PO metadata lands in named fields
Return supplier, buyer, ship-to, payment terms, and PO numbers in a contract your procurement stack can validate.
Purchase orders usually fail after OCR, not during it. The hard part is turning headers, supplier fields, and SKU tables into a stable object that fits procurement and ERP workflows without another cleanup layer.
Extract PO numbers, order dates, supplier metadata, SKUs, quantities, and unit pricing.
Teams usually win by treating purchase orders as structured line-item records, not generic OCR text.
{ "url": "https://example.com/purchase-order.pdf", "file_name": "purchase-order.pdf", "format": "structured", "instructions": "Extract the PO header, supplier block, ship-to details, and line item table.", "schema": { "type": "object", "properties": { "po_number": { "type": "string" }, "supplier_name": { "type": "string" }, "order_date": { "type": "string" }, "currency": { "type": "string" }, "items": { "type": "array" } } }}
Why it works
Purchase orders are usually table-heavy and downstream-sensitive, which makes output shape more important than raw OCR text.
Return supplier, buyer, ship-to, payment terms, and PO numbers in a contract your procurement stack can validate.
Line items, quantities, units, and unit prices can be returned as arrays instead of flattened text fragments.
Markdown stays useful for buyer review while structured output feeds ERP or approval logic.
What you control
The most important fields are the ones that drive approval, receiving, and ERP writeback.
The workflow should return the core document identity and vendor context as stable fields.
The useful handoff is an item array your procurement or finance system can consume directly.
POs often carry ship-to blocks and requested delivery dates that downstream operations still need.
A schema helps teams keep the extracted result aligned with approval and ERP workflows without another translation layer.
Examples
Most teams need either a clean ERP-ready PO object or a buyer review surface for exceptions and approvals.
This is the most common integration path when a purchase order needs to be validated and pushed into another procurement or ERP system.
{ "po_number": "PO-10441", "supplier_name": "Blue Harbor Supply", "order_date": "2026-03-10", "currency": "USD", "items": [ { "sku": "AX-44", "description": "Dock seal kit", "quantity": 12, "unit_price": 48.0 }, { "sku": "QF-17", "description": "Forklift tire", "quantity": 4, "unit_price": 129.5 } ]}
When buyers still need to inspect a supplier submission, markdown provides a reviewable version without breaking the structured workflow.
# Purchase order PO-10441- Supplier: Blue Harbor Supply- Order date: 2026-03-10- Currency: USD## Items| SKU | Description | Qty | Unit price || --- | --- | ---: | ---: || AX-44 | Dock seal kit | 12 | 48.00 || QF-17 | Forklift tire | 4 | 129.50 |
FAQ
Straight answers for teams evaluating how this workflow fits into production.
Yes. Purchase order workflows can return headers and line-item arrays designed for ERP, approval, or receiving systems.
Generic OCR text still leaves you to rebuild the PO object yourself. The value here is returning structured line items and metadata in the shape your workflow expects.
Yes. Markdown can stay available for review while structured JSON powers the downstream write path.
Ready to test
The useful evaluation is whether your PO headers and line items arrive in a contract your procurement stack can trust without another parsing layer.