Pull PO numbers, line items, quantities, unit prices, delivery dates, and vendor details from any purchase order PDF into structured spreadsheets
Purchase orders are the backbone of procurement operations, yet the data they contain is frequently trapped in PDF format. Suppliers receive POs as PDF email attachments that need to be entered into their order management systems. Procurement teams archive POs as PDFs but need the data in spreadsheets for spend analysis, vendor performance tracking, and budget reconciliation. Accounts payable departments match incoming invoices against PO data to verify billing accuracy, a process that requires both documents in structured format for comparison.
The manual bottleneck is significant. A mid-size manufacturer issuing 500 purchase orders per month generates 500 PDFs that contain critical line item data: what was ordered, in what quantity, at what price, from which vendor, and for delivery by what date. When that data exists only in PDF format, every downstream process that needs PO data requires someone to open each PDF, find the relevant fields, and type them into a spreadsheet or system. Procurement analysts building spend reports, buyers tracking open PO commitments, and AP clerks matching invoices to POs all depend on this manual data transfer.
Lido extracts structured data from purchase order PDFs using AI that reads PO layouts from any ERP system or custom format. Upload POs from SAP, Oracle, NetSuite, or any other source and get spreadsheet data with every field captured: PO numbers, line items with quantities and prices, delivery dates, vendor details, and totals. No templates, no per-vendor configuration. Start with 50 free pages.
Complex line item structures with part numbers. Purchase order line items contain more structured data than most other business documents. Each line typically includes an item number or sequence, a part number or SKU, a text description, a quantity, a unit of measure (each, case, pound, linear foot, pallet), a unit price, and an extended amount. Some POs add columns for delivery dates per line, cost center or project codes, tax codes, and discount percentages. The AI must identify each column in the line item table regardless of the column order, which varies between ERP systems and organizations. A SAP-generated PO arranges these columns differently than a QuickBooks PO, which arranges them differently than a custom procurement system's output.
Blanket POs and scheduled releases. Many procurement relationships use blanket purchase orders that establish pricing and terms for a commodity or service over a contract period, with individual releases or call-offs against the blanket PO as materials are needed. These releases reference the master PO number along with a release number, and they may contain only the specific items and quantities for that delivery rather than the full item catalog from the blanket agreement. The AI must capture both the blanket PO number and the release number, recognizing that they represent different levels of the procurement hierarchy, so the extracted data can be used to track cumulative spend and remaining commitments against the blanket agreement.
Ship-to locations and split deliveries. Purchase orders for organizations with multiple facilities often specify different ship-to addresses for different line items, or split a single line item's quantity across multiple delivery locations. A 1,000-unit order might specify 400 units to the Chicago warehouse, 350 to the Dallas distribution center, and 250 to the Atlanta facility. The AI must capture these delivery splits as separate entries in the output, associated with the correct line item and quantities, so logistics and receiving teams can track expected deliveries by location.
Purchase orders are frequently amended after initial issuance. Quantities change, delivery dates shift, new line items are added, and prices are renegotiated. Each amendment generates a new version of the PO PDF, often with a revision number and a change history section that documents what was modified. The AI extracts the current revision data while also capturing the revision number, amendment date, and change description when present. This enables procurement teams to build a complete audit trail of PO modifications from the extracted data, which is essential for contract compliance and dispute resolution with vendors.
The extraction process starts by identifying the PO's structural sections: header information (PO number, date, vendor, terms), line item table, and totals. The header section is parsed for all reference fields including PO number, revision number, PO date, buyer name, payment terms, shipping method, FOB point, and both vendor and ship-to addresses. Each field is identified by its label context rather than position, so "PO Number," "Purchase Order #," "Order No.," and "P.O." are all recognized as the same field type regardless of how the issuing system labels it.
Line item extraction identifies the table boundaries and maps column headers to standard field types. The AI determines which column contains part numbers, which contains descriptions, which contains quantities, and so on, even when columns are unlabeled or use abbreviated headers. Each row in the table becomes a row in the output spreadsheet, with header-level fields (PO number, vendor, date) repeated on each line item row for flat-file output. Alternatively, the output can be structured with a header row per PO and nested line items, depending on the downstream system's import requirements.
