Topological

Topological is developing physics-based foundation models for CAD optimization. We help hardware teams iterate at the same speed that software teams do. Our technology is accelerating the engineering workflow with AI and scales design and optimization to identify the ideal designs for complex problems given their physical constraints with enhanced speed and performance.

Our first model, UToP-v1, is a SOTA topology optimization model that understands physics, geometry, and manufacturability. It can generate the most efficient design given a problem’s physical requirements. It has <5% compliance error and is 1930x faster than current methods. We're reimagining mechanical engineering and computational design with precision spatial AI.

High-Level Overview

Topological is a startup developing physics-based foundation models for CAD (Computer-Aided Design) optimization, enabling hardware engineering teams to iterate designs as rapidly as software teams do. Their flagship product, UToP-v1, is a state-of-the-art topology optimization model that integrates physics, geometry, and manufacturability to generate highly efficient designs with less than 5% compliance error and operates nearly 2000 times faster than traditional methods. This accelerates engineering workflows by scaling design and optimization processes, helping solve complex physical problems with enhanced speed and precision. For an investment firm, Topological represents a cutting-edge AI-driven innovation in hardware design tools, targeting sectors like robotics, 3D printing, and mechanical engineering, and pushing forward the integration of AI in physical product development. For a portfolio company, it builds advanced CAD optimization software serving hardware engineers and product designers, solving the problem of slow, manual, and computationally intensive design iterations, with strong growth potential driven by its breakthrough speed and accuracy[1][2][5].

Origin Story

Topological was founded in 2025 by Nicole Ma (CEO) and Zeyneb Kaya (CTO), both Stanford graduates with backgrounds in computer science, mechanical engineering, physics, and AI research. Nicole previously worked on physics simulations at NASA, while Zeyneb has expertise in reinforcement learning, pretraining, and efficient algorithms. The idea emerged from the need to bridge the gap between slow hardware design cycles and fast software iterations by leveraging physics-based AI models to automate and accelerate topology optimization in CAD workflows. Early traction includes acceptance into Y Combinator’s Summer 2025 batch and the development of their first model, UToP-v1, which has demonstrated significant performance improvements over existing methods[1][2][5].

Core Differentiators

• Physics-based foundation models: Unlike traditional topology optimization tools that rely heavily on manual input and finite element analysis, Topological’s models inherently understand physics, geometry, and manufacturability constraints.
• Speed and accuracy: UToP-v1 achieves <5% compliance error and is approximately 1930x faster than conventional optimization methods, enabling near real-time design iteration.
• Scalability: The technology scales to complex design problems with diverse physical constraints, supporting rapid exploration of design spaces.
• AI-driven automation: Uses reinforcement learning and spatial AI to automate the generation of optimized CAD models, reducing manual labor and design bottlenecks.
• Developer and user experience: Designed to integrate seamlessly into existing engineering workflows, helping hardware teams iterate at software-like speeds.
• Strong academic and industry pedigree: Founders’ backgrounds in top-tier institutions and NASA research lend credibility and technical depth[1][2][5].

Role in the Broader Tech Landscape

Topological rides the wave of AI-driven automation in hardware design, a critical trend as industries push for faster innovation cycles and more efficient product development. The timing is favorable due to increasing demand for lightweight, high-performance components in robotics, aerospace, and additive manufacturing, where topology optimization is essential but traditionally slow and manual. By dramatically accelerating CAD optimization, Topological helps hardware teams keep pace with software development speeds, reducing time-to-market and enabling more iterative, data-driven engineering. This contributes to the broader ecosystem by lowering barriers to advanced design optimization, fostering innovation in mechanical engineering, and potentially transforming how physical products are conceptualized and manufactured[1][2][4][6].

Quick Take & Future Outlook

Looking ahead, Topological is poised to expand its foundation models to cover broader classes of physical problems and integrate deeper into CAD and simulation software ecosystems. Trends such as increased adoption of AI in engineering, growth in robotics and 3D printing, and demand for sustainable, optimized designs will shape its trajectory. Its influence may evolve from a niche optimization tool to a core platform underpinning next-generation mechanical design workflows, enabling hardware teams to innovate with unprecedented speed and precision. For investors and partners, Topological represents a compelling opportunity at the intersection of AI, physics, and hardware design, with potential to redefine engineering productivity and product innovation[1][2][4][5].