Robovision provides an end-to-end Computer Vision AI platform designed for industrial applications across sectors like manufacturing, food, and agriculture. The platform supports the entire machine learning lifecycle, from data labeling and model training to robust deployment on-premises or in the cloud. This enables businesses to automate visual inspection tasks such as defect classification and 2D grading with scalable, high-performance AI solutions.
Funding
Funding not disclosed
Founders
Product
Problem
Many industrial environments struggle to efficiently manage and scale vision AI due to the dynamic nature of production lines and the need for specialized AI expertise. Traditional systems often lack the adaptability to handle variations in product type, machine floor changes, and the tracking of new defects, leading to increased downtime and reduced throughput.
Solution
Robovision offers a comprehensive computer vision AI platform designed to empower users to optimize automation processes and increase value without requiring extensive AI knowledge. The platform streamlines the AI lifecycle, from data collection and annotation to model training, testing, optimization, and deployment. By providing a user-friendly, no-code interface, Robovision enables operators and domain experts to easily retrain models and manage changes, ensuring continuous improvement and minimizing the need for data scientist intervention. The platform supports both cloud and edge deployments, allowing for flexible integration with existing hardware and infrastructure while maintaining control over intellectual property.
Target Audience
Robovision targets machine builders and factory owners across industries such as agriculture, food processing, healthcare, logistics, and manufacturing who seek to enhance automation, improve quality control, and reduce operational costs.
Features
- No-code interface allows operators to adapt and maintain AI models without specialized AI skills
- Supports a wide range of hardware and deployment options, including cloud and on-premises
- Advanced annotation tools, including GrabCut and Magnetic Lasso, for fast segmentation
- Training data analytics ensure class balance with graphical class distribution analysis
- Interactive progress monitor for real-time visualization of training metrics
- Model optimization tools classify uncertain samples into an "unknown" class, protecting against model drift
- Model Monitor offers detailed reporting, tracking metrics like "unknown" rate and identifies low-confidence samples
- Built-in algorithms for semantic segmentation, instance segmentation, classification, object detection, anomaly detection, and multiview classification