Validation rules specific to purchase orders catch extraction errors. Line item extended amounts are verified against quantity multiplied by unit price. The sum of line item amounts is compared against the reported subtotal. Tax calculations are cross-checked against the subtotal and applicable tax rate. When a validation fails, the discrepancy is flagged in the output so procurement teams can verify the value rather than importing incorrect data. This validation is especially important for POs with complex pricing structures like tiered volume discounts or contract-specific rates that don't follow simple multiplication.
The structured spreadsheet output from PO extraction is designed for direct import into procurement systems. Column mappings align with standard ERP import formats, so the extracted data can be loaded into SAP, Oracle Procurement, Coupa, Ariba, or Jaggaer without extensive reformatting. For organizations using the extracted data for spend analysis rather than system import, the flat-file format with one row per line item is immediately usable for pivot tables, category analysis, and vendor spend comparisons. For related document extraction needs, the same AI handles invoice PDFs with the same template-free approach, enabling matched PO-to-invoice comparison in a single spreadsheet workflow.
Spend analysis and category management. Procurement teams performing spend analysis need PO data aggregated across vendors, categories, and time periods. When POs exist only as PDFs, building a spend cube requires weeks of manual data collection. AI extraction converts an archive of PO PDFs into a structured dataset where every line item includes vendor, category, part number, quantity, and price. This dataset feeds directly into spend analysis tools or pivot tables, revealing spending patterns, vendor concentration, and pricing trends that inform sourcing strategy and contract negotiations.
Three-way matching for accounts payable. Three-way matching compares the purchase order, the goods receipt, and the vendor invoice to verify that what was ordered, what was received, and what was billed all agree. This process requires PO data in structured format alongside invoice data. Extracting PO line items into a spreadsheet enables automated comparison against invoice line items: matching part numbers, verifying quantities, and checking prices. Discrepancies between the PO price and the invoiced price are flagged immediately rather than discovered during payment review, reducing invoice processing time and preventing overpayments.
Supplier performance tracking. Organizations evaluating supplier performance need to compare PO commitments against actual delivery outcomes. Extracting requested delivery dates and quantities from POs, then comparing them against actual receipt dates and quantities, produces on-time delivery and fill rate metrics by supplier. Without automated PO extraction, building these supplier scorecards requires manually pulling delivery date data from archived PO PDFs, which is so labor-intensive that most organizations only track supplier performance anecdotally rather than systematically.
Budget commitment tracking. Finance and procurement teams need visibility into open PO commitments to manage budgets accurately. Accrual accounting requires that committed but not-yet-invoiced PO amounts be reflected in financial reports. Extracting PO data enables automated calculation of outstanding commitments by cost center, project, or budget category. When new POs are issued, their extracted data is added to the commitment tracking spreadsheet immediately, giving budget owners real-time visibility into how much of their budget is already committed to outstanding purchase orders.
Upload POs from any ERP system or format and get structured spreadsheet data with every field captured
Yes. The AI reads the line item table in each purchase order PDF and extracts item descriptions, part numbers, quantities, units of measure, unit prices, and extended amounts for every line. It handles POs with anywhere from one to hundreds of line items, including multi-page POs where the line item table continues across pages. Column order and labeling varies between organizations, and the AI identifies the correct mapping dynamically without templates.
Blanket POs and standing orders that use release numbers are extracted with both the master PO number and the specific release number as separate fields. The AI recognizes the blanket PO structure where a single PO number covers multiple deliveries over time, each identified by a release or call-off number. Quantities on each release are captured independently, enabling procurement teams to track cumulative quantities against the blanket PO total without manual aggregation.
The AI extracts PO number, PO date, vendor name and address, ship-to and bill-to addresses, requested delivery date, payment terms, shipping method, FOB terms, buyer name, line item details (description, part number, quantity, UOM, unit price, extended amount), subtotal, tax, freight charges, and PO total. For organizations using project or cost codes on purchase orders, those reference fields are also captured and mapped to columns in the output spreadsheet.
Yes. Purchase orders generated by SAP, Oracle, NetSuite, Microsoft Dynamics, Sage, and custom ERP systems each have different PDF layouts and field arrangements. The AI reads each PO independently using visual layout analysis, identifying field labels and their values without relying on fixed positions or templates. A single batch can contain POs from multiple ERP systems and the extraction produces consistent spreadsheet output regardless of the source format.
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