Elementary AI Vision for Automated Inspection

Enables users to adjust vision sensor settings remotely using an intuitive visual editor that requires no programming knowledge.

Synchronizes inspection profiles instantly across all manufacturing lines and separate facilities to maintain high accuracy and consistency.

Utilizes a patent-pending AI mechanism to automatically capture an image frame as soon as a part arrives on the line.

Integrates traditional machine vision assessment capabilities with data-driven deep learning tools to evaluate products.

Allows operators to establish precise pass criteria and track specific operational metrics like Target Yield across inspection stations.

Uses a unified defect detection tool that seamlessly adapts to different automated use cases and varying manufacturing industries.

Helps non-technical team members design complex defect detection routines using an accessible graphical interface.

Uses proprietary technology to build and train complex deep learning models in just a few seconds.

Detects surface anomalies, dimensional variations, assembly mistakes, and label defects across a wide variety of production materials.

Enhances detection accuracy over time by learning from every new part and image captured during active inspections.

Compatible with area scan, line scan, and X-ray vision sensors to allow rapid scaling and flexible hardware deployment.

Generates performance evaluations for deep learning models using sample parts collected directly from the manufacturing process.

Powers ultra-fast local image processing to perform real-time analysis directly on the physical factory floor.