Talk to your fresh produce business
Pak-Bot is a chatbot that knows your stock, orders, batches, inspections and customers. Type or say a question on any phone, tablet or PC and it answers from live Producepak data: a number, a table, a chart, a label or a sent email.
6 pallets, 2,880 kg, all in Cooler 2. Pallet 5487 is the oldest. Want them moved to the top of today's pick list?
Illustrative answer. Pak-Bot answers from your own Producepak data.
A chatbot built for produce, not bolted on
General chatbots know nothing about your cool rooms. Reporting tools need someone who knows where the data is. Pak-Bot sits inside Producepak, the app your team already uses to receive, pack, inspect and ship, so it starts every conversation knowing your pallets, varieties, growers and customers.
It was the first AI chatbot developed only for fresh produce packers, wholesalers and food service companies.
Who uses Pak-Bot, and for what
| Role | What they ask Pak-Bot to do |
|---|---|
| General manager | Compare sales, waste and margin by customer, product or site; spot trends without waiting for month-end. |
| Packhouse manager | Check raw material against tomorrow's orders, see batch waste, flag old lots before they are passed over. |
| Sales and customer service | Confirm what can ship today, including what is on the packing line, and email a customer their order history. |
| Quality manager | Find failed inspections by supplier, pull photos for a claim, trace a complaint back to the field or block. |
| Office and accounts | Enter emailed orders, create a new retailer's label or invoice format, send reports and documents. |
| Warehouse and dispatch | Locate pallets, check what is still to pick, and stop a wrong pallet before it is loaded. |
Results from Producepak AI in the field
A seven-site grower and packer of leafy greens, herbs and asparagus used Producepak AI quality inspection and fed the results into Pak-Bot and the quality dashboard.
- Inspection time per sample fell from 14.5 to 4.35 minutes.
- AI accuracy rose from 81% at pilot to 99.8% by month 6.
- The team now relates harvest temperature to quality and uses supplier history in price negotiations.
Source: Producepak QI case study, January 2026 (client name withheld).
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.