Financial Operations teams manually process a high volume of invoices for departments like Cornell Dining and Retail Services. The invoices are often poor quality (scanned PDFs of varying formats, sometimes handwritten), and disorganized (individual PDFs for each page or multiple invoices in one PDF). Invoice processors must manually combine files, extract key information (invoice number, total), and type it into the FSS system. This project seeks to leverage AI to automate key parts of this workflow, by extracting information from PDF files, validating the data, and presenting structured results in a dashboard for easy review and oversight. The anticipated benefits include hours per day in staff time savings, improved efficiency and accuracy of invoice processing. Downstream, this effort supports quicker invoice processing and payment to vendors, and positive relationships with vendors.