A part of FEV Group
Discover how task-specific Small Language Models (SLMs) bring responsive, locally available generative AI to the vehicle cockpit—ensuring fast and reliable control even without a continuous cloud connection.
Discover how FEV IntelliGen transforms AI Systems Engineering by automating requirement workflows, accelerating artifact creation, and ensuring end-to-end traceability for modern engineering projects.
Expensive test rigs sitting idle costs time and money. FEV.io’s Smart Testing Management Platform (LIMS) transforms lab arithmetic across complex engineering fields. Developed and validated to TRL 6 within the EU- and UKRI-funded FASTEST project, our software optimizes high-stakes test environments—proven on demanding battery testing use cases.
Discover how FEV.IO TRACK uses agentic AI to keep requirements, test specifications, and validation evidence synchronized along the V-model — reducing manual documentation effort while keeping human experts in control.
Unlock the power of FEV’s collective engineering expertise. Discover how our AI-powered EPF Chatbot streamlines workflows by providing teams with instant access to system requirements and development processes.
Learn how the FEV Bot innovation program combines humanoid robotics, AI, simulation, and functional safety to develop safe and reliable robotic applications.
Discover how FEV etamax combines Functional Safety and trustworthy AI to enable safer autonomous rover navigation for planetary exploration and other safety-critical industries.
Modern observatories rely on more than mirrors. Learn how software supports observation planning, scheduling, data processing, and archiving at the European Southern Observatory (ESO), enabling scientific discovery from the Very Large Telescope to the upcoming Extremely Large Telescope.
Even millimetre-sized debris particles can threaten satellite missions at orbital velocities. Learn how FEV etamax uses ESABASE2 to assess debris risks, hypervelocity impacts, and environmental influences throughout spacecraft development.
Over the past 15 years, scenario-based approaches have become a key method for developing and validating CCAM systems. This paper highlights their evolution from rule-based to data-driven and AI-supported techniques, with insights from FEV.io’s work in major EU-funded projects such as L3Pilot, HiDrive, and Synergies.