Vision Systems That Catch Defects Before Customers Do

For a focused detection task, a few hundred well-labeled images per class is often enough to start, because we fine-tune pretrained models. Rare defects can be covered with anomaly detection or synthetic images. The feasibility study tells you what your data supports.

A prototype on your sample images starts from $2,500, a production system at one site from about $15,000, and multi-site platforms from $40,000. Cameras, lighting and edge devices are extra and depend on line speed and resolution.

We cannot promise a number before seeing your images, but we measure it early. The feasibility study gives an estimate, and every model is scored on held-out images using mAP, recall and false reject rate before it goes live.

Edge devices such as NVIDIA Jetson suit production lines that need decisions in tens of milliseconds or have limited bandwidth. Cloud inference suits document processing and batch video analysis. Many setups use both, with the edge doing detection and the cloud doing retraining.

Usually yes. We connect to RTSP, ONVIF and GigE Vision cameras. If image quality is the limiting factor, we recommend specific lighting or lens changes, which often matter more than the model.

Vision-language models like GPT and Gemini are useful for open-ended questions about images and for low-volume tasks. For high-volume inspection with strict latency and cost limits, a fine-tuned detector is faster, cheaper per image and more consistent.

We blur faces and plates on device, avoid storing raw video where it is not needed and set short retention periods. Processing can stay fully on premises if your policies require it.

Monitoring tracks confidence and reject rates per station. When they shift, reviewer-corrected images feed a retraining round, and the new model is tested on the frozen test set before it is rolled out.

You do. Labeled images, trained weights, deployment code and documentation all belong to you, with the source code and IP fully transferred.