Acube – Governing AI from the Mathematical Core

Acube – expertise areas

Acube is a research-driven AI company specialised in the design, governance and deployment of mathematically grounded machine learning systems. Our core expertise lies in controlling the mathematical layer of AI models, enabling the development of reliable, explainable and generalisable solutions for science-driven and industrial contexts.

We operate at the intersection of advanced mathematics, statistical learning theory, optimisation, and large-scale ML engineering, with strong competencies in multimodal AI, causal and decision intelligence, uncertainty quantification, and trustworthy AI.

Acube’s research activities span foundational and applied domains, including probabilistic and Bayesian ML, geometric and topological learning, causal inference and counterfactual modelling, representation learning, foundation models, reinforcement and multi-agent systems. These capabilities are complemented by strong expertise in ML systems, MLOps, data governance, and AI assurance, enabling end-to-end reproducibility, robustness, and compliance with EU standards and regulations.

Our methods are cross-domain by design and transferable across sectors addressed by Horizon Europe Cluster 4, including digital technologies, advanced manufacturing, materials, data spaces, AI systems, and emerging computing paradigms. This transversal approach allows Acube to contribute scientific value independently of the specific application domain.

What Acube can offer

Acube can actively contribute to all phases of the scientific and innovation lifecycle:

• Scientific foundations and method development: formulation of mathematically sound models, design of novel AI architectures, uncertainty-aware and explainable learning, causal and decision-oriented models, simulation and synthetic data generation

• Data and model engineering: multimodal data integration, data-centric ML, knowledge-informed learning, interoperability through standards, ontologies and APIs, and privacy-preserving or federated pipelines

• Validation, evaluation and assurance: calibration, robustness to distribution shifts, fairness and safety metrics, continuous benchmarking, auditability and alignment with AI Act and ISO/IEC requirements

• Scalable implementation: production-grade MLOps, versioned datasets and models, cloud/HPC-ready training, continuous monitoring, and secure deployment

• Impact and exploitation: support to demonstrators, pilots and validation activities, contribution to exploitation strategies, technology transfer, and alignment with EU open science and standardization efforts.

By combining deep algorithmic control with engineering at scale, Acube transforms frontier AI research into validated prototypes and deployable solutions. This positions Acube as a strategic partner for consortia seeking high scientific credibility, reduced technical risk, and a clear pathway from research to impact within Horizon Europe projects.

https://acube.ai/en/

fabrizio.coccetti@acube.ai