Order entry automation agent

A three-step pipeline: a multi-modal generative AI model extracts order and line-item detail from each PDF; a classifier trained on historic orders predicts a unique SKU per line; and historic purchase data fills the remaining fields. Output is produced in an ERP-ready format and routed either to automatic processing or to manual review according to a confidence threshold. In the grounding engagement the pipeline was deployed to a production Azure environment with automated SAP ingestion producing EDI-ready XML.

The value is throughput and accuracy without adding headcount. In the grounding engagement a four-week feasibility study automated 50 percent of customer orders end to end, exceeding its accuracy targets at 50 percent order-detail accuracy against a 40 percent target and 40 percent product-detail accuracy against a 30 percent target. Adopting GPT-4o roughly halved inference costs, and at MVP the pipeline exceeded its field-level targets on the core fields, with delivery date the weaker field.

A history of past orders to train product identification, historic purchase data to fill routine fields, and a connection into the ERP for automated ingestion. Because incoming documents vary, a representative sample of real order formats matters more than a clean one.

Route by confidence: process high-confidence orders automatically and send anything below the threshold to a person, so uncertain orders never post to the ERP unchecked. Set the threshold conservatively at first and tighten it as accuracy proves out. Monitor the weaker fields specifically and keep a person on exceptions.

Orders arrive as unstructured documents that a team rekeys by hand, volumes are rising faster than you can staff, and you have historic orders to learn from and an ERP you can write to. Tolerance for a confidence-based split between automatic and manual handling is important.