PDS.01
Generative Concepting
Concept boards generated from your brief and brand language, then sorted by envelope, part count and process, so a designer explores widely without losing the constraints.
Open pageIndustry
Product design AI,
from sketch to bill of materials.
The designer still decides what ships. Concept exploration, manufacturability checks, should-cost and design search, wired into your CAD and PLM. The model widens the options and catches the expensive mistakes early; the designer still decides what ships.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI product design means using models to generate and compare concepts, check a part against your manufacturing rules, estimate what it should cost and search existing designs, so a design team spends its time on judgement rather than on rework. The designer still decides what ships.
Where it breaks today
01
Problems surface at tooling
A draft angle that is missing, a wall that is too thin to mould, a tolerance the supplier cannot hold: these are found when the toolmaker quotes, weeks after the design felt finished, and every loop back costs more than the last.
02
Cost arrives after the decision
The bill of materials is priced once supplier quotes come back. By then the material, the process and the part count are already chosen, which is exactly when cost is hardest to change.
03
Nobody searched properly
Prior designs, registered design rights and the studio’s own archive are rarely searched with the care a launch deserves, because image-based design search is slow and the archive lives in folders named by project number.
OUR APPROACH
AI in product design, where it pays
Most AI tools for product design are image generators. They are good at mood and form language and say nothing about whether the part can be made, what it will cost or whether someone already owns the shape. The systems we build sit on the other side of the sketch: they read the CAD, the material library, your supplier rates and your design rules, and they report against them.
The pattern is the same in each case. Your own rules become an explicit, versioned checklist. The model reads a design against that checklist and returns findings a designer can verify in seconds, each pointing at a feature, a line of the BOM or a registered design. Nothing is released by the system. Conformity under the EU General Product Safety Regulation (2023/988) or the Machinery Regulation (2023/1230), and the CE marking that follows, remain a responsibility of the manufacturer and the people who sign the technical file.
HOW IT DEPLOYS
PHASE 01
Write the rules down
We extract your design and manufacturing rules, supplier rates and review checklists from the people and documents that hold them today.
PHASE 02
Connect the tools
The system reads STEP and native files from SolidWorks, Fusion, Creo or Onshape, and the BOM from your PLM or ERP, so nobody re-keys a part.
PHASE 03
Review, then release
Findings land in the design review you already hold. A designer or engineer accepts or dismisses each one, and that decision is logged.
What we build
01
Generative Concepting
Concept boards generated from a brief, your brand language and a reference set, then sorted against the constraints that will matter later: envelope, part count, process. Variety early, without losing the thread.
SEE GENERATIVE CONCEPTING →02
Design-for-Manufacture Checks
Each revision read against your process rules for moulding, sheet metal or machining, with every finding tied to a feature in the model. The toolmaker sees fewer surprises.
SEE DFM CHECKS →03
BOM and Should-Cost
A cost model built from geometry, material, process and your own quote history, so a change of wall thickness or material shows its cost while it is still cheap to change.
SEE SHOULD-COST →04
Prior Art and Design Search
Image and text search across registered designs, design patents and your own archive, returning a shortlist with sources for a person, or your IP counsel, to judge.
SEE DESIGN SEARCH →Controls we implement
CAD STAYS IN YOUR PERIMETER
NO TRAINING ON YOUR DESIGNS
EVERY FINDING CITES ITS RULE
HUMAN SIGN-OFF ON RELEASE
VERSIONED RULE SETS
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and the audits and attestations your sector requires remain yours to run — see our trust centre.
Product design use cases
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PDS.02
Design-for-Manufacture Checks
Each CAD revision read against your moulding, sheet-metal and machining rules, with every finding tied to a feature. An engineer still signs the release; a clean report is not approval.
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PDS.03
BOM & Should-Cost Estimation
Should-cost for every BOM line from geometry, process and your own quote history, shown while the design can still change. Every estimate lists its drivers and its confidence.
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PDS.04
Prior Art & Design Search
Image and text search across registered designs, design patents and your archive, returning a sourced shortlist. Novelty and infringement opinions stay with IP counsel.
Open page
Questions
Questions about Product & Industrial Design.
How do you use AI in product design?
In four places where it saves real rework: generating and sorting early concepts, checking each CAD revision against your manufacturing rules, estimating what a part should cost from its geometry and material, and searching registered designs and your own archive before launch. In every case the model reports and a designer decides. Image generators help with form and mood; the larger savings come from the checks that run after the sketch.
Will AI replace product designers?
Not in the work that matters most: deciding what a product should be, for whom, and which trade-off to accept between cost, form and function. What AI changes is the volume of options a designer can look at and how early a manufacturing or cost problem becomes visible. The systems we build are written around a designer who signs off, and they log that decision.
Is generative design the same as generative AI?
No. Generative design in CAD tools such as Fusion or Creo is an optimisation method: you set loads, constraints and a process, and the solver produces geometry that meets them. Generative AI produces images, text or meshes from examples and prompts, and nothing in that process checks the result is structurally sound. They work well together, with generative AI for early concepts and optimisation for the engineered part.
Does it work with SolidWorks, Fusion, Creo or Onshape?
Yes. We read neutral STEP files from any of them, and native data through their APIs where the check needs feature history rather than just geometry. Bills of materials come from your PLM or ERP, such as Windchill, Teamcenter, Arena or SAP. The integration is usually most of the build, and it sets the realistic timeline.
Are our designs used to train a model?
No. Unreleased designs are among the most sensitive files a company holds. We deploy on infrastructure you control, or behind hosted-model terms that exclude training, and the rule sets and archives we build stay yours. Where an image model is used for concepting, it runs on your reference set rather than sending sketches to a public consumer tool.
