When product quality and production costs are on the line, visual inspection capacity and accuracy are critical.
Relying on error-prone, time-consuming human inspection or traditional computer vision technology that cannot be accurately or easily modified for different product types aren’t practical options. But now manufacturing environments—from parenteral pharmaceuticals to medical imaging equipment sensors and more—can ensure product quality, increase inspection volume, and reduce operating expense with a cost-effective alternative.
AVIS, a revolutionary AI-based automated visual inspection solution, is a game changer for increasing inspection volume, reducing missed defects, and significantly reducing operating and capital expenses, without the need for highly skilled, costly, hard-to-find talent resources required by traditional AI development methods.
In a Fraction of the Time, at a Fraction of the Cost
AVIS can train in as little as one day without the need of extensive defect libraries—a fraction of the time it takes traditional computer vision or neural network/deep learning-based technology.
Why is that? Powered by the next-gen Boon Nano algorithm, AVIS enables automatic detection of defects after self-training from a “normal, defect-free product” model in less than a day on existing equipment.
Dedicated to enable delivery of healthcare services more efficiently and with better outcomes, a leading producer of parenteral drugs wanted a more cost-effective method of semi-automated inspection without human input and decisionmaking. Boon Logic retrofitted the organization’s semi-automated inspection equipment with AVIS, which delivered:
88% fewer false rejections • 22% increase in output
2.5-person reduction per retrofitted inspection machine
In contrast to alternative automated systems that are expensive and can’t be easily and quickly modified for different product types, AVIS is ideal for pharmaceutical manufacturers of higher-mix, lower-volume products that today depend on human operators prone to fatigue and errors.
If you’re looking for a way to increase visual detection accuracy and inspection volume and significantly reduce operating and capital expenses, let’s start a conversation about a feasibility study.