Design Review Comments and Markups for AI: QA/QC Data from Engineering Firms

Why internal QA/QC review data differs from public plan review comments

Internal design review comments are a firm's own quality control, so their labels, authors and rights differ from comments issued by a permitting authority. A building department's plan review cites code sections against a permit set; public examples exist, such as Seattle's Plan Comments records on data.gov [3], and those are covered on our page on plan review comments and code corrections. Internal QA/QC review happens earlier and covers far more: interdisciplinary coordination checks, senior peer review, constructability reviews by field staff, and drafting-standard checks before a set leaves the office.

That breadth is what drawing-review copilots need. A model that only learns code corrections will miss the clash between a duct run and a beam, the missing detail callout, or the spec section that contradicts the schedule. Public research datasets are thin here: one shared design review corpus, DTRS 10, contains videos and time-stamped transcripts of design critiques [2], which is conversational rather than labeled, sheet-anchored engineering QA/QC.

Drawing understanding benchmarks such as MechVQA test question answering on mechanical drawings collected from textbooks and design platforms [4]. They do not contain reviewer judgments about whether a real project set is correct. Review comments with resolutions supply that judgment signal.

What a review comment record contains in markup exports

A review comment record is only useful for training when it keeps the comment text, its location on the sheet and its outcome together. Most firms hold this data in three forms:

• PDF annotations. Markups made in PDF review tools are stored as annotation objects on the page (cloud, callout, text box, polyline, stamp) with a rectangle in page coordinates, author, subject, creation and modification dates, status and reply threads. Many tools export a markup summary as CSV or XML, or annotations as XFDF.
• BIM issues. Model-based coordination issues are often exchanged as BCF, the buildingSMART standard in which each issue is a "topic" with markup metadata, comments, viewpoints and snapshots, referencing IFC elements by GUID.
• Comment-resolution logs. Many firms keep a spreadsheet or document-control register per review cycle with comment ID, sheet, discipline, comment, response, action and closure date.

Ask which of these a supplier actually holds. Review comments are frequently scattered across email and PDFs rather than tied to model versions [1], so a firm's archive may need reconciliation before it becomes one table.

Illustrative example: invented to show structure; it does not describe an available dataset.

Comment categories and labels to request

The most valuable label set separates comment type, disposition and severity, because a copilot must learn both what to flag and what not to flag. Typical categories in engineering and architecture QA/QC are coordination conflicts between disciplines, code or standard compliance, constructability, missing or incomplete information, and drafting standards (title blocks, dimensions, symbols, sheet references). Treat any firm's category scheme as a hypothesis to validate on a sample, since firms define these differently.

Disposition is the label buyers most often forget. Comments marked "no action," "disagree" or "superseded" are useful negatives: they show where a reviewer's concern did not hold up, which helps reduce false positives in automated review. Request the full disposition history, not just closed status.

Severity or priority fields, where present, help build an evaluation set weighted toward issues that would have produced RFIs or change orders. If a firm can link a QA/QC comment to a later RFI, that link is rarer and worth asking about explicitly. For a related pattern in another domain, see our guide to construction submittal review data.

Preparing sheets and markups for multimodal training

Keep the markup coordinates and the underlying sheet image together, because a comment without its location cannot train issue localization. Practical preparation steps:

1. Render each reviewed sheet at a fixed DPI and record the transform from PDF points to pixels so bounding boxes remain valid.
2. Preserve the revision the markup was made on; comparing against a later revision is how you confirm the fix.
3. Separate review markups from design content (redlines from the drafter, plotted clouds, delta triangles) so the model does not learn to treat revision clouds as reviewer comments.
4. Normalize sheet numbers to a standard naming convention, such as discipline prefix plus sequence, so joins work across projects.
5. Split train and evaluation sets by project, not by comment, to avoid leakage from repeated details and typical sheets.

Document the dataset in a machine-readable form that records labeling procedures and life cycle steps; Croissant-RAI is one vocabulary built for that purpose [5]. A model card for a drawing-review copilot should be able to say which review types, disciplines and project types it was trained on.

Rights, client contracts and federal project restrictions

The firm that wrote the comments rarely owns every right in the drawings beneath them, so rights review must cover the client agreement as well as the firm's own policies. Owner-architect and owner-engineer agreements often address ownership and licensing of drawings and other project documents, and some include confidentiality clauses that restrict disclosure beyond the project. Comments alone may be releasable while full sheets are not; a supplier may offer text and coordinates with cropped or redacted sheet regions.

Federal and defense projects add another layer. Drawings and specifications for these projects may carry Controlled Unclassified Information, which the federal CUI Program governs under 32 CFR Part 2002. That program typically reaches contractors through their agreements, so ask suppliers to exclude CUI-marked projects or confirm decontrol before release. Security-sensitive facility details, such as critical infrastructure layouts, deserve the same screen even without a formal marking.

Remove reviewer names, initials embedded in annotation author fields, and client and site identifiers, while keeping discipline and seniority. Annotation author fields and stamps often contain usernames that standard text redaction misses. Language models can memorize and regurgitate training text verbatim, including contact details [6], so redaction must happen before training.

This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Buyer request checklist

A precise request names the review types, formats and labels you need rather than a firm or a project. Use this checklist when describing the data to any supplier.

Illustrative example: invented to show structure; it does not describe an available dataset.

• Review types: internal QA/QC, senior peer review, constructability review; state whether BIM coordination issues are in scope.
• Disciplines and project types: for example architectural, structural, MEP; healthcare, education, industrial.
• Formats: PDF with native annotations, markup summary CSV or XFDF, BCF topics, comment-resolution registers.
• Required fields: sheet number, revision, markup geometry, category, comment text, response, disposition, closing revision.
• Negatives: include dismissed and superseded comments.
• De-identification: reviewer and client names removed or replaced; method documented; sample checked.
• Exclusions: CUI-marked or security-sensitive projects; projects whose client agreements bar disclosure.
• Use: fine-tuning a comment generator, issue detection on sheets, or a held-out evaluation set.

Teams sourcing adjacent engineering data can compare this against CAD and engineering drawings for AI training and CAD and PCB design datasets. For review-and-resolution data in software, see code review comment resolution data.

How SourceX sources design review data

SourceX sources operational datasets, including engineering records and documents, from US companies and manages the licensing process; it does not hold this data in stock, and a request does not guarantee a match. Buyers describe the data they need, and SourceX looks for US businesses that hold it; every release is approved by the supplying company. Each dataset is rights-reviewed for ownership and consents, personal details are removed or replaced before delivery with the method recorded and a sample checked, and delivery follows an executed license that defines records, uses, term and delivery. You can describe your design review data requirements to SourceX, and see the wider industry operational data guide and the engineering consultancies buyer page.

Source design QA/QC review comments for your copilot

SourceX sources operational data such as engineering records from US companies on request and manages the license, from assessment of data and licensing permissions through agreement and delivery. Nothing is contracted until a supplier agrees, and a request does not guarantee a match. Describe the design review data you need.