The problem
Customers send orders the way they like, a PDF from their own system, a spreadsheet, a line of text in an email. Every line has to be matched to the right item, unit and price in the ERP and keyed before the order can be picked.
The reading is not the hard part. The hard part is that a customer's description or part number rarely matches your item code, and a wrong match ships the wrong goods.
How the pipeline works
The system treats order entry as a data problem first and an AI problem second.
- An agent reads each incoming order, whatever the format, and extracts the customer, lines, quantities and dates.
- Each line is matched to an item in the catalogue, using the customer's own history and cross-references where they exist.
- Checks confirm the customer, units, pack sizes and prices before anything is written to the ERP.
- Lines the checks cannot confirm go to a person with the reason, instead of being guessed.
Accuracy is measured per line against what the team would have keyed, not per order, because one wrong line in an otherwise perfect order is still a wrong delivery.
What changed
At a wholesale distributor, order entry went from a ten-person task to one, about 90 percent less manual work at over 95 percent line accuracy. In a multi-country distribution group the same approach took order entry from five people to two in each country, about 60 percent less manual work across every country in the group.
What a project like this needs from you
Access to the systems the work already lives in, usually a mailbox and the system of record, with a login the agent can use safely. The rules written down, or a person who knows them and has an afternoon to explain them.
A set of real past cases to test against. Replaying last month through a new version before it goes live is what lets a team trust the change. A named owner for the decisions the agent will not make, and a period where it runs alongside the team in shadow mode before it acts on anything.