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.
Classifying Spare Parts to Power Smarter Preventive Maintenance
This article was written in collaboration with Research Engineer Enzo Pinho Fernandes of the Industry 5.0 Lab within Berger-Levrault’s BL Research team. Introduction A single missing bolt can stop an entire production line. When this happens, the cost is not only the price of the part, but also the production
So, what exactly is a MoTion skill ?
Introduction It is fascinating to see how quickly AI is advancing, and even more striking how suddenly it entered mainstream life. In late 2022, when ChatGPT was launched, AI moved from research labs into everyday conversations, classrooms, offices, and homes. That moment opened the floodgates for a broader transformation in
What’s Draining My Energy? Energy Observability for Greener Containerized Infrastructure
This article was written in collaboration with Research Engineer Otmane Tahraoui of the Software Engineering Lab within Berger-Levrault’s BL Research team. Introduction Energy consumption is no longer just a technical curiosity. It has become a business necessity driven by three converging factors: reducing infrastructure costs, supporting sustainability commitments, and addressing
Building Knowledge Graph RAG solution from Model Driven Engeneering – Part 1
Building Knowledge Graphs for RAG from Famix Models Large Language Models (LLMs) are impressively good at understanding code — but they also hallucinate. When reasoning about an unfamiliar codebase, an LLM can fabricate methods, classes, or relationships that don’t exist. This is a real problem when we ask them to help us navigate,
From Local Retrieval to Global Reasoning: GraphRAG for Legal Knowledge Modeling
1. Introduction Artificial Intelligence is reshaping the way legal professionals access, analyze, and reason over information. Retrieval-Augmented Generation (RAG) has become the foundation of many Legal AI assistants, enabling Large Language Models (LLMs) to retrieve relevant legal documents before generating responses (Li et al., 2025). This approach has significantly improved
From Discovery to Business Impact Assessment: A User-Centered Method for Analyzing Generative AI
Introduction The emergence of generative artificial intelligence, and more specifically chatbots, marks a turning point in the recent history of digital technology. Unlike many professional innovations that were adopted gradually, these tools have spread particularly rapidly among the general public. Their ability to generate content, search for information, assist with
Understanding AI Hallucinations: Causes, Techniques, and Evaluation
1. Introduction Generative artificial intelligence models have fundamentally transformed the way we create, access, and use information. However, their widespread adoption comes with a major challenge: hallucinations, the generation of content that appears credible and fluent but is factually incorrect or entirely fabricated. Today, hallucinations represent one of the most
What Should a Machine Teammate Look Like?
Design guidelines for conversational AI interfaces and why a blank text field is not good enough The broken promise of an input inbox There is a design decision that almost nobody talks about, yet it shapes every interaction people have with generative AI today: the empty input box. You open
From Generic to Domain-Smart: Tailoring Embeddings for High-Performance Business Search
1. Strategic Context and Challenges Modern answer engines look simple from the outside: a user asks a question, and the system returns a grounded answer. But before the language model can write anything useful, the system must first retrieve the right sources from the enterprise knowledge base. If the right
Beyond Static Chunking: Multimodal and Adaptive Segmentation for Improved Information Retrieval
Introduction Retrieval-Augmented Generation systems [Li et al., 2025], called RAG, rely heavily on how documents are segmented before indexing. This preprocessing step, known as chunking, directly affects retrieval quality and, consequently, the performance of downstream question-answering systems. In practice, chunking strategies [Jain et al., 2025] hardly take into account the
