Where AI is actually useful in manufacturing engineering
The highest-value application of AI in a manufacturing engineering workflow isn’t decision-making — it’s the repetitive data work that sits between a drawing arriving and an engineer being able to make a decision about it. Reading a title block, ballooning every dimension on a multi-sheet drawing, and re-entering the same part data into three different systems are all time-consuming, error-prone, and not where an engineer’s judgment actually adds value.
What this looks like in practice
- Drawing extraction — reading a casting or machining drawing’s title block (part name, drawing number, revision, material, weight) directly into a system, instead of manual re-typing.
- Automated dimensional ballooning — numbering and plotting every dimension and tolerance across a multi-sheet drawing automatically, for an engineer to review and correct rather than build from scratch.
- Confidence-scored review — every extracted or plotted item carries a confidence level, so an engineer’s attention goes where it’s actually needed instead of re-checking everything uniformly.
- Costing assistance — rolling up material, tooling and process costs from already-extracted engineering data, instead of rebuilding the same calculation by hand for every RFQ.
InFaktory is a working example of this model applied to die-casting RFQs — AI handles extraction and ballooning, adaptively sweeping complex drawings in tiles for accuracy, while engineers review, correct and approve at every stage.
Why “assistance” is the right frame, not “replacement”
Manufacturability judgment, process selection, and final sign-off on a quotation depend on experience that isn’t fully capturable in a drawing — draft angles that look fine on paper but cause ejection problems in practice, gate placement decisions, tolerance stack-up judgment calls. AI tools here are built to remove the repetitive data-entry layer around that judgment, not to replace it. Framing these tools as full automation sets an expectation they can’t responsibly meet, and undersells what they’re actually good at.
Beyond die-casting
The same underlying approach — structured extraction from engineering documents, confidence-scored review, and automated roll-up into downstream systems — applies beyond die-casting RFQs, to machining drawings, inspection documentation, and other repetitive engineering-adjacent paperwork. See Manufacturing Software Development for the broader category this fits into.