Processing purchase orders automatically: how a food distributor sped up its order intake with AI
Every day, purchase orders read manually, supplier by supplier, line by line. Repetitive work that slows down order processing turnaround. AI can solve it, but only if it actually works on the variety of formats every distributor sees daily.
Summary
A food distributor had a challenge every operator in food distribution recognizes. Purchase orders come in every day, in dozens of different formats from just as many suppliers. The data on them, article numbers, quantities, descriptions, delivery addresses, has to be transferred exactly and completely into the order system, because errors cost time and money downstream.
We ran it the way we always do: start pragmatic and build a focused PoC in a short sprint that genuinely validates the core technology. After that, we integrated the AI solution directly into the client’s existing order processing.
The challenge
Purchase orders are the starting point of every order file. PO number, order date, customer details, delivery address, and per order line the article number, quantity, unit, and description. All of it on the document. And all of it has to land correctly in the system.
The problem is the variation. Every supplier has its own layout. Some purchase orders are cleanly structured in a table, others are densely typed with order lines as running text. Some have handwritten additions or corrections. Some carry two article numbers per line (an internal code and a supplier code), others just one. An order processor has to make sense of that variation every day and retype the right fields, for every file, every time.
Three bottlenecks were stacking up. Time-heavy manual data entry for data that already exists digitally. Error-prone extraction of article codes and quantities where a single typo has downstream impact. And a process that scales linearly with volume, while margins in distribution don’t.
The approach: start pragmatic, validate fast, deliver value immediately
Our methodology runs in three phases that flow into each other without seams.
Pragmatic start. A short kick-off to nail down scope, surface edge cases, and lock delivery criteria. Which order formats need to be supported? Which fields are critical and which optional? How do we handle handwritten additions or dual article numbers? When do we call extraction reliable enough?
High-output PoC. A short, intense build sprint. We built an AI extraction engine that automatically reads an incoming purchase order and pulls out the relevant fields: PO number, order date, VAT numbers, customer details, delivery address, delivery date, and per order line the article number, quantity, unit, and description. The PoC was tested on a representative dataset with purchase orders from different suppliers and formats, against criteria locked upfront. End of the sprint: a working prototype, validated against those criteria, and a grounded go/no-go moment.
Straight to production. The AI extraction was integrated directly into the client’s existing order processing. An incoming purchase order is now automatically converted into a structured order file, with customer, delivery address, and all order lines already populated.
The result
A working system that reads purchase orders directly and converts them into a structured order file. Line by line, field by field, automatically populated, integrated into the existing processing flow where the order processor sees the output and can validate where needed.
The difference for the order processors: instead of starting every file from a blank screen, they get a pre-filled baseline. Their attention goes to the exceptions and the commercial context, not to retyping data that’s already on the document.
The impact
Faster turnaround on order processing. The reading work that used to take minutes per order now runs in the background. Order processors validate instead of retype.
Less risk on critical data. Article numbers, quantities, and delivery addresses are extracted consistently, regardless of purchase order layout. One typo downstream is always more expensive than one prevented.
Scalability without matching headcount. Repetitive entry no longer scales with volume. Capacity goes to files where human judgment matters.
AI embedded in existing tooling. Users don’t switch between systems. The order processing stays the working screen, the AI runs underneath.
Why this works for distributors
Food and non-food distribution are high-volume document processing sectors with low tolerance for errors and margins that don’t grow with workload. That makes them an ideal place for AI, provided the solution fits inside existing workflows instead of living next to them.
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