Prevent receiver rejections with fresh produce AI
A rejected load costs the fruit, the freight, the claim and the customer's trust. Most rejections trace back to a pallet that should never have been loaded. Producepak AI checks quality and order accuracy before the doors close.
Four checks before it ships
Quality at the loading dock
Before sealing, the supervisor asks "Show me the QC results and photos for the pallets on container 4". Failed or uninspected pallets are flagged.
Holds on failed stock
A failed inspection can put inventory on hold automatically and alert quality and dispatch with photos.
Right pallet, right order
Every pallet is checked against the order at scan; mismatches sound an alarm.
Pre-shipment QC
Inspections can be run on pallets, containers and trailers at the point of dispatch or export.
When a claim comes in anyway
Photos taken after picking, on the trailer and at delivery are attached to the BOL. Receivers can scan a QR code to see what was sent and sign for delivery. When a claim arrives, Pak-Bot pulls the inspection results and photos for that invoice in seconds.
The same evidence works upstream: out-of-spec deliveries from growers can be claimed back with proof.
Rejected export containers cut from 3 a season to 1; mock recalls from 6 hours to 15 minutes. Each rejected container cost about AU$60,000. Rejections fell from three a season to one, and AU$35,000 in supplier claims was recovered.
Questions
Can AI inspect fruit before dispatch?
Yes. Producepak AI quality inspection uses photos and a model trained on your criteria; it reached 99.8% accuracy in one seven-site deployment.
Does it work for export containers?
Yes. QC results and photos can be checked for every pallet on a container before it is sealed.
How does it help with customer credits?
Fewer wrong or short deliveries, plus photo evidence of what was sent. A wholesaler halved credits from 1.8% to 0.9% of sales.
Put a question to Pak-Bot about your own packhouse
Book a session with a Producepak consultant. You will see Pak-Bot working on real produce data, and talk through your crops, sites, customers and documents.