Individual-level traceability: narrow recalls down to a single item
We identify each part by its own surface pattern, and link manufacturing history, inspection results and shipping destination to that individual item. This page introduces traceability built on GAZIRU.z's tagless individual identification — for anyone who wants to narrow a lot-wide recall down to a single item.
Typical challenges
Too small to tag / a single defect forces a lot-wide recall / can't trace which process caused a defect, item by item
With GAZIRU
- Register and match each part by its own surface pattern — no tags or engraving needed
- Link manufacturing history, inspection results, and shipping destination to each item, narrowing recall scope down to the individual unit
- Match process data against each item to pinpoint which process produced a defect, feeding into yield improvement
How it works
Compares the image taken at registration with the image taken at matching time, and judges whether it is the same individual by score. The pool is partitioned by unit — such as lot or manufacturing month — and only the needed data group is loaded and narrowed down at match time. Matches in around a second even against a registry of 1 million items.※1
Related products
Industries
Automotive parts / electronic components / materials / industrial parts
Related articles
Notes
※1 Time to find one individual among 1,000,000 registered items: about 1.5 seconds with 3-way parallelism (without alignment; three machines with 8 physical cores / 64GB each within a public cloud platform; measured in-house in July 2026 on a GAZIRU.z V5.0.0 development build). Results for 4-way parallelism and above, or more than 1,000,000 items, are estimates based on the scaling trend confirmed by measurement. Actual response time varies with the equipment, network configuration and other conditions. With alignment enabled, response time also depends on the image content. A GPU option is available to speed up matching when alignment is enabled.
Other solutions
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.
AI visual inspection (anomaly detection)
Get started with normal images alone, and move to 100% inspection.
Counting & classification
Count and sort in a single shot.