The founder
Pierre Lague
French AI researcher building the trust infrastructure he believes Europe needs — grounded in interpretability, reproducibility, and the conviction that powerful AI must remain accountable.
Pierre Lague · FounderPierre Lague is a French AI researcher and PhD student at Inria Rennes (I4S team) and the Université d'Angers, in collaboration with the Direction Générale de l'Armement. His doctoral research focuses on physics-informed machine learning for structural health monitoring and vibration characterisation, with a particular emphasis on open-source, reproducible implementations of standards-based methodologies — work where the reliability and interpretability of AI systems are non-negotiable.
He holds a Master's degree in Machine Learning (Université de Lille — ranked 3rd, with honours) and in Complex Systems Engineering, and a Bachelor's in Computer Science (Université Bretagne Sud — class major). Throughout his studies he built research experience through multiple internships at the CNRS, leading projects in semantic segmentation for aerial drone imagery and 3D point-cloud classification.
He then joined the private sector as a Machine Learning Engineer, building the entire technological stack for cloud optimisation with deep-learning time-series forecasting and full autonomy over research and product development. His broader technical interests span reinforcement learning, multi-agent systems, explainable AI, and physics-informed neural networks.
My research has always lived in the same place: making powerful models you can actually trust — physics-informed, reproducible, and explainable enough that an expert can verify them rather than merely believe them.
The same principles decide whether AI defends a society or quietly erodes it. As fraud, deepfakes, and coordinated manipulation industrialise, Europe needs intelligence infrastructure that is powerful and accountable — built under our law, open to scrutiny, and answerable to the public it protects.
That is why I founded Elendil Systems.
Trajectory
A track record in reliable, interpretable AI.
PhD Researcher — Physics-Informed ML
Inria Rennes (I4S) · Université d'Angers · with the DGA
Doctoral research on physics-informed machine learning for structural health monitoring and vibration characterisation — with a focus on open-source, reproducible, standards-based implementations, where the reliability and interpretability of AI systems are non-negotiable.
Machine Learning Engineer
Sudo Group
Built the entire technological stack for cloud optimisation using deep-learning time-series forecasting, with full autonomy over both research and product development.
Research Internships — Computer Vision
CNRS
Led projects in semantic segmentation for aerial drone imagery and 3D point-cloud classification across multiple internships.
MSc Machine Learning · MSc Complex Systems · BSc Computer Science
Université de Lille · Université Bretagne Sud . Université Technologique de Compiègne
Master's in Machine Learning (Université de Lille — ranked 3rd, with honours) and Complex Systems Engineering; Bachelor's in Computer Science (Université Bretagne Sud — class major).
Building this with us?
Elendil Systems is early. If you are a researcher, institution, or partner who shares the mission, Pierre would like to hear from you.