EyePop

EyePop is a visual intelligence platform that lets you build custom AI vision models without writing code. Instead of reviewing hours of footage or tagging images by hand, you define what you want to detect — objects, conditions, or behaviors — and EyePop handles the heavy lifting. Models train on your own images, videos, or live streams, then deploy either in the cloud or fully on your own infrastructure. The whole process is guided step-by-step, and you can adjust outputs to match your existing data format.

The platform is built around what it calls 'Abilities' — reusable detection modules that you customize for specific tasks. You start by choosing a target (like a product on a shelf, a roof defect, or a specific camera event), upload or connect your data, and train the model with auto-labeling and prioritization tools. Once trained, you deploy it to run on cloud servers, edge devices, or on-premise hardware. The system is designed for operations teams, not just machine learning engineers, so there's no need to manage infrastructure or write complex scripts. Iteration is straightforward: add new data or tweak requirements, and retrain.

EyePop fits best in environments where visual data needs constant monitoring or enrichment. Surveillance teams use it to turn live or recorded footage into searchable intelligence — door events, perimeter alerts, or unusual activity. Marketplaces rely on it to classify and moderate product images and videos at scale, improving search and catalog accuracy without expanding review teams. Broadcast media and CDNs use it to tag scenes, auto-clip highlights, and process visual content as it flows through their pipelines. It's a practical tool for anyone who needs structured, actionable data from video or images, without bending their workflow to fit a rigid vendor product.