By Frédéric Dumollard, former field inspector, then general agent and insurance broker. After years spent at the heart of underwriting corporate liability risks, he is now co-founder of NeoMundi and brings his expert perspective on the new insurance challenges posed by generative AI. DOI: 10.5281/zenodo.21222308 Why a former underwriter is taking up this subject I […]
NeoMundi is pleased to announce its acceptance into the NVIDIA Inception program. An Already Operational Runtime Governance Infrastructure NeoMundi joins NVIDIA Inception to accelerate the development of ControlTower, our infrastructure for measurement, auditability, and real-time governance of AI systems. As organizations massively deploy AI in production, the key question is no longer whether a model
In the framework of the NeoMundi AI Observatory’s work, we are publishing today a contribution by James Moore, founder of Nova Jema AI Systems and specialist in the governance of artificial intelligence systems under real-world execution conditions. He kindly shared with us a precise reflection on a frequently overlooked subject: once we measure an AI’s
Scientific Contributor / Data Scientist – NeoMundi AI Observatory Most discussions on AI governance start too late. We believe measurement must come first. We have developed ControlTower, a diagnostic, piloting, and traceability instrument designed to continuously observe the risk of AI responses in real-world conditions. ControlTower enables standardized and reproducible measurement campaigns, allows comparison of
Independent Audit Report by Pape Malick DIOP, Data Scientist and Machine Learning Researcher, June 10, 2026. At NeoMundi, we believe that the governance of generative systems cannot rely on a single signal. This is why we commissioned an independent external exploratory audit of our runtime signals: G-Score, ∆G, FLAG, regime, and benchmark accuracy. Pape Malick
NeoMundi Recherche publishes a public, sanitized version of an independent methodological review of its real-time approach to measuring the stability of generative AI. The review is based on a May 2026 comparative cohort of eight anonymized providers. It examines what a real-time signal can and cannot capture, documents the current limits of the method, and
