Inside Dessia’s AI Libraries: Design engineering logic meets machine intelligence

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.

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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.

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From black box to engineering-grade intelligence

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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:

• Statistical Machine Learning, for pattern recognition, classification, and similarity detection.
• Symbolic Reasoning and Logic Programming, for rule enforcement, constraint solving, and explainable decision-making.
• Structured Knowledge Representation, for modeling engineering semantics, metadata relationships, and part behavior.

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This triad allows Dessia’s AI-powered platform to go beyond automation — it enables reasoning, validation, and reuse across design cycles.

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Geometry AI-libraries: Structural understanding of 3D models

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Instead of analyzing shapes visually, Dessia reads native CAD structure — parametric features, constraints, operations — to understand geometry functionally.

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For example: A cut is interpreted based on its size, position, and surrounding geometry, which can reveal functional roles such as mounting points.

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This interpretation enables:

• Smarter rule application
• Precise part reuse (based on function , not just shape)
• Constraint validation embedded in the design process

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Mathematical graphs : Metadata that thinks in context

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Dessia creates a live design graph where parts, rules, specs, and revisions are linked in a computable model.

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Example:

• A battery module is not just a part — it’s a node with connections to:
  • Material specs
  • Spatial placement
  • Program-specific constraints
  • Rule sets for mounting or cooling

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Our symbolic engine can:

• Trace downstream impact of a geometry edit
• Validate compliance of a design variant
• Suggest reusable modules based on specs and topology

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This means engineering logic becomes navigable, queryable, and scalable.

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Rule libraries: Engineering constraints made executable

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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.

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Example rules Dessia can automate:

• “All bolt holes must include washers”
• “Maintain 15mm spacing between power and signal routes”

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These are encoded using symbolic logic and linked to both geometry and metadata. Result:

• Fast rule validation in CAD
• Traceability for every decision
• Audit-ready logic for compliance-heavy environments

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Routing libraries: Intelligent pathfinding

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Routing is one of the most complex design tasks — not just about clearance, but about system behavior.

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Dessia’s routing libraries turn assemblies into topological graphs, enriched with symbolic constraints and statistical routing patterns.

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Use cases:

• Routing an electrical harness between fixed points while avoiding sharp bends and interference zones
• Generating pipes and ducts paths that obey slope rules and stay within protected envelopes
• Updating routes automatically when anchor points shift in a variant model

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These libraries combine:

• Graph theory
• Constraint logic (minimum bend radius, spacing, system zoning)
• Learned patterns from prior validated routes

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The result: fast, constraint-aware routing — built into the design process, not bolted on after.

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Statistical models for reuse and design intelligence

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Dessia also uses statistical learning to identify opportunities for reuse across teams, programs, or variants.

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Examples:

• Detecting functionally similar brackets used across product lines
• Finding reusable mounting strategies from prior subsystem designs
• Grouping parametric variants to reduce part proliferation

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Using part embeddings, similarity models, and historical routing data, the system turns what used to be tribal memory into a computable design memory.

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Faster design. Fewer errors. More intelligence

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For design engineering teams, Dessia’s AI libraries bring immediate benefits:

• Validation; no more final-stage rule checks
• Traceability; every decision is explainable
• Reuse; smarter part and knowledge reuse reduces design duplication
• Scalability; easier onboarding, variant creation, and cross-team alignment

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For directors and innovation leads, this means:

• Shorter release cycles
• Reduced quality risks
• Lower engineering costs  through automation
• Less reliance on expert memory
• Higher ROI

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Conclusion: From data storage to design engineering reasoning

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You already have the data and knowledge — models, specs, and rules.

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Dessia activates it.

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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.

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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.

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Dessia builds that logic.

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