Abstract: Periorbital measurements such as margin reflex distances (MRD1/2), palpebral fissure height, and scleral show are essential in diagnosing and managing conditions like ptosis and eyelid disorders. We developed Glorbit, a lightweight, browser-based application for automated periorbital distance measurement using artificial intelligence, designed for use in low-resource clinical settings. The app integrates a DeepLabV3 segmentation model into a modular pipeline with secure, site-specific Google Cloud storage. Glorbit supports offline mode, local preprocessing, and cloud upload via Firebase-authenticated logins. We evaluated usability, cross-platform compatibility, and deployment readiness through a simulated enrollment study of 15 volunteers. The app completed the full workflow -- metadata entry, image capture, segmentation, and upload -- on all tested sessions without error. Glorbit successfully ran on laptops, tablets, and mobile phones across major browsers. The segmentation model succeeded on all images. Average session time was 101.7 seconds (standard deviation: 17.5). Usability survey scores (1-5 scale) were uniformly high: intuitiveness and efficiency (5.0), workflow clarity (4.8), output confidence (4.9), and clinical utility (4.9). Glorbit provides a functional, scalable solution for standardized periorbital measurement in diverse environments. It supports secure data collection and may enable future development of real-time triage tools and multimodal AI-driven oculoplastics. Tool available at: https://glorbit.app