AI visual inspection that starts with normal images alone — reduce inspection effort and move to 100% inspection
This is AI visual inspection that you can start with just a few dozen images of good items — no need to collect defect images. It helps make up for a shortage of skilled inspectors and missed visual defects, with 100% inspection that doesn't stop the line. This page introduces how GAZIRU.eye works and which industries it fits.
Typical challenges
Not enough skilled inspectors who can spot defects / We want to switch to AI inspection, but with high-mix low-volume production we can't collect defect images, let alone large sets of normal images for training / We want AI to reduce visual-inspection misses and inspector fatigue
With GAZIRU
- Builds an AI model from as few as a few dozen images of good items. No need to collect defect images
- Inference in under a second lets you run 100% inspection without stopping the line
- Visualizes anomalies with a heatmap, so people can check the basis for each judgment
How it works
Learns the characteristics of normal products and detects deviations from them as anomalies. The AI engine can be run in parallel or swapped out.
Related products
Industries
Automotive parts / electronic components / metalworking / food manufacturing
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Individual traceability
Narrow recalls to the individual unit. Link process data to each item.
Tagless, engraving-free item management
Eliminate the process and cost of tags, labels, and engraving.
Autonomous manufacturing lines
Emerging use caseMatch the physical item at every robot-to-robot handoff.
Genuine product verification & anti-substitution
Determine whether an item is the one you registered, based on identity with the registered individual.
DPP & linking physical items to digital records
Emerging use caseTurn the physical item itself into the product ID.
Counting & classification
Count and sort in a single shot.