Generative Design

AI-driven generative design automates creation and optimization of innovative, efficient design alternatives.

Business impact

• Design cycle time — Shortens time needed to generate and evaluate multiple design options
• Cost reduction — Lowers material and prototyping costs through optimized designs
• Product quality — Enhances structural integrity and performance of final products
• Sustainability metrics — Reduces material waste and embodied carbon in designs
• Innovation rate — Enables exploration of novel design spaces beyond human intuition

Data requirements

• CAD models (Code) — Provide geometric and parametric input data for design exploration
• Simulation data (CAE, CFD, FEA) (Numeric) — Enable performance evaluation and optimization of design variants
• Material properties databases (Structured) — Inform constraints and feasibility of design options
• Environmental and climate data (Numeric) — Support sustainability and energy efficiency optimization
• User feedback and preferences (Text) — Guide design goals and constraints for human-centered outcomes

AI methods and techniques

• Agentic AI — Autonomously generates and iteratively improves design options based on constraints
• Predictive AI — Predicts performance metrics rapidly to guide design optimization
• Generative AI — Creates novel design geometries and configurations beyond human intuition

AI models and model families

GPT-4o, Claude, Llama, Custom deep learning surrogate models for simulation

Industries

Real-world evidence

26 documented case studies on record.

Companies using this: ABB, Airbus SE, Autodesk, BIG Bjarke Ingels Group, Baytree, Bosco Verticale, Buildrz, Daiwa House Industry, Diabatix, Eastgate Centre, Evogene, General Motors, Gensler, Google, New Balance and 9 more.