Order Entry Without Re-Typing

Signs you need this

• Customer orders arrive by email — as PDFs, spreadsheets or free text — and someone types each one into the ERP.
• Order entry takes up one or more full-time people, and it peaks at the worst moments: month-end, before holidays, after a price change.
• Typing errors surface later as wrong deliveries, credit notes and unhappy customers.
• Customers wait hours or days for an order confirmation, and nobody can say quickly where a given order stands.

The problem

Industrial companies receive orders through many channels at once. Large customers may use EDI, but most still email a PDF generated by their own ERP, fill in an order spreadsheet, or write a few lines in the body of an email — each in its own format, with their own product codes and units. All of it ends up in front of someone who re-types it into the ERP.

Re-typing is slow, error-prone and hard to scale. It ties skilled people to data entry, and when volumes rise or someone is on holiday, confirmations slip. Template-based tools that read one fixed document layout break as soon as a customer changes theirs.

The approach: AI reads, the ERP rules decide

Every channel feeds one intake process. AI reads the documents; your ERP’s data and rules check every order; people handle only the exceptions.

One intake for every channel. Email inboxes, spreadsheet uploads, EDI messages and portal orders arrive in one process, and every order keeps a link to its original document.

AI extraction. For free-form documents — PDFs from customers’ systems, scanned forms, emails — an AI model extracts the customer, items, quantities, requested dates and references, with a confidence level for each field. Structured formats such as EDI or a standard spreadsheet are read directly, without AI.

Validation against the ERP. Every extracted order is checked against your own data: does the customer exist, is the delivery address known, do the product codes and units match — customers’ own codes and units are mapped to yours — are prices and contract terms correct, and is there stock for the requested date?

Straight-through or exception. Orders that pass every check are created in the ERP automatically, and a confirmation goes back to the customer. Anything uncertain — a low-confidence field, an unknown product code, a price that doesn’t match the contract — goes to an exception queue, where a person sees the original document next to the extracted data and approves or corrects it in one step.

Learning from corrections. Corrections are reused: once a customer’s product code has been mapped, it’s recognized the next time. Recurring issues also show which customers could move to a portal or EDI.

How it’s implemented

1. Order channel analysis. Collect a representative sample of real orders by channel and customer, measure volumes and formats, and agree the validation rules and which orders may go through without review.
2. Intake, extraction and the exception queue. Connect the main channels and build extraction and validation against the ERP. At first, every order is reviewed in the exception screen, so accuracy is measured on real orders before anything is automated.
3. Gradual straight-through processing. Orders that consistently pass all checks start flowing into the ERP automatically, customer by customer and format by format, and the remaining channels are added.

Expected outcomes

• Orders entered in minutes instead of hours, and confirmations sent out faster.
• Fewer entry errors, because every order is checked against ERP data before it’s created.
• An order team focused on exceptions and customers rather than typing — and able to absorb peaks.
• Every order traceable to its original document, with a clear view of where each one stands.

Risks and trade-offs

• AI extraction isn’t perfect. That’s why every order is validated against the ERP and uncertain fields go to a person; automation is extended only where accuracy has been proven on real orders.
• The ERP integration is often the hardest part. Older ERPs can have limited interfaces; the integration approach is decided in the first phase, before anything is built.
• Customer formats change. A new template from a customer may need a correction or two before it flows straight through again — the exception queue absorbs this without disruption.
• Order documents contain business data. Processing stays within your environment or EU-hosted services, with access limited to the order team.

The core idea

Order entry doesn’t need people to type — it needs people to judge. Let AI read the documents and your ERP’s rules check them, and keep your team’s attention for the orders that genuinely need it.