Quality
Review individual assessments with product context while keeping authority over acceptance and disposition.
Solutions & FAQs / AI Quality Assistance
Find inspection problems hidden among the rejected parts.
When a station keeps rejecting parts, reviewing images one at a time can consume the team's attention before anyone sees a pattern. Automatic image review helps bring possible false rejects to quality and engineering for investigation.
A reject count does not explain whether the inspection needs attention.
A high reject rate creates both product questions and review work. Engineers need to understand whether the product changed, the inspection changed, or a recurring condition is misleading the inspection. IntelliTrack™ links AI image reviews to the individual product and surfaces a station-level finding when enough reviews suggest possible false rejects.
Review suspected false rejects with their production history.
Review rejected-part images automatically
Periodic sweeps create AI review records for rejected parts' images. The reviews provide assessments for people to examine; they do not change the production verdict.
Surface the recurring pattern
When too many reviewed images look like possible false rejects, IntelliTrack™ creates a station-level finding. The finding appears in the findings inbox and notifications so the team knows where to investigate.
Inspect the parts behind the finding
Use the Image Reviews page and each Product Report to examine assessments alongside production history. Engineering and quality determine whether the inspection, the process, or the product needs attention.
Engineering
Start with a defined station-level pattern instead of first having to discover it through a manual image review.
Production
Bring a recurring inspection concern to the responsible people before it simply becomes an accepted source of rework.
Illustrative workflow
A station rejects parts that appear acceptable on review.
An image sweep produces several reviews suggesting possible false rejects and raises a station finding. The team opens the finding, examines the Image Reviews page, and checks the relevant Product Reports. Engineering investigates the inspection conditions while quality determines the next steps for the affected parts. This is an illustrative workflow; an AI assessment is not proof that a part is acceptable.
Plan the setup around your process.
Configure image capture, image-to-product association, review settings, and the provider connection. Images are excluded from external provider calls by default; enable permitted sharing during setup. Confirm sweep scheduling, finding thresholds, and notification recipients during setup.
Questions your team may ask
Does AI turn a rejected part into a passed part?
No. AI agents have no ability to change IntelliTrack™ records. They create review output for authorized people to assess; product decisions remain subject to the existing role, signature, and approval controls.
Where do we see the result of a sweep?
Individual assessments appear on the Image Reviews page and on each Product Report. Station-level findings appear in the findings inbox and notifications when the configured criteria are met.
How do we connect an image concern to the production process?
Open the review alongside the Product Report to examine the associated production history. Use that context to decide which process relationships deserve further investigation; the automated sweep itself focuses on rejected images.
Can this establish the cause of a defect?
It provides a lead for investigation and connects the image assessment with production context. Correlation and model output do not establish causation; engineering must test the explanation and quality must assess the product.
Review image analysis against a recurring inspection concern.
We can follow a rejected image through review, the station finding, and the associated product history.