A fintech company processes 2 million documents a month: invoices, receipts, bank statements, contracts and tax forms. Today an offshore team of 80 people keys the data in by hand. The company wants to automate extraction of structured fields: vendor, amounts, line items, dates, tax numbers, payment terms, and route only uncertain cases to humans.
Documents arrive as scanned PDFs of wildly varying quality: crisp digital exports, phone photos taken at an angle, faxes, and 40-page contracts with the important clause on page 31. Layouts differ per vendor and there are tens of thousands of vendors.
Extraction errors cost real money. A wrong amount on an invoice can cause an incorrect payment. The company will accept a lower automation rate in exchange for high precision on what it does automate.
Build the architecture on a canvas: place the components, configure them, connect them into a data flow, and write a short reason for each one. The AI reviewer grades your design against a rubric written specifically for this problem.
Work out the unit economics. At what automation rate does this beat $0.40 per document?
How do you get to 99% precision on amounts when your model is only 96% accurate on them?
Your model says it is 95% confident. What has to be true for that number to be usable for routing?
Minimum 6 components · needs a wide desktop screen