Error and reproduction

Challenge

Modern industrial products integrate complex hardware and software, often resulting in numerous configuration variants. When failures occur in the field, diagnosing and reproducing them is often slow and inefficient. Critical information is often incomplete, fragmented across systems, and progressively degraded as it moves from service teams to R&D. Consequently, reproducing field issues can take days or weeks, miscommunication is common, and valuable knowledge is rarely captured in a reusable form. This results in prolonged resolution times, increased costs, and rising customer dissatisfaction.

Project goal

Via this project (ERRARE_IRVA), we aim to shorten the field-to-fix cycle for complex technical issues. The project delivers an AI-powered assistant that supports service engineers through structured error reporting, assists R&D with automated reasoning and test scenario generation, and consolidates diagnostic insights into a unified, reusable knowledge base. By combining rapid, practical field support with in-depth analytical capabilities, ERRARE_IRVA enables faster issue reproduction, fewer escalations, and fosters continuous learning between service and engineering teams.