Applied AI for Engineering and Product
Engineering organizations carry an enormous amount of knowledge in forms that are technically accessible and practically unusable: legacy drawings, prior change orders, requirements documents, and test reports. The cost shows up as rework, as parts designed that already exist, and as a change whose downstream impact is discovered after release.
In short
Mach12 stands up AI labs that apply AI across engineering and product development: design and change management, bill of material intelligence, requirements tracing, technical documentation, and the engineering to manufacturing handoff. The lab connects PLM, ERP, and engineering tooling so design intent survives the trip to production.
Where Engineering Time Goes
These are the patterns we see most. In our experience they come down to the distance between what the systems already hold and what anyone has had the hours to do with it, rather than to a technology gap.
Change impact is underestimated until release
An engineering change touches BOMs, routings, tooling, documentation, suppliers, and open orders. Tracing all of it manually is slow, so it gets traced partially and the rest is discovered downstream.
Parts get designed that already exist
Reuse depends on being able to find a comparable part in a catalog of hundreds of thousands. Classification is inconsistent and searching by attribute rarely works, so new part numbers keep getting created.
Requirements tracing is manual and therefore incomplete
Linking requirements through design, test, and verification is a documentation exercise done under schedule pressure. Coverage gaps are found during review, or later.
Technical documentation lags the design
Manuals, work instructions, and service documentation are written after the fact by people who were not in the design decisions. They are correct on the day they ship and drift from then on.
The engineering to manufacturing handoff loses context
Manufacturing receives a released design without the reasoning behind it. Questions that could be answered in a sentence become change requests.
Design history is unusable under time pressure
The answer to a current problem is often in a test report or a change order from years ago. Finding it inside the window available to decide is another matter.
What the Lab Builds
Built against your systems and your data, shipped into production with the people who use them. A given lab will build a subset of this, in whatever order discovery ranks it.
Change and configuration intelligence
Full downstream impact traced before a change is released rather than after.
- Change impact analysis across BOM, routing, tooling, and documentation
- Affected open order, inventory, and supplier identification
- Effectivity and cut-in planning support
- Change package assembly and completeness checking
Part and BOM intelligence
Making a large part catalog searchable by what a part is rather than what it was called.
- Similar part retrieval by attribute, geometry, and specification
- Duplicate and near-duplicate part identification across the catalog
- BOM comparison, rollup, and variance explanation
- Classification and attribute enrichment on legacy part data
Requirements and verification
Tracing maintained continuously instead of assembled for a review.
- Requirement extraction from specifications and statements of work
- Traceability matrix maintenance across design, test, and verification
- Coverage gap detection before design review
- Test report analysis against requirement acceptance criteria
Technical documentation
Documentation generated from the design and kept current as it changes.
- Work instruction and service documentation generation from design data
- Documentation currency checking against released changes
- Legacy drawing and document data extraction
- Engineering question answering over design history and test records
Typical First Builds
Chosen for speed to production as much as for value. We would rather have something working in your environment early than something more ambitious on paper.
- 01Change impact tracing for the change type that causes the most downstream rework
- 02Duplicate part detection across the existing catalog
- 03Requirement extraction from incoming specifications
- 04An engineering answer agent over design history and prior test reports
Built Against What You Run
Connectors are built during stand-up. You do not replace anything first, and your data does not leave your boundary.
Relevant Accelerators
Applications we have already built in this area. A lab can deploy one as-is, extend it, or use it as the pattern for something new.
Common Questions
- Is our intellectual property safe in this?
- The lab runs inside your boundary, on infrastructure you control or a compliant environment we host for you. Your design data is not used to train anyone else's model. For organizations with export control obligations, that boundary is designed in from stand-up rather than added later.
- Can it read legacy drawings that only exist as scans?
- Often yes, and it is a common early build because the value is obvious and the scope is contained. Accuracy depends on scan quality, and the evaluation harness tells you what the real extraction rate is before anyone relies on it.
- Will this write CAD?
- No. Generative design is a different problem with mature tools already in the market. The lab works on the information layer around engineering: change, configuration, requirements, documentation, and history, which is where most of the recoverable time sits.
- How does this interact with our PLM implementation?
- PLM stays the system of record. The lab builds against it. If a PLM migration is underway, the lab can help with data preparation and classification, which tends to be the part that stalls those programs.
Common in these industries
The work carries across industries. These are where we see it most often.
Build This in Your Business
Tell us what your engineering & product function runs on and where the work is piling up. We will scope the first build.
Start a Lab