Explore our projects & studies
Explore our projects & studies
From Artificial Intelligence to Human-Computer Interaction and Software Architecture, our research team tackles complex problems across many domains. We seek at providing the best of modern technology for public services, health-care and industry.
New White Paper – AI for maintenance! Challenges, Opportunities and Case Studies
How is artificial intelligence transforming maintenance practices today? What are the concrete opportunities for equipment, infrastructure and fleet managers? The new white paper published by CARL Berger-Levrault offers a rich and accessible insight into these strategic questions. Entitled “AI at the service of maintenance: challenges, opportunities and case studies”, this
Towards a Human-Like Evaluation: Innovations, Applications, and Future Directions
In his previous article, Romain explored the theoretical foundations and methods dedicated to the automatic evaluation of artificial intelligence systems, emphasizing the importance of making them more human and intuitive. This article continues this reflection by focusing on the practical applications of these advances. How are these innovations deployed? What
New White Paper – Agentic AI, a Software and Societal Revolution!
Research at Berger-Levrault is tackling a new frontier in artificial intelligence: that of (partially) autonomous agents capable of reasoning, planning, collaborating, orchestrating and learning in complex business environments. Our new white paper, “Beyond automation – How agentic AI is transforming the software industry”, shares with you an in-depth analysis of
Re.Optim: Responding dynamically to changes and unforeseen events in schedules generated by BL.Optim
BL.Optim: A cutting-edge but static planning optimization tool BL.Optim is our optimization engine for routing and scheduling, used to generate efficient planning under complex constraints. While highly flexible, it currently operates as a static solver: schedules are generated once, based on a submitted problem, and cannot be adapted without full
Clément talks at BIM World 2025 on the future of augmented maintenance using BIM models
At BIM World 2025, held in Paris on April 2-3, Clément Colin, our research engineer in BL Research team, spoke at a workshop dedicated to Digital Twins, alongside Laurent Truscello, Product Marketing Manager at Carl Berger-Levrault. The theme of their talk: “GEM & GMAO – Valuing and increasing the use
Some Berger-Levrault Living Lab Uses Cases: a Valuable Tool to Understand Users in Real Situations
This article by Valérie follows on from a previous one describing the innovative Living Lab set up at Berger-Levrault. As a reminder, this innovative, user-centered approach aims to optimize the development of appropriate, useful solutions that respond directly to the real needs and challenges encountered in the field by customers
Exploiting knowledge of BIM data : A MEP network topology detection method using geometry and spatial knowledge graph technologies
The integration of BIM data into CMMS for asset management and maintenance is becoming increasingly widespread and accessible. Among the standardized data models used in the built asset industry, IFC (Information For Construction) remains the most widely adopted open format within the BIM domain. It facilitates the creation of digital
Automatic Merge Request reviewer with AutoCodeReview
Article co-authored by : Kilian Bauvent (Lead engineer), Benoit Verhaeghe (Project manager and Techn Lead), Marius Pingaud (Developer on BL.CodeRules and diagram designer), Nicolas Hlad (Blog post author and survey designer) On the importance of Merge Requests The overall Merge request Process at Berge-Levrault Let us start by giving the
Experimenting with Innovation in Real-Life Situations: the Key to User-Centered Co-Design
Key words: Innovation – Living Lab – User-centered co-design – Real-life situation – Human and Social Science – Safety – Market launch Innovation at the heart of Berger-Levrault’s strategy A long tradition Innovation is not simply the emergence of new ideas, but their effective implementation to meet the challenges of
Automatic Evaluation of LLM and RAG: Foundations and Established Methods
In professional environments, an increasing number of companies are integrating Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) solutions to automate specific tasks or support their teams. However, ensuring the accuracy and relevance of these systems’ responses is critical, particularly when handling sensitive data or high-stakes business decisions. Evaluating these
