Who Bumped My Car? Multimodal Agentic AI for Edge Case Mining in Massive Datasets

In vehicle inspection, spotting edge cases like a shallow scratch on a tail light can make all the difference. These rare issues often lie in the long tail of the distribution - subtle, underrepresented, and easy to miss. At UVeye, we scan over a million vehicles each month, making it critical to surface those rare examples that impact real-world performance and customer trust. In this talk, I will share the journey of developing an agentic AI architecture designed to improve deep neural network performance within an algorithm development workflow. I’ll describe the key steps that helped me build a system that performs smart, selective semantic search over massive datasets. It uses an MCP to retrieve metadata via SQL queries and RAG to fetch relevant images and videos, enabling context-aware search across petabyte-scale data. I’ll demonstrate how this approach helped us find missed cases, retrain models, and improve results. By the end of this session, you’ll learn how agentic AI can transform passive data into an interactive, searchable source of truth.

Speakers