Freight operations generate a wide range of document types, including purchase orders, invoices, shipping notices, and rosters. Manually detecting, reading, and extracting data from these documents was slow and error-prone, while reconciling information across multiple document types to catch discrepancies proved difficult to manage manually.

• Manual data extraction across document types was slow and error-prone

• Cross-document reconciliation to catch discrepancies was difficult at scale

• Delays in entry and reconciliation slowed freight operations overall