Document AI in Operations: Field Extraction Accuracy, Confidence Thresholds, Straight-Through Processing, Review Queues & Cost per Document

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Compare what the extraction engine read from four invoices with the correct values, flag every field, count the correct fields per field type, work out field accuracy and colour the misreads.
• Multiply seven field accuracies into the expected share of fully correct documents, compare it with the observed share from a 500-document sample, count wrong fields per document and work out how good each field must be for a document target.
• Test five confidence thresholds on 20 checked fields: how many fields would be accepted without a person, how many wrong fields would slip through and which threshold keeps the error rate under the target.
• Mark which documents pass without any human touch, calculate the straight-through rate overall, by document type and by supplier, and turn it into the number of documents a person still has to handle each month.
• Turn documents per day, review shares, fields per document and seconds per field into reviewer hours, and round them up into the number of reviewers needed on an average day and on a busy Monday.
• Summarise a week of exceptions by reason, compare how often each reason occurs with how much time it eats, and turn the average resolution time into a monthly rework cost.
• Build the all-in cost of one automated document from a fictional page price, the platform fee, review labour and exceptions, and compare it with keying the document fully by hand.
• Turn the ramp-up of documents into monthly and cumulative savings, find the month in which the one-off setup cost is paid back and chart the monthly saving for the steering meeting.