Not all AI is built for engineers. Dessia’s libraries go beyond automation, weaving logic, rules, and structure into design data. The result? Intelligence you can trust — hidden inside your CAD, waiting to be unlocked.
In design engineering, automation isn’t just about speed — it’s about structure, logic, and traceability. At Dessia, we’ve developed a suite of AI libraries purpose-built for engineering, designed not to generate speculative geometry, but to interpret, validate, and augment real design work intelligently.
Unlike generic generative AI models, our libraries combine statistical machine learning, structured knowledge representation in mathematical graphs, and symbolic reasoning. The result: systems that understand geometry functionally, apply engineering rules contextually, and operate across CAD, PLM, and documentation layers with explainability and intent.
Many AI tools in the design space rely on large-scale deep learning — powerful, but often opaque. In contrast, design workflows demand predictability, explainability, and domain logic. That’s why Dessia’s AI libraries are built with a hybrid approach that combines:
This triad allows Dessia’s AI-powered platform to go beyond automation — it enables reasoning, validation, and reuse across design cycles.
Instead of analyzing shapes visually, Dessia reads native CAD structure — parametric features, constraints, operations — to understand geometry functionally.
For example: A cut is interpreted based on its size, position, and surrounding geometry, which can reveal functional roles such as mounting points.
This interpretation enables:
Dessia creates a live design graph where parts, rules, specs, and revisions are linked in a computable model.
Example:
Our symbolic engine can:
This means engineering logic becomes navigable, queryable, and scalable.
Most design teams rely on Excel sheets, internal wikis, or PDF specs to enforce rules. Dessia formalizes these rules into constraint networks and logic graphs.
Example rules Dessia can automate:
These are encoded using symbolic logic and linked to both geometry and metadata. Result:
Routing is one of the most complex design tasks — not just about clearance, but about system behavior.
Dessia’s routing libraries turn assemblies into topological graphs, enriched with symbolic constraints and statistical routing patterns.
Use cases:
These libraries combine:
The result: fast, constraint-aware routing — built into the design process, not bolted on after.
Dessia also uses statistical learning to identify opportunities for reuse across teams, programs, or variants.
Examples:
Using part embeddings, similarity models, and historical routing data, the system turns what used to be tribal memory into a computable design memory.
For design engineering teams, Dessia’s AI libraries bring immediate benefits:
For directors and innovation leads, this means:
You already have the data and knowledge — models, specs, and rules.
Dessia activates it.
By combining machine learning, symbolic reasoning, and structured engineering logic, our AI libraries turn disconnected data into a live design intelligence system — one that design engineers trust, that scales, and that delivers real operational value.
If your team is still redrawing what already exists, validating rules by hand, or guessing at compliance — you're not lacking tools. You're missing the logic between them.
Dessia builds that logic.
Published on
01.10.2025
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