AI first vision products for unmatched accuracy

Overview.ai systems achieve best-in-class accuracy on micron-level defects. Our edge-computing cameras use advanced deep learning models trained on your specific defects, ensuring superior detection rates while minimizing false positives. Real-world deployments consistently exceed customer accuracy requirements across automotive, pharmaceutical, and electronics manufacturing.

Deployment typically takes days, not months. Our plug-and-play cameras come with pre-configured AI models that you can fine-tune in hours with as few as 5 images. The browser-based interface requires no special software installation, and our team provides hands-on support throughout setup and initial training.

No. Overview.ai cameras run completely on-premise with zero cloud dependency. All AI processing happens at the edge on embedded NVIDIA GPUs, ensuring your production data stays secure within your facility. This also means no latency issues, no monthly cloud fees, and no internet connectivity requirements for operation.

Our systems detect surface scratches, dents, cracks, contamination, color variation, dimensional errors, assembly defects, and virtually any visual anomaly. The OV10i excels at classification tasks, the OV20i handles complex segmentation, and the OV80i captures ultra-high-resolution details for micron-level inspection. Our defect library showcases hundreds of real-world examples across industries.

Overview.ai cameras support standard industrial protocols including Ethernet/IP, Profinet, Modbus TCP for direct PLC communication, as well as MQTT, OPC-UA, and HTTP for MES/SCADA integration. You can trigger inspections via digital I/O, network commands, or software triggers. Results output via TCP/IP, digital outputs, or APIs in real-time.

Unlike traditional AI systems requiring thousands of images, Overview.ai models can be trained with as few as 5 examples. Our transfer learning approach leverages pre-trained models, dramatically reducing data requirements. You can even start with good parts only for anomaly detection, then refine as you collect defect examples during production.