Valgo
Valgo provides a risk quantification platform that transforms high‑fidelity simulation data from autonomous and cyber‑physical systems into calibrated loss estimates for insurers. Using diffusion‑based failure sampling, Bayesian probability estimation, and reachability analysis, the platform delivers statistically sound failure probabilities and loss distributions via an API and dashboard, enabling data‑driven underwriting and compliance validation for physical AI technologies.
- Artificial Intelligence
- Autonomous Vehicles
- Insurance Technology
- Robotics
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Insurers lack historical claims data for autonomous and cyber‑physical systems, making it difficult to price coverage and assess risk for these emerging technologies.
Solution
Valgo offers a risk quantification platform that converts high‑fidelity simulation outputs into actionable loss estimates for physical AI systems. The platform applies advanced safety‑validation algorithms—including diffusion‑based failure sampling, Bayesian probability estimation, and reachability analysis—to generate statistically sound failure probabilities and loss distributions. These estimates are delivered through an API and dashboard that insurers can integrate into underwriting workflows, enabling data‑driven pricing of coverage for autonomous vehicles, robotics, and other AI‑driven hardware. Valgo also provides validation tooling that helps manufacturers verify safety claims against regulatory standards, reducing uncertainty for both producers and insurers.
Target Audience
Primary customers are insurance underwriters and risk managers covering autonomous vehicles, robotics, and other physical AI systems, as well as manufacturers seeking validated safety metrics for regulatory and market purposes.
Features
- Simulation‑to‑loss conversion engine that produces calibrated loss estimates from virtual test data
- Diffusion‑based failure scenario generation for rare but critical operating conditions
- Bayesian safety validation methods for black‑box systems to estimate failure probabilities
- Reachability analysis and explainability modules to identify failure modes and root causes
- API and web dashboard for seamless integration with underwriting and risk‑management systems
- Compliance‑focused validation toolkit supporting industry safety standards