Text-to-CAD for product design: where it fits and where it doesn't

I was in a design review last month with a client who makes consumer electronics. Small team, tight timeline, two weeks to get from napkin sketch to prototype. The industrial designer had some hand-drawn concepts of the enclosure. The mechanical engineer had a rough sense of the board layout and connector positions. And someone, I think it was the project manager, asked whether AI could "just generate the CAD" so they could skip ahead to printing.

I said maybe, for parts of it. Then I spent the next hour demonstrating exactly which parts, and it turned out to be a narrower slice than anyone hoped.

Product design is not geometry generation. That's the core misunderstanding that text-to-CAD marketing tends to encourage. The geometry is one layer, and not even the hardest layer, of a process that includes user requirements, ergonomics, material selection, assembly design, tolerancing, DFM, testing, and iteration. Text-to-CAD can help with the geometry layer, specifically the early, rough, disposable version of it. Everything else is still a human job.

Where product design actually spends its time#

Before talking about what AI can do, it helps to be honest about where time goes in a real product design project. I've been doing this kind of work in Fusion 360 for years, and before that in SolidWorks, and the breakdown is roughly the same regardless of the tool.

Maybe 15-20% of the time goes into initial geometry creation. Sketching profiles, extruding features, cutting pockets, adding fillets. This is the part that demos focus on because it's visual and satisfying. A shape appears. Progress is visible.

Another 20-30% goes into assembly work. Making parts fit together. Defining mating relationships. Checking interference. Managing fastener access. Making sure the lid actually closes on the box, the board actually fits in the housing, and the cable actually reaches the connector. This is where simple-looking products become complicated, because every part exists in relationship to other parts.

The remaining 40-50% (often more) goes into detailing, tolerancing, DFM, testing, and revision. Adjusting wall thickness for moldability. Adding draft for tooling. Specifying surface finish on cosmetic faces. Running tolerance stacks to verify that the assembled product works across the worst-case combination of parts. Revising after a prototype reveals problems. Revising again after the tooling engineer says the ribs are too thin. Revising a third time after the client changes the board layout.

Text-to-CAD touches the first 15-20%. It doesn't touch the rest. That's not a criticism of the technology. It's a description of what the technology is: a geometry generator. The question for product design is whether faster initial geometry actually matters when the downstream work dominates the timeline.

Early concepts: the genuine sweet spot#

The one place text-to-CAD consistently saves me time in product design is at the very beginning of a project, when I need shapes to react to rather than imagine.

Before these tools existed, early concept exploration in CAD meant sketching several variations of a housing or bracket, each one taking 15-30 minutes in Fusion. Quick by detailed-design standards, but slow when you're trying to explore ten different form factors in a single afternoon.

With text-to-CAD, I can generate five or six variations of a basic enclosure in the time it takes my coffee to cool from painful to drinkable. "Rectangular enclosure 120x80x40mm with rounded corners." "Same but with a tapered front face." "Same but split horizontally with alignment pins." None of these models will be the final design. Most of them will be wrong in important ways. But they give me and the team something concrete to discuss, critique, and steer from.

I used this workflow on a recent project for a small sensor housing. Generated four enclosure shapes with Zoo.dev, pulled them into Fusion 360, dropped in the PCB model for a quick fit check, and had a design direction selected within an hour. The selected concept still needed complete rework: proper wall thickness, snap fits, cable routing, thermal venting, and about a dozen other details the AI didn't include. But the "what general shape are we going for" question was answered fast, and that let the real design work start sooner.

The assembly gap#

Product design lives and dies on assemblies. A housing is not just a housing. It's a housing that contains a PCB, a battery, a display, three connectors, two switches, and a lens. Each of these components has specific dimensions, mounting requirements, and keep-out zones. The housing exists to hold them all in the right positions relative to each other and relative to the user's hand.

Text-to-CAD tools can't generate assemblies. They generate single parts. You can ask for "an enclosure for an Arduino Nano with a USB-C port opening on one side and two mounting screw holes on the bottom," and you might get something that looks right. But the USB-C opening won't be positioned to match the actual connector location on the Arduino board, because the AI doesn't have the Arduino board model. The screw holes won't match the board's mounting pattern unless you specified the exact coordinates in the prompt, and even then, the accuracy is approximate.

I tried this experiment systematically with three different boards. Generated enclosures for each using detailed prompts that included every relevant dimension. The USB port cutout was misaligned by 1-3mm on every attempt. The mounting holes were off by up to 2mm. Close enough to see the concept, nowhere near good enough to print and assemble.

In real product design, assemblies drive the geometry. The part shape comes from the components it needs to contain, the manufacturing process it needs to survive, and the user interactions it needs to support. Text-to-CAD generates shapes without any of that context. The shape is freestanding. The product is not.

Material selection: invisible but essential#

When I design a consumer product enclosure, material choice is one of the first decisions. Is it injection-molded ABS? PC/ABS for impact resistance? Glass-filled nylon for stiffness? Silicone overmold for grip? Each material has different design rules. ABS needs different wall thickness than polycarbonate. Nylon has different shrink rates. Silicone has different Shore hardness options that affect the geometry of overmold features.

Text-to-CAD tools don't know what material the part will be made from. They generate geometry in a material vacuum. The walls are whatever thickness the training data averaged. The features are whatever the model learned was typical. There's no feedback loop between material properties and geometry.

This means the AI-generated starting point needs to be re-evaluated against the chosen material before any detail work happens. A 1.5mm wall might be fine for ABS but too thin for unfilled polypropylene. A snap fit designed at one thickness might need to be 20% thicker for a more brittle material. These adjustments aren't optional. They're the difference between a product that survives a drop test and one that doesn't.

