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Amazon’s Project PI AI looks for product defects before they ship

Amazon’s Project PI AI looks for product defects before they ship

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Project PI combines computer vision and generative AI to catch damaged items and prevent returns.

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Amazon’s Project PI, or “Private Investigator,” setup combines generative AI and computer vision to “see” damage on products or determine if they are the wrong color or size before the item gets sent to customers.

The way it works is that products on their way to customers go through a tunnel that scans the items. The computer vision program — a type of AI that looks at images and understands what’s in them — checks to see if there is damage. If it finds something, that item is isolated, while the system evaluates the defect and determines if there’s an issue with similar items to track down the root cause.

Animation showing spray bottles in boxes from a top-down view and highlighting one that’s leaking

Animation showing spray bottles in boxes from a top-down view and highlighting one that’s leaking

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According to Amazon, Project PI is active in “several” North American warehouses and will be added to more sites throughout the year. Last year, Amazon rolled out a different system that flags frequently returned items to highlight items that tend to have issues before customers actually hit the order button. This all highlights how avoiding a potentially “nightmarish” return process is good for customers, Amazon, and the environment in terms of reducing carbon emissions.

The company says human Amazon employees review the things Project PI flagged so they can decide if they will be sold at a discounted price on Amazon’s Second Chance resell site or be donated elsewhere. 

Amazon is also working on bringing in a multimodal large language model to investigate why customers are dissatisfied with the items they receive. The AI tool reviews what customers say in their feedback and then scans images from Project PI and other data sources to try to find out where things went wrong. Amazon says this technology could be helpful to its other sellers so they know if they accidentally mislabeled items.

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