Text-to-CAD for enclosures: a practical test

I have a Raspberry Pi 4 sitting on my desk in a 3D-printed case that I designed two years ago in Fusion 360. Four standoffs, a snap lid, ventilation slots, cutouts for HDMI, USB, Ethernet, GPIO, and the SD card. It took me about three hours to model, including the snap-fit tuning that required two test prints before the lid stopped either popping off when you looked at it wrong or requiring a flathead screwdriver to remove. That case is the benchmark I used when I decided to test whether text-to-CAD tools could generate electronics enclosures.

The results were educational. One tool produced something I could work with. Two others produced geometry that ranged from "almost a box" to "conceptually adjacent to an enclosure." None of them got close to what I'd call production-ready, but the gap between the best and worst was large enough to be interesting.

I tested Zoo.dev, CADScribe, and AdamCAD. Same prompt for each: "Electronics enclosure for a Raspberry Pi 4, rectangular, 95x65x35mm, 2mm wall thickness, snap-fit lid, four M2.5 standoffs matching the Pi's mounting holes, ventilation slots on both long sides, USB and Ethernet cutouts on one short side, HDMI cutout on the opposite short side, SD card slot on the same side as HDMI."

That's a detailed prompt. Probably more detailed than most users would write. I wanted to give each tool the best chance of producing something useful, and I wanted to compare them on the same specification.

Zoo.dev: the closest to usable#

Zoo.dev generated a rectangular enclosure with approximately the right overall dimensions (it was 93x64x34mm, close but not exact). The walls were fairly consistent at around 1.8mm, which is slightly thinner than the 2mm I requested but within the range where FDM printing still works fine.

The standoffs existed. Four cylindrical bosses inside the enclosure, positioned in a pattern that was close to the Raspberry Pi's mounting hole layout but off by about 1.5mm on two of the positions. Close enough to see the concept, not close enough to actually mount a board without drilling out the holes and hoping.

The ventilation slots were there, which surprised me. Eight rectangular slots on each long side, evenly spaced. They were a bit narrower than I'd have designed by hand (about 1.2mm wide, where I'd normally do 2mm for better airflow), but they existed and were correctly oriented.

The lid was where things got complicated. Zoo.dev generated a flat lid that sat on top of the enclosure walls, but the "snap fit" was more of a friction fit using thin tabs on the lid edges. The tab geometry was too thin to actually flex and snap, and the corresponding features on the enclosure walls were shallow enough that the lid would slide off with a mild shake. This is a hard thing to get right, even for human designers, so I'm not surprised the AI struggled. But a snap fit that doesn't snap is just a fit, and not a very good one.

The port cutouts were approximately located but not accurately sized. The USB opening was a single rectangular cutout where the Pi has two stacked USB-A ports and two USB-C ports. The Ethernet cutout was close but about 1mm too narrow. The HDMI cutout was positioned too high by about 2mm.

Overall verdict: this was the best of the three. With 30-45 minutes of cleanup in Fusion 360 (correcting standoff positions, fixing port cutout dimensions, redesigning the snap fit, and adjusting wall thickness), I had a printable enclosure that actually held a Pi. The AI saved me maybe an hour of initial modeling time compared to starting from scratch. Net time savings: 15-30 minutes. Not zero, but not the revolution the marketing promises either.

CADScribe: the shape without the features#

CADScribe generated a rectangular box. Just a box. With a lid that was a separate flat plate. No standoffs inside. No ventilation slots. No port cutouts. The dimensions were roughly right (96x66x36mm), and the wall thickness was consistent at about 2.1mm, which was actually closer to my spec than Zoo.dev managed.

But the feature list from my prompt was almost entirely ignored. I got the overall enclosure shape and walls. Nothing else. No mounting features, no openings, no snap geometry, no ventilation.

I tried a simplified prompt: "Simple box enclosure 95x65x35mm with four holes in the bottom for M2.5 screws and a removable lid." The box came back with the lid, but the four holes were positioned in a symmetric grid pattern that had nothing to do with the Raspberry Pi's mounting layout. At least the holes existed.

CADScribe seems to work best for the simplest enclosure geometry: a box with a lid. Anything beyond that requires manual modeling, which is the thing you were trying to avoid. As a starting point for "I need a box shape to work from," it functions. As an enclosure design tool, it doesn't.

AdamCAD: better parametrics, limited features#

AdamCAD took a different approach. It generated a rectangular enclosure with adjustable dimension sliders, which is useful for dialing in the size. The overall dimensions were close (94x64x34mm), and I could adjust them with the parametric controls. Wall thickness was controllable too, which is a nice touch.

The standoffs were generated as simple cylinders, four of them, but they were positioned based on the AI's interpretation of "matching the Pi's mounting holes," which was off by 2-3mm on three of the four positions. The parametric controls didn't extend to standoff positions, so fixing them required exporting and editing in another tool.

No ventilation slots. The snap fit was a basic tongue-and-groove around the lid perimeter, which would actually work for a friction fit but wouldn't snap. Port cutouts were absent. The SD card slot was ignored entirely.

AdamCAD's strength is in the parametric adjustment after generation, but the feature set for enclosures is limited to the basic box plus simple internal features. For a plain enclosure where you plan to add all the specific features in your own CAD tool, it gives you a dimensionally adjustable starting shape. That's something, but it's not an enclosure design tool.

What all three got wrong#

Some failures were consistent across all three tools, which suggests they're fundamental limitations of current text-to-CAD technology rather than bugs in specific implementations.

Snap fits. No tool generated a functional snap-fit mechanism. This is one of the most common features in plastic enclosures and one of the hardest to generate correctly because it requires understanding material deflection, interference fits, and the manufacturing process (snap fits need draft on both sides for injection molding). For 3D printing, the tolerances are different. The AI doesn't model any of this. It generates tab-like geometry that looks like a snap fit in a render but doesn't function as one.