Surface quality and cosmetic intent#

Product design, especially for consumer products, cares about surfaces in a way that mechanical engineering often doesn't. A visible face needs to be smooth. A parting line needs to be positioned where the user won't see it. A textured surface needs specific draft to release from a textured mold. A painted surface needs different geometry than a color-matched surface.

Text-to-CAD geometry has no concept of cosmetic intent. Every surface is equal. The fillet that transitions between the front face and the side wall is the same quality as the fillet hidden inside a cable channel. There's no distinction between A-surfaces (visible to the user) and B-surfaces (functional but hidden). There's no consideration of how light will play across a curved surface, or where a customer's thumb will rest, or which surface the marketing team will photograph.

For products where appearance matters, which is most consumer products, the AI-generated geometry is a starting shape that needs its surfaces completely rethought. That's normal in product design; surface refinement is always a separate pass. But it means the AI is contributing to the structural concept, not the finished design. The contribution is real but limited.

Ergonomics: the thing geometry can't capture alone#

A product that a human holds, touches, carries, or operates needs ergonomic consideration. Handle diameter. Grip contour. Button placement. Weight distribution. Viewing angles. These aren't add-ons. They're primary design drivers.

I asked a text-to-CAD tool to generate a handheld device enclosure. I got a rectangular box with rounded edges. It was technically holdable in the same way that a brick is technically holdable. The radii were arbitrary. The grip zones were flat. The weight distribution (if the internals were included) would have put the center of gravity in the wrong place. The button positions were decorative.

Ergonomic design requires understanding human hands, which come in different sizes. It requires testing with foam models, 3D-printed mockups, and user feedback. It requires the kind of judgment that comes from watching someone struggle with a prototype and knowing which surface to adjust. Text-to-CAD can generate the first foam-core-equivalent shape to hold in your hand and react to. It cannot design the final ergonomic form.

DFM: the wall between concept and production#

Design for manufacturability is where product design gets expensive if you ignore it. Every manufacturing process has constraints that need to be reflected in the geometry from early in the design process, not bolted on at the end.

Injection molding needs draft angles, uniform wall thickness, gate locations, and rib-to-wall ratios. Sheet metal needs bend radii and relief cuts. Die casting needs different draft than injection molding and has minimum wall thickness requirements tied to flow length. Even 3D printing has DFM rules around support, orientation, and feature resolution.

Text-to-CAD tools have no DFM awareness. I've covered this in detail in the manufacturing post, but the product design angle is slightly different. In product design, DFM isn't just about making the geometry producible. It's about making trade-offs between appearance, function, and manufacturability throughout the design process.

A product designer might choose to add a visible parting line on a less prominent surface to avoid a side action in the mold. They might thicken a wall to improve flow, even though it adds weight. They might split a part into two pieces to make it moldable, changing the entire assembly strategy. These decisions require understanding the manufacturing process, the cost implications, and the product requirements simultaneously. The AI generates a shape. The product designer generates solutions.

Where text-to-CAD fits in the product design timeline#

After working with these tools across several projects, here's my honest mapping of where they help and where they don't.

Weeks 1-2, concept exploration: genuinely useful. Generate multiple form factors quickly. Use them as conversation starters. Print rough shapes on FDM for early feel tests. This is prototyping territory, and text-to-CAD is good at it.

Weeks 2-4, detailed design: not useful. This is where you're building proper parametric models with assembly relationships, material-aware features, and DFM considerations. The AI-generated concept might inform the starting dimensions, but the actual CAD work is ground-up in Fusion or SolidWorks.

Weeks 4-8, refinement and validation: not useful. Tolerance stacks, FEA, mold flow analysis, interference checks, and drawing creation are all manual engineering tasks that require proper parametric models with full feature history.

Weeks 8+, production release: not useful. ECOs, revision management, and supplier communication require fully defined engineering models. The AI hasn't touched the file since week one.

The useful window is real but narrow. Maybe 10-15% of the project timeline, and only for the simplest parts in the assembly. The PCB mounting bracket. The cable guide. The battery holder. Not the main housing. Not the user-facing surfaces. Not the mechanism.

Simple components within a product#

There's one product-design use case where text-to-CAD reliably helps: generating internal structural components that don't interact with the user or the outside world.

A PCB standoff. A cable clip. A simple bracket that holds a speaker in place. An internal rib structure. These are the utility parts of a product, necessary but not interesting. They have simple geometry, forgiving tolerances, and no cosmetic requirements. They're exactly the kind of thing where spending fifteen minutes in Fusion feels tedious and a thirty-second AI generation feels like a win.

I've started using text-to-CAD for these components consistently, generating the first draft, importing into the assembly, checking fit, and adjusting. For a product with six to ten internal structural parts, this saves maybe an hour of total modeling time. Not transformative, but real. And it lets me spend that hour on the parts that actually need human attention: the outer surfaces, the mechanism, the ergonomics.

The workflow I've settled on#

For product design projects, my current text-to-CAD workflow is:

Use it for concept-phase exploration. Generate form-factor options. Print and hold them. Make decisions about overall shape and proportion.

Use it for simple internal components. Standoffs, clips, brackets, mounts. Generate, import, adjust, move on.

Don't use it for the primary enclosure past the concept phase. The surface quality, assembly relationships, and DFM requirements need proper parametric modeling.

Don't use it for any part that needs tolerances tighter than plus or minus 1mm. Don't use it for parts with complex surface transitions. Don't use it for parts that need to match specific hardware components without measuring and correcting first.

This gives me the speed benefits where they matter and keeps me in real CAD where accuracy matters. It's not the revolution the demos promise. It's a new tool in the box, useful for specific screws and not for others. Product design has always been about knowing which tool to reach for. Text-to-CAD is one more option, as long as you understand its boundaries.