V7 allows you to label, train, and deploy vision AI models and handle your team's ML-Ops stack.
try It for Free

Defect Inspection

Spot damage, scratches, cracks, dents, or missing pieces as small as 0.01% of an image. Detect unforeseeable defects that differ in shape from previously collected examples. V7 allows developers to turn images and video of your defects into state of the art vision AI with semi-automated tagging, model training, and inference deployment. Get started today by automating the detection of anomalies in your production line or manufacturing process, and bring the growing value of AI to your supply chain.

Hover over the image below to see what the AI identifies

A PCB ball grid array with computer vision defect inspectionA PCB ball grid array with computer vision defect inspection

See it in action

Rich text goes here

View additional examples

Click to enlarge the images below

No items found.

Case Study

Lord of the Fries: How a startup inspects 1 billion french fries for defects

John Daly of Intelliscience is the reason your McDonald's and KFC fries look the way they do. As a machine learning engineer at Intelliscience, they developed a conveyor system to sample and scan french fries powered by V7's technology.

From the Blog

ML-Ops defines your accuracy. We help you grow it to 99.9%.

AI performance shouldn't stop at the first training session.
Sync your product with a dataset and continually improve your AI performance by turning its output into new ground truth. Continual learning will allow your models to surpass 95% accuracy barriers and reach 99% and above as more data from your use case or product is supervised by humans, and learnt by your AI.

v7 neurons product
Your product
accurate labeling with v7 darwin
Fast labeling on V7 Darwin
image dataset
Newly collected images or video
model training and pruning
Training & active learning

Ready to get started?

Schedule a demo with our team or discuss your project.

Dataset Management

AutoML model training to solve visual tasks or auto-label your datasets, and a scalable inference engine to launch your project.