AI on Your Order Data: Extraction, Matching and the Count Underneath
You take one purchase order, one amendment nobody saw, and the arithmetic under four jobs a machine now does on a factory's paperwork: reading it, scoring what it read, matching it to a delivery, and sorting what arrives. Every figure here is that factory's own measurement on its own documents, and the course says so on the page.
Published by Merchandising Academy · First lesson free to read
Course value
What will you be able to do?
Work outcome
You can use an assistant on your own order data without taking its word for anything, tell a real capability from a demonstration, and set the rules under which your team is allowed to act on what it says.
Who it is for
Factory, supplier, brand and buying-office teams.
What you will produce
You run a measurement pass on your own paperwork. You work out a field rate and a document rate for your own extraction. You run a test that asks which document each value came from. You calibrate the one score band you plan to accept without looking. You measure an error rate per supplier and per document shape. And you find a break-even check rate for six jobs.
Learning format
7 lessons · 0 templates · workplace calculations and decisions.
Lessons
- 01A field rate and a document rate are different numbers🔒17 min
- 02Right about every field, wrong about which document🔒17 min
- 03A confidence score is a ranking, not a probability🔒18 min
- 04Tested in documents, failing in money🔒18 min
- 05The class it gets wrong is the class you needed🔒17 min
- 06The failure that arrives with no version number🔒17 min
- 07What to check, what to leave, and the question that decides it🔒16 min