How to use AI for produce orders and customer labels

Two jobs worth handing to AI

In most produce offices, two tasks soak up time every single day: keying customer orders and preparing customer-specific labels. Both are repetitive, both are done under time pressure, and both cause real damage when they go wrong. A wrong quantity becomes a short-shipment. A wrong label becomes a rejected pallet at a distribution centre.

Pak-Bot can take on both. It converts customer documents into orders, and, once a customer's label design is loaded, it can generate that customer's labels instantly. This page explains how to use each, step by step, and how to roll them out without risk.

How to use AI to turn a purchase order into an order

Step 1: give Pak-Bot the document

Customer purchase orders arrive as PDF attachments, spreadsheets, email text or photos of order sheets. Pak-Bot is designed to work with the documents customers already send, so nobody has to change how they order.

Step 2: let Pak-Bot read it

Pak-Bot reads the document much as a person would. It looks for the customer, purchase order number, delivery date and location, and each order line: product, pack size, quantity and, where present, price. It matches the customer's descriptions to your products in Producepak and prepares a draft order.

Step 3: check the draft

A person reviews the draft before it is released. Concentrate on:

• lines Pak-Bot could not match with confidence;
• new products or products the customer rarely orders;
• quantities far outside the customer's usual range;
• delivery date and location, especially on multi-drop retailer orders;
• special instructions such as "no substitutions" or "ripe for Friday".

Step 4: release

Once confirmed, the order is a normal Producepak order: it flows to picking, packing, labels, dispatch documents and invoicing, with full traceability back to the original purchase order.

Getting purchase order capture right

Prepare your product list

Matching works best when product names are clear. "Roma Tomatoes 10 kg Carton" matches far more reliably than "TOM RMA 10". Make sure each product's unit of sale is set correctly (carton, kilogram, each, bag) and record customers' own item codes against your products where you can.

Start with the easiest customers

Begin with two or three customers whose orders arrive as clean PDFs. Review every draft carefully for the first week. Then add spreadsheet and email-text orders, then photos.

Feed back mistakes

When Pak-Bot misreads a line, correct it in the draft and leave feedback. Recurring misreads, such as a customer's shorthand for a product, are exactly what the team uses to train Pak-Bot.

Customers that trade by EDI can continue to do so; Producepak supports EDI. Document capture covers everyone else.

How to use AI for customer labels

The Pak-Bot guide puts it in one line: give Pak-Bot your customers' label designs and Producepak will be able to generate those labels instantly, with no need to use the label designer.

Step 1: collect each customer's label specification

For each customer, gather what you have: their specification document, an approved sample label or a clear photo of a compliant label. Note the label size, the printer used and the products the label applies to.

Step 2: send it to Producepak

The design is set up as a template for that customer. Data fields such as product description, barcode, lot code, pack date, best-before date and weight are linked to Producepak records.

Step 3: ask for labels

From then on, packing staff ask for that customer's labels rather than editing a layout. The template fills in the details from the system, so the date and lot on the label match the record.

Step 4: check one label per run

At the start of each run, print one label and compare it with the specification: product wording, barcode scan, lot and dates. One check catches setup errors before hundreds of cartons are labelled.

Why templates reduce errors

Hand-edited labels go wrong in predictable ways: yesterday's date left in place, the wrong customer's layout selected, a lot code typed from memory, a font or logo changed by accident. Templates fed by system data remove each of these. They also keep labels identical across sites, because every site prints from the same template.

That consistency matters for traceability. The lot code on the carton is the link between a product in a customer's hands and the records behind it. When the label is generated from the record, the two match by design.

Measuring the difference

Before you start, note a few baselines so you can see the effect:

• orders entered per day and the time it takes;
• time from order received to order released for picking;
• order errors reaching customers each month;
• deliveries rejected for label problems.

Check the same measures after a month. Many can be answered by asking Pak-Bot itself, such as "number of orders by customer this week".

Worked example: an email order from a café

An illustrative order arrives at 5:10 am in the body of an email: "Hi, for tomorrow please: 2 x cos, 4 x 10kg onions brown, 1 tray avo 20s, 3 boxes roma toms. Thanks, Marco." Here is how the four steps play out.

1. Give it to Pak-Bot. The email is passed to Pak-Bot for conversion.
2. Pak-Bot reads it. It identifies the customer from the sender, sets the delivery date to tomorrow, and finds four lines: cos lettuce, brown onions in 10 kg bags, avocados count 20 by the tray, roma tomatoes by the box.
3. Check the draft. The reviewer confirms that "box" for this customer means a 10 kg carton of roma tomatoes and that "2 x cos" means two cartons rather than two heads, then corrects anything Pak-Bot flagged.
4. Release. The order goes to picking with the other morning orders.

The reviewer spent time only on the two lines that needed judgement. Leaving a comment on the misread shorthand helps Pak-Bot handle the same café's next order better.

Worked example: a multi-drop retailer PO

Supermarket purchase orders often list several distribution centres, each with its own quantities and delivery windows. When checking a draft from a retailer PDF, confirm that each delivery point has become its own order with the right date, and that pack sizes match the retailer's item codes. Once these are set up correctly for a retailer, later orders from the same retailer usually need less checking.

Who does what

Labels and traceability rules

Labels are where traceability becomes physical. In the United States, the FDA Food Traceability Rule under FSMA section 204, with a compliance date of 20 July 2028, requires businesses handling foods on the Food Traceability List, including many fresh produce items, to keep records linked to traceability lot codes. A label generated from the lot recorded in Producepak keeps the code on the carton consistent with the record behind it. Retailers worldwide have similar expectations for lot and date information on cartons and pallets.

A four-week rollout

Adjust the pace to your season. Nothing here is worth disrupting a peak week.

Common order-reading pitfalls and how to catch them

None of these is unique to AI; they trip up people keying orders by hand too. The difference is that the reviewer can focus on them instead of retyping every line.

Keeping customers informed

Once orders are flowing, Pak-Bot can help answer the questions customers ask afterwards. Staff can ask "orders shipped to [customer] today" or "short-shipped orders for [customer] last week", check the answer and email it to the customer contact. Answering promptly with facts, rather than promising to call back, is part of being a reliable supplier.

When to keep orders manual

Not every order needs to go through document capture. Phone orders taken by a salesperson, one-off orders with unusual pricing, or complex orders that need negotiation may be quicker to enter directly. The aim is to remove repetitive keying, not to force every order through one route. Most businesses find that the regular daily orders from regular customers, which make up the bulk of the volume, are where AI capture saves the most time.

Labels for new customers

When a new customer comes on board, ask for their label specification at the same time as their first order details. Getting the template in place before the first delivery avoids a rushed label set-up on the morning of the first run, which is exactly when mistakes happen.

Questions to ask after orders go live

Once purchase order capture is running, use Pak-Bot to keep an eye on it. "Number of orders by customer this week" shows volume. Comparing orders against sales for key customers shows whether order errors are turning into short-shipments. Tracking label-related rejections from distribution centres shows whether templates are working. A short monthly review of these numbers, with the office and packing leads together, keeps both processes on track and highlights the next customers to add.

Orders and labels questions