AI for CAD Tools
Best AI for mechanical design that works inside CAD: what SolidWorks, Fusion, Onshape and NX assistants do, how to test them, and where they stop.
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8 min read
Michelle Ben-David
Michelle Ben-David is a mechanical engineer and Technion graduate. She served in an IDF elite technology and intelligence unit, where she developed multidisciplinary systems integrating mechanics, electronics, and advanced algorithms. Her engineering background spans robotics, medical devices, and automotive systems.
BOTTOM LINE
The best AI for mechanical design depends on the question. Guidance assistants such as Aura, Onshape’s AI Advisor, and Design Copilot NX help you work faster inside one CAD system. Action assistants such as Fusion’s can change your model, so they deserve a test on your own parts, with the parametric history checked afterward. Generative tools need the most verification. All of them stop at the edge of the CAD session, so add a retrieval layer across PDM, PLM, and ERP when the real delay is finding what your company already knows.
Every major CAD vendor now ships some form of AI inside the modeling environment, and the labels blur together fast. A chat panel that explains a command, an assistant that builds a feature from a typed request, and a generator that proposes whole geometry are three different tools with three different failure modes. Choosing the best AI for mechanical design starts with knowing which of the three you are actually buying into.
This guide sorts the in-CAD options by what they do, summarizes what four vendors say their assistants cover, gives a short test you can run on your own parts, and then looks at the part of design work that none of them were built to reach: the knowledge that lives outside the CAD session.
Three Kinds of AI That Work Inside CAD
Most products marketed as AI for mechanical design fall into one of three groups, and mixing them up is the fastest way to a disappointing pilot.
Guidance assistants answer questions about the software itself. They are trained on the vendor’s documentation and help you find the right command, understand a feature error, or learn a workflow. They do not change your model.
Action assistants translate a typed request into operations on your model. You describe a fillet, an extrusion, or a toolpath setup, and the assistant performs it inside the parametric history, ideally with a confirmation step before anything is applied.
Generative tools propose new geometry from text, sketches, or constraints. They are the most ambitious category and the one where verification matters most, because a generated shape can look right and still fail on manufacturability or load. Our review of AI generative mechanical design tools covers that group in depth.
A useful rule: the more a tool changes your model, the more evidence you should ask for before trusting it. Guidance assistants are low risk. Action and generative tools deserve a real test.
It also helps to match the category to the bottleneck. If your team loses time because new hires cannot find the right command, a guidance assistant pays for itself quickly. If the delay is repetitive feature creation, such as the same bracket in six sizes, an action assistant is the closer fit. If the delay is early concept exploration, a generative tool is worth a controlled trial. Buying a generative tool to fix a documentation problem, or a guidance chat to fix a part-reuse problem, is how pilots stall.
IN PRACTICE
Leo uses a Large Mechanical Model trained on 1M+ technical sources. It also provides citations, so we don't have to guess whether a material property or tolerance is correct. We see 96% accuracy on technical queries.
- Dorian G., AI Engineer
What Four Vendors Say Their Assistants Do
The summaries below rely on each vendor’s own descriptions, which are claims rather than independent measurements. None of the sources we reviewed publish accuracy figures.
SolidWorks. Aura is available in SolidWorks Connected on the 3DEXPERIENCE platform. A Dassault reseller describes it as handling repetitive work such as generating part variants and guiding users through complex mates, troubleshooting feature errors from model context, and answering questions from SolidWorks documentation. It is a cloud-platform feature, so a desktop-only installation is not the audience. We cover it in more detail in what Aura does in SolidWorks.
Autodesk Fusion. Autodesk describes its Assistant as an active participant in the workflow rather than a question-answering box. It maps natural-language requests to Fusion actions, such as creating dimensioned bodies, fillets and extrusions while preserving parametric history, and it can create manufacturing setups and toolpaths. Autodesk says it confirms changes before applying them. This is the clearest example of an action assistant.
