Accelerate Your Anomaly Detection At Any Scale
Automatically identify defects for improved scale, quality, and reduced cost across your entire manufacturing process without the need for image labels.
Upgrade your visual inspections with advanced AI
Deploy a visual inspection algorithm in a single hour
Scale-up with ease, with no need for training, labeling, or setup
Spend less human resources on troubleshooting and RCA
Gain real-time insights about production issues and bottlenecks
Labeling images for AI classification doesn't scale — but your production line does.
Visual inspection is an integral part of the manufacturing process. Unfortunately, it’s also among the most significant drains on time, resources, and budget your organization will encounter. Inspection algorithms were meant to reduce this burden, but they often have the opposite effect.
With traditional artificial intelligence, you need a dedicated algorithm for each product type, and a label for each image. Training alone is extremely costly and time-consuming, often requiring several months for each algorithm. As for the labels, these require Ph.D. domain experts to comb through literally thousands of images.
Under this model, scaling is almost impossible.
Vanti can build you a Defect Detection model in just 60 minutes without the need for labeling and without sacrificing performance.
Learn how this top memory manufacturer achieved a detection rate of 95% See this manufacturer's success story to discover how Defect Detection improved efficiency by 30% and achieved full anomaly detection coverage for a process comprising over 2,500 steps.
Gain real-time insights from your image inspection
Unlock the true value of your visual inspection data with purpose-built models that fit your processes.
Seamless integration with your existing data sources
Bridge the gap in your data flows and unlock faster insights with an integration-ready solution.
High-performing defect detection
Streamline image inspection with a scalable, unsupervised, product-agnostic algorithm.