2D design verification is challenging not because drawings are missing, but because consistency across drawings, BOMs, grids, and metadata is hard to maintain at scale. This article explains why manual checks break down—and how automation fixes it.
In many mechanical and systems engineering organizations, 2D technical drawings remain a contractual and operational reference for manufacturing, integration, and quality. Design completeness in this context means more than “the drawing exists.” It means the drawing, the layout grid, the BOM/checklist, and the title block are all consistent, exhaustive, and aligned.
In practice, teams must continuously reconcile multiple sources of truth:
When these elements drift—even slightly—teams risk documentation errors that cascade into rework, compliance issues, and late-stage changes.
Most organizations rely on manual verification to ensure that what is declared in a BOM/checklist actually appears in the drawing, in the right place, on the correct sheet, with the correct metadata.
This is precisely the type of task where automation delivers immediate leverage: high repetition, high consequence, and clear rule logic.
A robust 2D checker typically covers four validation families:
Dessia’s AI-powered 2D Checker library automates the extraction, structuring, and validation of engineering information from 2D drawings and BOMs. The goal is simple: turn unstructured drawing data into structured, checkable objects, then run repeatable rules across the full dataset.
The checker reads native 2D drawing files from major CAD environments and detects content across sheets and subviews.
It extracts, for each tag and annotation:
Many drawings use a row/column grid to encode placement. The checker:
This converts “where it is on the page” into a machine-readable location reference.
Engineering BOMs are frequently shared as exported spreadsheets or unstructured PDFs. The checker:
Once both worlds are structured, the checker runs validation rules such as:
Results can be flagged using a clear visual identification system (for example, color-coded severity) and exported as a verification report.
A completeness checker is only valuable if its output is actionable for engineers. Typical outputs include:
This transforms verification from “manual proof-reading” to “exception handling.”
By integrating Dessia’s 2D drawing checker into the design process, teams can move from periodic, manual audits to continuous, rules-based verification—without scaling headcount.
Typical impact observed:
Most importantly, design engineers spend less time “chasing inconsistencies” and more time on design intent and engineering decisions.
Automated drawing completeness checks are especially relevant in domains that produce large drawing sets under strict documentation constraints, including:
Anywhere 2D drawings remain a formal reference, completeness checking becomes a repeatable performance lever.
Yes, checkers are designed to be reusable. Once developed, a verification application can be adapted to other projects by simply changing certain parameters (business rules, input data formats, etc.). This allows teams to capitalize on development efforts and standardize best practices.
It depends on the complexity of the business rules to be implemented, but in general, a first functional checker can be developed within a few days. Development time is even shorter when starting from an existing base or using pre-built generic rules.
The cost depends on the functional scope, the desired level of automation, and the number of rules to be implemented. However, thanks to the reusability of checkers, the investment is quickly recouped when applied across multiple projects or product families. Moreover, using checkers often helps reduce manual review time, errors, and production rework.
Yes. Checkers-fully python based-are fully editable and scalable. It’s easy to add or modify rules, change input or output formats, or integrate new business constraints based on field feedback or evolving internal standards.
Rules can be geometric (distances between elements, alignment, overlap, orientation), functional (compliance with templates or restricted areas), or business-specific (adherence to internal standards or customer requirements). Checkers can also include conditional logic or criticality levels.
The 2D Checker connects at the verification stage. It reads 2D CAD drawings to extract callouts, grids, and title block data, then cross-checks this against BOMs/ rules checklists from Excel. Results are pushed back into PLM or reporting tools, embedding consistency checks directly in the design process.
Published on
05.02.2026
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