Accudire, a Verona-based start-up, will present QP-ERM (Quantum-Proof Enterprise Risk Management) at CES in Las Vegas. This technological innovation is dedicated to advanced risk management in hyper-complex and highly uncertain contexts involving international trade, freight transport and passenger travel. Accudire’s innovation is a state-of-the-art decision support system capable of comparing possible futures and alternative scenarios and suggesting optimal choices in unstable environments.
The development of QP-ERM began as a three-year project conceived by the start-up team, whose founding partners are the current CEO Abramo Vincenzi (pictured) and the Ormesani company, while the seed phase investors are the Zucchetti Group and Circle Group, joined by Filippo Fernè, who also took on the role of president of Accudire. At the end of December 2025, a new €1 million investment round was also closed with the entry of a new group of partners represented by business angels, money that will be used to continue the innovation work on the ExAC platform, including the three-year project featured at CES.
The University of Verona is also collaborating on the project, under the guidance of Alessandra Di Pierro from the Department of Computer Science, one of the leading international experts in quantum machine learning. Academic collaboration is a strategic pillar of Accudire’s innovation and aims to consolidate, validate and evolve the platform’s quantum architecture over time, strengthening the link between advanced scientific research and immediate industrial applications.
The goal is to offer organisations and end consumers concrete tools to operate and make decisions in a world characterised by systemic shocks, climate change, geopolitical uncertainty and non-linear risk propagation, transforming the management of uncertainty into a competitive advantage. QP-ERM was created to enhance traditional AI algorithms, overcoming their current limitations, often linked to computational models that look exclusively at historical data and its linearity, to offer a predictive and adaptive vision of possible futures.
The innovation introduced with QP-ERM also generates direct and tangible benefits for end consumers. In an increasingly unstable global context, the ability to anticipate and manage risk along supply chains translates first and foremost into greater product availability. Consider, for example, the availability of medicines, even during geopolitical, climatic or logistical crises, reducing sudden shortages and empty shelves.
QP-ERM also helps to ensure fresher, safer and higher quality products through predictive monitoring of transport conditions and reduction of critical disruptions, such as cold chain breaks in food and pharmaceuticals. This means less waste, greater food and health safety, and a better shopping experience.
The platform also enables a new level of transparency and trust: through verifiable data on the origin, journey and environmental and ethical impact of products, consumers can make more informed choices that align with their values, distinguishing between truly sustainable supply chains and mere marketing claims.
Finally, more efficient risk management along the supply chain contributes to more stable prices, limiting hidden costs associated with logistical emergencies and inefficiency. In this way, upstream uncertainty does not automatically translate into penalties for purchasers, but is absorbed and managed before reaching the consumer.
“We are thrilled,” Vincenzi said in a statement, “to be protagonists at CES 2026 with QP-ERM, the quantum engine that allows us to predict and compare possible futures, suggesting the optimal path when everything is fragile, non-linear and unpredictable. In a BANI world, which stands for brittle, anxious, non-linear, incomprehensible, AI that only looks to the past is not enough. QP-ERM combines quantum machine learning and advanced AI to model uncertainty even when scenarios change rapidly, while blockchain makes every piece of data and every choice traceable and verifiable. The result is less risk, faster decisions and operational confidence, even in chaos.” .
Traditional AI algorithms are effective when operating in stable and repetitive contexts, because they learn from historical data and recognise future paths from scenarios already observed. However, when the context changes rapidly, as happens in the presence of sudden and changing geopolitical shocks, extreme weather events or systemic crises, these models tend to lose accuracy because the future no longer resembles the past. The quantum approach to forecasting was created precisely to overcome this limitation.
In support of the current approach to AI, which works primarily by retrospective extrapolation, quantum models allow us to explore possible alternative futures simultaneously, even in the absence of direct historical precedents. In other words, they do not merely learn from what has already happened, but help to model what might happen.
This makes predictive data more robust in contexts of high uncertainty, because it does not depend on a single historical trajectory, takes into account complex and non-linear interactions, and above all remains reliable even when new or unexpected events arise. The result is not a certain prediction in the traditional sense, but greater decision-making certainty: decisions made with greater awareness, verifiable scenarios and impacts, even when the system is fragile, non-linear and unpredictable. QP-ERM is fully integrated into the ExAc platform, already used on the market for digital shipping and electronic transport document management (e-CMR and e-DDT).
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