AI Tools for Engineers: CADDi Drawer & CADDi Quote

CADDi could replace the manual, non-AI workflows that engineers use to search, compare, and reuse historical engineering data.

How specifically will this AI tool help engineers?

• Converts legacy drawings into usable engineering data. CADDi applies advanced OCR and AI models to extract text, dimensions, GD&T symbols, annotations, and handwritten notes from 2D drawings and scanned documents. Information that was previously locked in PDFs, images, or paper archives becomes searchable and analyzable.

• Enables geometry-based similarity search. Beyond keyword search, CADDi extracts shape and geometric features from drawings and builds an internal geometric representation. Engineers can search for parts based on visual and geometric similarity, even when part names, file structures, or naming conventions differ across systems or acquisitions. This allows teams to identify duplicate parts, create part families, and uncover prior designs that can be reused or adapted rather than recreated.
• Automates drawing comparison and revision analysis. The platform highlights differences between revisions or similar drawings, allowing engineers to quickly see what changed and assess implications for manufacturability, cost, or quality without manual side-by-side review.
• Preserves design intent and institutional knowledge. CADDi links drawings with design review notes, quality inspections, NCRs, and historical decisions. Engineers gain visibility into why a design was changed, what problems were encountered, and how those issues were resolved, even when the original engineers are no longer available. This prevents the loss of tribal knowledge as experienced engineers retire.
• Connects engineering decisions to manufacturing outcomes. By linking engineering data with procurement history, supplier performance, lead times, margins, and quality outcomes, engineers can evaluate tradeoffs earlier in the design process using real operational evidence rather than assumptions.
• Supports assembly and BOM-level navigation. For assemblies and subassemblies, CADDi automatically extracts BOMs and links components across documents. Engineers can navigate from a decades-old assembly drawing to individual components, identify alternatives, and evaluate reuse or replacement options for maintenance, repair, or aftermarket needs.
• Enables AI-assisted exploration with human oversight. Engineers can use conversational AI to query historical documentation, such as identifying recurring quality issues or summarizing past defects. Every AI-generated response is linked back to source documents, keeping engineers in control of decisions and maintaining traceability.
• Reduces non-design work. By eliminating time spent searching shared drives, PLM exports, PDFs, and paper archives, engineers spend less time hunting for information and more time designing, reviewing, and improving products.

Will this AI tool replace a non-AI tool or process?

Yes. CADDi replaces the manual, non-AI workflows that engineers use to search, compare, and reuse historical engineering data.

It eliminates activities such as:

• Navigating shared drives and legacy folder structures

• Manually searching PDFs and scanned drawings

• Relying on individual memory to locate past designs or suppliers

• Hand-driven drawing comparisons

• Recreating designs that already exist elsewhere in the organization

CADDi does not replace CAD, PLM, or ERP systems. It replaces the fragmented processes engineers use to extract insight from those systems.

The result is faster access to prior work, reduced duplicate design effort, improved consistency, and engineering decisions grounded in accumulated knowledge rather than individual recollection.

Read the article in Design News