Genia, a Los Angeles-based startup, has secured $3 million in funding to support the development of its AI-driven platform, the Genia Structural CoPilot. The solution, designed for the construction industry, enables engineers to validate structural designs significantly faster than traditional methods, potentially reducing design time by a factor of ten.
Structural engineering is a resource-intensive process, often requiring months of manual calculations and large teams of engineers. Genia aims to streamline this workflow by leveraging AI to automate physics-validated design generation, reducing the time and effort required for complex projects.
Industry Challenges Driving Innovation
The construction industry faces growing pressures from multiple factors, including natural disasters, sustainability requirements, and rising interest rates. Events such as the Palisade fires in Los Angeles can overwhelm local engineering contractors, making it difficult to meet urgent demand for new projects. Additionally, adaptive reuse—repurposing existing structures—requires extensive structural assessments, adding further complexity to an already challenging industry landscape.
Traditional structural design methods rely on manual calculations, often performed using spreadsheets or even pen and paper. Designing a high-rise building can take over a year and involve extensive human effort, with planning permissions frequently delayed due to errors in calculations. Homeowners and developers often experience long wait times as a result.
AI-Powered Structural Design with Genia Structural CoPilot
Genia was co-founded by Zhihao Zhao, a former Amazon engineer; Robin Li, a former structural engineer at Arup; software entrepreneur Houtao Wang; and generative AI researcher Peter Dai. The company’s AI model enables engineers to upload PDFs or AutoCAD files, which the Genia Structural CoPilot then analyzes to generate up to five structurally sound design options. Each option is optimized for cost, feasibility, stability, and sustainability.
Unlike conventional AI models that generate architectural concepts without structural validation, Genia’s platform conducts physics-based calculations to ensure the feasibility of each design. The AI provides actionable design outputs, including detailed material specifications, with the potential to reduce material use by up to 20%.
To train its AI, Genia employs a combination of open-source data, client designs, and internally simulated structural models. The company has also partnered with major building material suppliers, including Weyerhaeuser’s ForteWeb and Simpson Strong-Tie, to validate its materials database.
Industry Adoption and Future Prospects
Genia has already secured a partnership with Suffolk Construction, one of North America’s leading contractors. Suffolk plans to use Genia’s technology for adaptive reuse projects, converting old buildings into residential or commercial spaces while maintaining structural integrity and reducing environmental impact.
“Genia integrates AI with code-based checks to ensure structural designs are both AI-generated and compliant with regulations. Unlike many AI solutions that serve as conceptual tools, Genia’s approach is practical and industry-focused,” said Dr. Murat Melek SE, Design AI Director at Suffolk Construction.
The $3 million seed funding round was led by venture capital firm Pi Labs, with participation from Amplify, Boost VC, Dorm Room Fund, Suffolk Technologies, Y Startup Index, Moatable founder Joseph Chen, and executives from Scale AI. The investment will support further development and market expansion of Genia’s AI-driven structural design platform.
If you are a proptech company and want to promote your products for Free, go to proptechbuzz.com and submit your products. For investors or proptech buyers, sign up on our platform to stay informed about exciting updates and trends in the Proptech Ecosystem.
Explore more Proptech news at proptechbuzz.com/news, for news tips and promotions, reach out to marketing@proptechbuzz.com
By Proptechbuzz
By Ravi Kumar
