Lazar Trifunovic is Solutions Architect at Camio, a visual compliance software service that monitors and reports policy deviations in real time. Camio uses the latest advances in LMMs to detect and remediate risks by comparing compliance policy text to the activities observed by standard IP cameras. Before joining Camio, Lazar was a Field Application Engineer at Boulder Imaging, where he used AI and high-precision optics to reduce endangered species turbine strikes at wind farms internationally by detecting birds up to 1,600m away. Lazar also applied machine vision technologies to currency systems for Federal Reserve products that detected deviations such as tears, folds, counterfeits and graffiti. Lazar earned his BS in Mechanical Engineering from the University of North Carolina at Charlotte, where he began his AI journey applying machine learning to optimize prototyping costs by analyzing wind tunnel experiments in conjunction with numerical modeling simulations.