AI label generation from customer label specifications

The problem: every customer has a specification

Ask any produce supplier with a few large customers about labels and you will hear the same story. Each customer has a label specification. Some are a single page with a sample label. Others are long supplier manuals with sections on label size, element positions, fonts, barcode types, mandatory text, placement on the case and examples of right and wrong labels. Each specification has to be turned into a working label format, tested and approved, and every time the customer updates the specification, the work starts again.

Done by hand, setting up a new customer format typically means an experienced person reading the specification, building the layout in a label designer, connecting the data, test printing and fixing small differences. It is skilled work, it is slow, and small mistakes can slip through until a distribution centre rejects a delivery.

The AI approach

Producepak includes Pak-Bot, a fresh produce AI chatbot that can read documents as well as answer questions. The Pak-Bot guide states it plainly: 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 by step: generating a label format with Pak-Bot

Step 1: gather the specification

Collect whatever the customer has provided: a specification PDF, a supplier manual section, an approved sample label, or a clear photograph of a compliant label. The more complete the material, the better the result. Useful details include:

• label size and orientation;
• every mandatory element and its position;
• barcode types and what each encodes;
• font sizes or minimum text heights;
• date formats and rules, such as best-before days;
• logos or marks the customer supplies;
• placement on the case or pallet.

Step 2: upload it to Pak-Bot

Give the specification to Pak-Bot. It reads the document, identifies the label elements and their requirements, and works out which Producepak data each element needs: product description, GTIN, lot, pack date, weight, origin, customer item code and so on.

Step 3: review the generated format

Pak-Bot produces a label format mapped to your data. Look at it the way a customer's quality team would. Is every mandatory element present? Are the barcodes encoding the right data? Do sizes and positions match? If something needs adjusting, you can fine-tune it in the label designer.

Step 4: test print and approve

Print a test label on the actual printer and label stock. Scan every barcode. Lay the test label next to the customer's sample or specification and check element by element. When it matches, approve the format and link it to the customer and products.

Step 5: print on demand

From now on, labels for that customer are generated with live data whenever they are needed. Packing staff do not edit layouts; they ask for the customer's labels and the format fills in today's product, lot and dates. Pak-Bot can also print documents and labels on request.

What makes a good specification for AI

Why AI generation reduces label errors

It reads every requirement

People skim long specifications. AI reads the whole document, which helps surface requirements buried in a paragraph or a footnote. A person still checks the result, but starts from a format built against the full text.

It connects data, not sample values

A common hand-design mistake is leaving sample text in a field, such as a fixed date or lot copied from the customer's example. A generated format maps each element to Producepak data, so live values are printed.

It makes updates quick

When a customer changes their specification, upload the new version and regenerate or update the format. Every packing station then prints the new version, because formats are stored centrally.

It frees skilled people

The people who know your labels best spend their time checking and approving rather than building from scratch.

Where a human stays in the loop

AI generation is a fast route to an accurate draft. It is not a reason to skip approval. Customers hold suppliers responsible for compliant labels, and only a person can confirm that a format matches what the customer actually expects. Build these checks into every new or changed format:

1. Every mandatory element is present and legible.
2. Every barcode scans, and the scanned data matches the human-readable text.
3. Dates follow the customer's format and rules.
4. The label fits the stock and prints cleanly on the production printer.
5. Placement instructions are passed to the packing line.
6. The approval is recorded with the specification version.

Asking Pak-Bot for labels

Once a customer's format exists, Pak-Bot's ability to print documents and labels on request becomes useful on the packing floor. The Pak-Bot guide says: "I can email and print documents, labels, and generate instant ad hoc reports at any time." Packing staff can ask for a customer's labels for a run in plain language instead of navigating menus.

Pak-Bot also answers questions about inventory, sales, orders and yield, which helps around labelling. Before a run, a supervisor might check what is on order for the customer; after it, how much was packed. The Pak-Bot guide examples include Red onion in poly bags and Walmart orders vs sales Q1 2026.

Security of label specifications

Customer specifications can be confidential. According to the Pak-Bot guide, your data, excluding prompts, is not sent to ChatGPT and always stays on Producepak servers. Pak-Bot also knows which employees can access sales data and which site each employee works at, and filters its answers accordingly. As with any system, limit who can upload and approve label formats to the people responsible for customer compliance.

A rollout plan for AI label formats

Worked example: a supermarket specification

Imagine a supermarket sends a six-page supplier labelling document. It specifies a 100 x 150 mm case label; product name in capitals at a minimum height; the supplier's site number; a GS1-128 barcode encoding GTIN, lot and best-before date; a best-before date set from the pack date; country of origin; and a box in the corner reserved for the retailer's own stamp.

Built by hand, that format would take someone careful time to lay out, with real risk of missing the reserved box or the date rule. With Pak-Bot, the document is uploaded, the format is generated with each element mapped to Producepak data, and the reviewer's job becomes checking: is the reserved area clear, does the barcode include the best-before date, is the minimum text height met, does the test print match? The checking still matters, but the slow building work has gone.

This example is illustrative; every retailer's specification differs.

When a customer changes the specification

Retailers revise supplier manuals, introduce new barcode requirements or change label sizes. When that happens:

1. Get the new version of the specification and note the effective date.
2. Upload it to Pak-Bot and generate an updated format.
3. Compare the new format with the old one to see exactly what changed.
4. Test print, scan and approve.
5. Switch the customer to the new format on the effective date.
6. Retire the old format so it cannot be printed by mistake.

Questions to ask a customer before uploading

• Is this the current version of your labelling specification?
• Which products and pack sizes does it apply to?
• Do you need case labels, pallet labels or both?
• What should the barcode encode?
• How should dates be formatted, and how are best-before or use-by dates calculated?
• Do you need sample labels for approval before the first delivery?

Clear answers make the generated format right first time and avoid rework after the customer reviews samples.

Common mistakes with AI-generated formats

AI labels and the rest of Pak-Bot

Label generation is one of several things Pak-Bot does. It also answers questions about inventory, sales, orders, employees, suppliers and customers, quality control and yield, and it can email and print documents and reports. For a supplier onboarding a new retailer, that means the same assistant can set up the label format, show what the retailer has ordered, and later compare orders with what was shipped. Having these in one place shortens the path from a new customer to a smooth first delivery.

Who should own AI label formats

Label formats sit between sales, packing and quality, and it is easy for nobody to own them. Give one person responsibility for customer label formats: receiving specifications from customers, generating and approving formats with Pak-Bot, keeping the list of approved formats current and retiring old ones. In a small business this may be the office manager; in a larger one, a quality or technical manager. Clear ownership means specification changes are picked up promptly, approvals are recorded and the packing line always prints the current version. It also gives customers one contact for labelling questions, which retailers appreciate when they audit supplier compliance.

AI label generation questions