or missed. Using an AI report as a pre-submission QA check is consistent with responsible charge. It is a tool for professional review, not a substitute for it.
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