Canvas Raises $10M to Accelerate AI-Driven 3D Modeling for Construction
- Editor
- Jan 29
- 2 min read
What's Happening: Canvas, a leader in 3D measurement and modeling solutions for residential and light commercial spaces, has secured $10 million in Series A funding. The round was led by Trilogy Equity Partners and Foundry Group, with participation from other investors. Canvas plans to use the funds to enhance its AI-driven technology for converting mobile LiDAR scans into detailed 3D models for construction and remodeling workflows.
The Key Moves:
Canvas will recruit top researchers and engineers to advance its proprietary AI-driven spatial data processing technology.
The company aims to scale its go-to-market team to expand its reach globally.
Canvas is focusing on making 3D modeling accessible and affordable for professionals in residential and light commercial markets.
By The Numbers :
Canvas has over 15 years of experience and 20+ patents in 3D sensing and computer vision.
The company serves over 12,000 design and construction professionals.
Canvas has modeled over 110 million square feet to date.
Key Quotes:
"Canvas is solving a critical challenge in the design and construction industry: turning raw 3D scans into reliable, construction-grade models quickly and affordably," said Canvas CEO Sarah Bird.
"We're at a pivotal moment in 3D modeling, driven by AI and the growing accessibility of LiDAR in mobile devices," stated Yuval Neeman of Trilogy Equity Partners.
"This investment is a powerful endorsement of our ambitious mission to create a digital twin of every interior, transform it into structured data and power the full lifecycle of each property," added Sarah Bird.
The Bottom Line: Canvas's $10 million Series A funding round positions the company to accelerate its development of AI-driven 3D modeling technology for the construction industry. By making the process of converting LiDAR scans into detailed models more accessible and efficient, Canvas aims to transform how design and construction professionals approach their work, potentially saving significant time and reducing errors in the field.
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