Valgo, a San Francisco–based startup building the risk quantification layer for insurance of physical AI systems, has raised early-stage funding to expand its simulation-driven platform for pricing autonomy risk. The company, founded in 2025, is part of the Y Combinator Winter 2026 cohort and is developing probabilistic models that help insurers evaluate and underwrite emerging risks in robotics, autonomous vehicles, and other safety-critical AI systems.

The funding is primarily backed by Y Combinator, which provides seed capital alongside its accelerator program, technical mentorship, and access to a global network of enterprise customers and insurance partners. Valgo’s participation in the Winter 2026 batch places it within a cohort of startups focused on infrastructure for artificial intelligence, particularly in domains where real-world deployment risks are difficult to quantify using traditional historical data.

Valgo’s core product addresses a fundamental gap in insurance markets for autonomous systems: the lack of historical claims data. Unlike conventional auto or property insurance, where decades of incident data can be used to model risk, autonomy-enabled systems such as delivery robots, self-driving vehicles, and industrial AI agents have very limited operational history. This makes it difficult for insurers to accurately price coverage or estimate tail risk.

To solve this problem, Valgo builds bottom-up probabilistic simulations of routes, tasks, environments, and system behaviors. These simulations are used to estimate potential failure scenarios and generate loss distributions that insurers can use for pricing and underwriting. Rather than relying on historical claims, the platform focuses on discovering rare and high-impact failure events through computational modeling and stress testing.

The company was founded by Robert Moss, Sydney Katz, and Jon Qian. Moss and Katz are Stanford PhDs with deep research backgrounds in safety-critical systems, including work on validating autonomous systems and formal safety analysis. Jon Qian brings extensive experience in insurance and actuarial science, having worked in senior leadership roles managing large-scale insurance operations and mergers and acquisitions. Together, the founding team combines expertise in autonomy safety engineering and insurance market structure.

Valgo’s platform is designed to serve insurers, reinsurers, and brokers that are increasingly exposed to emerging risks from physical AI systems. The company models not only system behavior but also environmental variability and operational conditions, aiming to produce more realistic loss estimates than traditional actuarial methods can provide for autonomous technologies.

In addition to its insurance-focused applications, Valgo’s technology is also relevant to robotics and autonomy developers who need to understand the risk profiles of their systems before deployment. By simulating rare failure modes and edge-case scenarios, the platform helps companies identify safety gaps earlier in the development lifecycle, potentially reducing both operational risk and insurance costs.

The funding secured through Y Combinator will be used to expand Valgo’s engineering team, improve its simulation infrastructure, and develop partnerships with insurers and autonomy companies. The company is also focused on refining its probabilistic modeling techniques to better capture long-tail risk distributions that are critical for underwriting high-uncertainty systems.

As the adoption of physical AI accelerates across industries, from autonomous logistics to industrial robotics, Valgo is positioning itself as a foundational infrastructure provider for risk assessment and insurance pricing. With its early-stage funding and accelerator backing, the company is working to bridge the gap between rapidly evolving autonomous technologies and the insurance frameworks needed to support their safe and scalable deployment.

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