Gen AI in Designing - Transforming 3D Workflows for Better Result in Product Design
Overview - Reimagining 3D Design using Artificial Intelligence
A product design business was under increasing pressure to create complicated designs in shorter periods of time. Their teams spent hours on manual activities, which hampered projects and resulted in burnout. Design approvals required many days since each model had to be thoroughly evaluated by top specialists.
When they contacted us, we presented a one-of-a-kind Gen AI solution tailored to their Autodesk Maya environment. This brilliant invention utilized their greatest ideas to automate time-consuming procedures. It also spotted flaws in 3D models immediately, rather than weeks later, when repairs are more difficult. This allowed designers to concentrate more on the creative aspects of their jobs rather than the boring tasks.
Manufacturing & Product Design
AI-Enabled 3D Design Automation
Our Process: Building Intelligence into Design Workflows
We followed a clear, step-by-step approach to create a solution that truly fits the client’s needs. Each stage built on the previous one to create a system that works in the real world.
Discovery Phase
We began with deep dive seminars to learn about the client’s 3D modeling workflow, pain spots, and quality standards. Our team mapped current processes, identified automation potential, and collected samples of successful and problematic designs to train our AI.
Model Development
We built a dataset using the client’s past design files. Then, we developed a custom AI algorithm to spot design trends and quality issues. The system was trained to understand top design ideas in their industry. We fine-tuned the model over multiple rounds, using feedback from the design team.
Autodesk Integration
Before full deployment, we ran extensive tests on past designs. The design team carried out user acceptance testing. We measured performance by time and quality. Real-world feedback helped us refine the method further.
Testing & Optimization
Before full deployment, we ran extensive tests on past designs. The design team carried out user acceptance testing. We measured performance by time and quality. Real-world feedback helped us refine the method further.