Onshape. PTC’s AI Advisor is a guidance assistant. It answers questions about Onshape using Onshape’s own documentation and learning resources, and its content is refreshed with the platform’s release cycle. Items such as FeatureScript optimization and AI agents are described as roadmap work, not current features. PTC’s other CAD product has a similar story, covered in our piece on what PTC’s built-in AI does inside Creo.
Siemens NX. Siemens added Design Copilot NX, a natural-language interface for learning the software that draws on Siemens learning resources to answer technical questions and best-practice queries. Like Onshape’s advisor, it is primarily guidance.
Read across the four, a pattern appears. Three of them are guidance-first, grounded in the vendor’s own manuals, and the one that acts on the model is the one that adds a confirmation step. That is a sensible design, and it tells you what each vendor is confident about. Questions about their software are the safest territory for an assistant today. Questions about your parts, your standards, and your history are a different territory, and none of the vendor descriptions claim to cover it.
How to Test an In-CAD AI Tool on Your Own Parts
Demo models are chosen to flatter the tool. A short internal test tells you far more, and it takes an afternoon.
Pick five real parts from your recent work, including one with a messy feature tree and one that someone else built.
Ask the assistant to do tasks you already know the answer to, such as adding a standard fillet, explaining a rebuild error, or creating a variant at a new dimension.
Check the parametric history afterward. A result that looks correct but breaks when you change a dimension is a failure.
Ask a question that depends on your company’s own practice, for example which fastener family your team prefers for this housing. Note whether the tool says it cannot know.
Record time saved and errors introduced, not impressions. Two or three numbers are enough to compare tools.
Step four is the one teams skip, and it is the most revealing. It shows exactly where the assistant’s knowledge ends.
A worked example makes the point. Suppose you ask an assistant to add a mounting flange to a housing. A good action assistant will create the flange with a sensible hole pattern and keep the feature tree clean. What it cannot tell you is that your last six housings in this family used a four-bolt pattern with a pilot spigot, or that your supplier stopped stocking the fastener it chose. The geometry was right. The decision was uninformed, and that gap is the subject of the next two sections.
Where AI Inside CAD Stops
Every in-CAD assistant shares the same structural limits, and they are worth naming before you commit budget.
First, scope. An assistant built into one CAD system knows that system’s documentation and your open model. It generally does not know the bill of materials in your ERP, the drawing archive on a network share, or the supplier specification in a PDF. Second, history. It can see the geometry in front of it, but not the reason a tolerance was chosen three years ago or which of two near-identical parts your team standardized on. Third, standards. A number on a drawing is only correct against a specific edition of a specific standard, and a guidance assistant trained on software manuals is not a standards reference.
None of this is a criticism. It is the normal shape of a vendor tool: deep inside one product and thin outside it. Teams that run more than one CAD package feel this most, because each assistant knows only its own product. For a side-by-side look at what each vendor ships, see our comparison of built-in AI across CAD and PLM.
Adding a Retrieval Layer on Top of Your CAD
The practical answer for most teams is two layers. The in-CAD assistant speeds up the modeling step. A cross-system layer shortens the searching step, which is where a large share of an engineer’s week actually goes: finding the previous revision, the existing standard part, the past calculation, the reason behind a change.
Leo is built for that second layer. It is an AI assistant for mechanical engineers, trained on more than one million pages of standards, books, and articles, and it connects to an organization’s knowledge base across PDM, PLM, local and network directories, and ERP. Leo offers integrations with leading PDM and PLM platforms, including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, Arena PLM, and others. It sits on top of the systems you already run rather than replacing them, so it works the same way whichever CAD package is underneath. Every answer carries a citation you can click and check. For how that layer connects to your data, see our guide to how AI CAD tools integrate with PLM and ERP systems.
Leo is SOC-2 certified and GDPR compliant, no AI is trained on customer data, and your IP stays protected. That matters when the layer reads past designs and supplier files.
A good way to judge any retrieval layer is the same test from earlier: bring a real question your team has answered before, such as why a tolerance was loosened or which standard part already covers a need, and see whether the answer arrives with a source you can open. If it does, the layer is doing its job. If it produces a confident answer with no citation, treat it like an unreviewed drawing.
FAQ
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