By linking CAD geometry, PLM metadata, and engineering rules through AI, Dessia enables fully connected, traceable, and automated design workflows at scale—improving speed, quality, and compliance across engineering teams.
Most design engineering teams today operate in highly digital environments. Designs are authored in parametric CAD software. Lifecycle data is stored in structured PLM systems. Internal rules, often critical for compliance, manufacturability, or safety, exist in scattered formats: Excel, SharePoint, or the minds of senior engineers.
Yet for all this tooling, critical connections are missing.
This is not a tooling issue. It’s a knowledge flow issue. The systems that power modern engineering are fundamentally disconnected. And that disconnect is costing engineering organizations time, money, and confidence.
AI, when applied correctly, doesn’t replace those systems. It connects them, with logic, with context, and with memory.
The fragmentation between CAD, PLM, and engineering logic isn’t just annoying, it’s architectural. These systems were never designed to share semantics:
These domains speak different languages. Bridging them requires more than APIs, it requires a semantic model that understands geometry, context, and rules simultaneously.
That’s not a user interface problem. It’s a systems engineering challenge, and one that AI is uniquely positioned to solve.
This is not the AI of text prompts or speculative generative design. Dessia’s AI is built specifically for design engineering—not to replace engineers, but to augment their decision-making through contextual, explainable, and geometry-aware intelligence. At its core lies a powerful system of AI libraries tailored to the realities of product design, validation, and reuse.
Unlike off-the-shelf solutions, these libraries are developed in-house and built around the specific engineering needs of each use case—adapting to your rules, geometry, and logic, not the other way around.
1. Interpreting CAD geometry structurally with purpose-built geometry libraries
Traditional approaches often rely on visual pattern recognition. Dessia’s geometry AI libraries go deeper: they structurally interpret CAD models by analyzing constraints, topologies, feature hierarchies, and design relationships. These libraries are built to identify not just what a shape is, but why it exists—distinguishing, for instance, between mounting and ventilation holes based on contextual features, connection rules, and spatial logic. Geometry is read through function and intent, not visual form.
2. Understanding PLM context with dynamic metadata linkage libraries
CAD geometry doesn’t live in isolation. Dessia’s libraries bridge engineering data across PLM systems, automatically parsing and linking metadata such as part configurations, supplier constraints, material specs, and version histories. These AI components maintain contextual continuity, enabling the platform to track how a single part evolves across variants, programs, and teams. The result is an AI that understands where a part comes from, where it’s going, and what rules it must follow.
3. Encoding engineering rules through logic graph libraries and constraint engines
Engineering knowledge often resides in static documents, tribal memory, or Excel sheets. Dessia’s rule libraries formalize this knowledge into structured, computable networks, built using logic graphs, constraint solvers, and object-oriented rule systems. These rules are directly linked to CAD features and lifecycle data, enabling real-time validation, rule chaining, and explainable decisions. What was once a manual checklist becomes an automated, always-on reasoning engine.
4. Linking all three to build a live, self-aware engineering memory
What sets Dessia apart is the seamless integration of these AI libraries into a unified system. Geometry interpretation, metadata understanding, and rule logic aren’t separate tasks—they’re continuously cross-referenced and synchronized. A geometry edit can trigger rule re-evaluation and lifecycle impact analysis. Reuse of a part across programs is automatically checked against variant-specific rules. Engineers are no longer operating blind—they’re supported by a system that understands, remembers, and reasons.
A common objection to AI in engineering is the fear of automation displacing expertise. In reality, AI is built on the principle of human-in-the-loop design, it complements engineers, rather than controlling them.
1. Designers retain authority
AI surfaces violations, suggestions, and alternatives, but the decision to accept, override, or escalate remains with the design engineer. This maintains ownership while providing richer information to support judgment.
2. Decision rationale is transparent
Every rule, check, or suggestion comes with traceability: the logic used, the source of the rule, and the contextual factors that triggered it. Engineers are never “blind-sided” by AI logic—they see exactly how a decision was reached.
3. Low-value tasks are eliminated
Manual rule-checking, searching for similar designs, verifying documentation alignment, these are critical, but time-consuming. AI offloads them, enabling engineers to focus on creative problem-solving, systems-level thinking, and innovation.
This is not automation in the traditional sense. It’s cognitive support infrastructure, built around the realities of design complexity and compliance risk.
Incorporating AI into engineering design workflows must reinforce, not replace, engineering authority. The purpose of AI in this context is to support cognitive offloading of routine validation and rule-checking, not to interfere with technical decisions.
The AI does not generate designs. It does not approve or reject them. It provides structured insights, rule validations, and system-level consistency checks that assist the engineer, while leaving final decisions and overrides fully in their hands.
Each suggestion, flag, or violation comes with traceable logic. design engineers can trace every rule to its definition, understand the context of the violation, and accept, modify, or reject recommendations with full transparency. This ensures trust and preserves accountability, even in highly regulated environments.
By removing repetitive work and ensuring data consistency across CAD and PLM, AI gives engineers more time and confidence to focus on higher-order design decisions: functional performance, system architecture, innovation. It’s not about less engineering, it’s about better engineering at scale.
For engineering leaders, the integration of AI as a connective tissue across CAD, PLM, and design rules represents a strategic enabler, not a tooling upgrade.
With synchronous rule enforcement and reduced manual review overhead, engineering timelines compress, not by forcing speed, but by removing friction.
When the rules, geometry, and metadata are aligned, errors reduce at the source. First-pass design quality improves, and downstream change cycles are minimized.
Rules are no longer something to “remember” they’re embedded and enforced. Whether for internal standards or external regulations, compliance becomes a live system property, not a review phase.
As rule logic and validation pathways are digitized, engineering organizations become less dependent on individual memory and more resilient to turnover or reorganization.
Most importantly, this transformation requires no platform replacement. AI integrates through APIs and data pipelines, augmenting existing PLM and CAD environments rather than competing with them.
This is not process reengineering, it’s process reinforcement. A digital infrastructure that finally synchronizes the systems engineers already rely on.
Published on
01.08.2025
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