Standoff positioning. All three tools positioned standoffs based on approximate symmetry rather than the actual Raspberry Pi mounting pattern (which is 58mm x 49mm, not centered in the board outline). This is a specific-knowledge problem. The AI doesn't have a database of PCB mounting patterns, so it places standoffs where they look reasonable rather than where they need to be.

Port cutouts. Even with specific descriptions, the cutout geometry didn't match real connector dimensions. The tools seem to generate generic rectangular openings rather than sized cutouts for specific connectors. This makes sense given that the AI doesn't have access to connector specifications, but it means every cutout needs manual correction.

Wall thickness consistency. All three tools produced enclosures where the wall thickness varied by up to 0.5mm between faces. For FDM prototyping this is tolerable. For injection molding, where uniform wall thickness is critical to prevent warping and sink marks, it would be a problem. None of the tools seemed to enforce a minimum or target wall thickness consistently, even when specified in the prompt.

Thermal design. My prompt mentioned ventilation slots, but none of the tools considered thermal performance beyond the literal geometry I requested. An enclosure for a Raspberry Pi needs to dissipate heat. A human designer would think about airflow paths, the relationship between intake and exhaust positions, and whether the board's hot components are near the ventilation. The AI places slots where prompted and calls it done.

What I'd actually use AI-generated enclosures for#

After this test, I have a clear picture of where text-to-CAD fits in enclosure design.

Concept-phase enclosures for showing a client or team the general shape and size of a product. "Here's roughly what the thing will look like." Print it, set it on the table next to the PCB, and have a conversation about proportions, orientation, and layout. For this, even the worst tool's output is faster than modeling from scratch, and accuracy doesn't matter because the enclosure is a communication prop, not a functional part.

Quick fit-check enclosures for prototyping. Generate the outer shell, print it, drop the board inside, and see if everything fits spatially. You're checking overall volume and basic keep-out zones, not mounting-hole alignment or connector positions. The 1-2mm dimensional error is tolerable because you're asking "does this board fit in a box this size," not "do the screw holes line up."

Starting-point geometry for real enclosure design. Generate the box shape, import into Fusion 360, and use it as the starting body to add proper standoffs, snap fits, port cutouts, and ventilation. This saves the 10-15 minutes of creating the initial shell geometry manually. Whether that's worth the workflow disruption of generating externally and importing depends on how fast you are in your CAD tool. I'm fast in Fusion, so the savings are marginal. For someone less experienced, they might be more meaningful.

What I wouldn't use it for: any enclosure going to production. Any enclosure that needs to seal against dust or water. Any enclosure with complex internal features like cable channels, EMI shielding, or mechanical interlocks. Any enclosure that will be injection molded. These all require the kind of detailed design work that text-to-CAD doesn't touch.

The enclosure-specific prompt that works best#

After testing various prompt styles, I've found that simple dimensional prompts work better than feature-rich prompts for enclosure generation. The AI handles "rectangular box 100x60x40mm, 2mm walls, removable lid" much better than "rectangular box with four standoffs at 58x49mm spacing, ventilation slots, snap-fit lid, USB-C cutout at X position..."

The more features you add to the prompt, the more likely the AI is to get some of them wrong or ignore others entirely. A simpler prompt gives you a more reliable starting shape that you can add features to in your own CAD tool. This is counterintuitive if you expect the AI to do more work for you, but it reflects the reality of where these tools are in 2026: they're good at boxes and bad at details.

My recommended approach for enclosure prototyping: prompt for the box and wall thickness only. Import STEP into Fusion 360. Add your own standoffs at the correct positions from the PCB drawing. Add your own port cutouts measured from the actual connectors. Add your own ventilation, snap fits, and mounting features. Let the AI give you the shell. Do the engineering yourself.

Compared to templates and libraries#

It's fair to ask whether text-to-CAD is even the right tool for enclosures when parametric enclosure libraries already exist. GrabCAD, Thingiverse, and various paid template libraries have adjustable enclosure designs that you can download and modify. Some CAD tools have built-in enclosure generators with parametric controls.

For standardized rectangular enclosures, those libraries are often faster and more reliable than text-to-CAD. You pick a template, enter your dimensions, adjust the features, and you're done. The standoffs are properly parameterized. The snap fits are proven. The wall thickness is consistent.

Where text-to-CAD has an edge is in non-standard shapes. If your enclosure has an unusual profile, a tapered side, an asymmetric layout, or a form factor that doesn't match any template in the library, generating from a text description gives you more flexibility. The output quality is lower than a well-designed template, but the shape freedom is higher. For product design work where the enclosure shape is part of the brand identity, that flexibility matters.

The honest verdict on AI-generated enclosures#

Text-to-CAD can make a box. It can put a lid on the box. It can put holes in the box. It cannot make an enclosure in the engineering sense of the word, which includes mounting features at correct positions, properly dimensioned port cutouts, functional snap fits, thermal management, EMI compliance, and the hundred other details that separate a box from a product housing.

For concept visualization and early prototyping, the box is enough. For everything beyond that, you're opening Fusion 360 and doing the real work yourself. The AI saved you a shell. You're building the enclosure.

Zoo.dev is the best of the tools I tested for this use case, which tracks with my experience on other text-to-CAD work. It produced the most complete feature set and the cleanest geometry. But "best of three" and "good enough" are different standards, and for enclosure design, none of the tools meet the second one without manual rework. That gap will likely narrow over time as training data improves and tools add enclosure-specific logic. For now, keep your parametric skills current. The snap fit still needs a human who knows what 0.3mm of interference actually feels like in PLA.