AUTOMAVIS (AMV) is a spin-off of Centre for Research and Technology Hellas (CERTH) and its Information Technologies Institute (ITI). The company operates in the field of automated quality inspection and integrated machine vision systems.

 

AMV specializes in delivering customized, end-to-end quality control solutions for industrial environments, designing and implementing complete pipelines for data acquisition, analysis, and visualization.

An industrial setting featuring an AI-powered automated quality inspection system. A robotic arm with a camera scans metal parts on a conveyor belt, while a digital screen displays real-time analysis, defect detection, and a 'PASS' status. A technician monitors the process via a control panel in a modern factory. Gemini_generated_image
A vertical three-panel collage showcasing industrial AI solutions. Top panel: A robotic arm performs automated quality inspection on a conveyor belt with a digital overlay showing 99.8% accuracy. Middle panel: A technician uses a tablet to manage a modular, mobile AI inspection unit, representing system flexibility. Bottom panel: A specialist interacts with a large touchscreen to identify product defects like scratches and missing features, with a 'Learning Curve' graph indicating the AI's self-improvement through human interaction. Gemini_generated_image

FEATURES/BENEFITS

What we do

Automated and (near) real-time quality control

on par with human performance. The target for quality control accuracy highly depends on the characteristics of each production line or business process, but AUTOMAVIS will replace manual inspection with automated one matching or even surpassing human operators.​

High modularity

in order to enable fast and agile solution development while supporting integration with customers’ existing business logic.

Faster deployment and lower annotation costs

via smart visualization and efficient machine-operator visual interaction. In this direction, an innovation that AUTOMAVIS will commercialize is online AI systems that through visual interaction with a human operator learn over time and become self-sufficient.