Nereva obtains Jeune Entreprise Innovante (JEI) status for its French subsidiary

The Nice-based subsidiary of Swiss group Nereva has been granted Jeune Entreprise Innovante (JEI) status for the development of its artificial intelligence dedicated to construction logistics. Nereva strengthens its positioning in research and development with the granting of Jeune Entreprise Innovante (JEI) status to its French subsidiary based in Nice, following a formal tax ruling procedure with the French tax authorities. This status, governed by French regulations, recognizes the eligibility of companies engaged in research and development activities that meet specific criteria, particularly in terms of R&D intensity and the nature of the work carried out. It is part of a broader framework designed to support companies developing new technological approaches.

An operational platform at the core of transport activities

The Nereva platform is currently deployed and used by transport companies to manage their operations in the construction sector. It provides an operational foundation designed to structure workflows that have historically been managed in a fragmented way, often relying on heterogeneous tools and processes heavily dependent on manual intervention. By centralizing planning, real-time monitoring, and mission coordination, Nereva enables transport operators to gain a unified view of their operations. This ability to make flows visible and traceable supports better day-to-day decision-making while reducing operational friction related to handling unforeseen events.

A research activity structured around real-world complexity

Since its creation, Nereva France has concentrated the group’s research activities with a clear objective: to model and better understand the complexity of logistics flows in the construction sector. These flows are characterized by a unique combination of constraints: strong dependency on field conditions, interdependent sequences of missions, multiple stakeholders, and continuous variability in operational situations. To address these challenges, the work carried out follows a structured approach combining mathematical modeling, algorithm development, and experimentation on real-world data. This approach goes beyond simple digitalization, aiming to build tools capable of integrating the intrinsic complexity of the sector.

EVA: an artificial intelligence engine under development

At the core of this research strategy is EVA (Engine for Virtual Anticipation), an artificial intelligence system currently under development and training. EVA is designed to address dynamic planning challenges, where decisions must simultaneously account for multiple constraints, including resource availability, time constraints, mission sequencing, and operational uncertainties. At this stage, the work focuses on designing and training models capable of leveraging real-world data to progressively improve their ability to represent and anticipate actual operational conditions.

A gradual integration into the Nereva platform

EVA is intended to be progressively integrated into the Nereva platform in order to extend its capabilities. The objective is not to replace existing tools, but to introduce an additional layer of intelligence designed to support transport companies in their day-to-day decision-making. Over time, this integration aims to enable more structured planning, improved anticipation of operational constraints, and enhanced ability to adjust operations in response to changing field conditions. This approach follows a continuous improvement model, where system capabilities evolve progressively through experimentation and data accumulation.

A scientific approach combining multiple disciplines

To address the complexity of these challenges, the research work relies on several complementary approaches. Optimization models are used to structure resource allocation and mission sequencing. Predictive algorithms are applied to estimate key variables such as travel and operation times. Adaptive mechanisms are developed to incorporate real-time changes, while data processing technologies help improve the quality and reliability of the information used. This combination makes it possible to better account for uncertainty and variability in construction transport operations.

Experimentation under real-world conditions

The models developed are trained and tested using data from real-world operations in order to evaluate their behavior in representative environments. These experimentation phases make it possible to identify gaps between models and reality, understand their limitations, and progressively improve their robustness. They are a critical step in developing solutions that are aligned with operational constraints before broader deployment.

A structuring milestone for Nereva

The granting of JEI status marks an important milestone in Nereva’s development. It reinforces a strategy based on a clear articulation between an already operational platform and the progressive development of advanced capabilities derived from research. Through EVA, Nereva pursues a structuring ambition: to evolve construction transport logistics toward a data-driven model capable of integrating operational complexity and sustainably improving decision-making processes.

Nereva obtains Jeune Entreprise Innovante (JEI) status for its French subsidiary.