Pull invoice numbers, line items, totals, tax amounts, and vendor details from any PDF invoice into structured spreadsheet rows — no templates required
Accounts payable departments process invoices from dozens or hundreds of vendors, and every vendor sends invoices in a different format. A construction company receives invoices from material suppliers, subcontractors, equipment rental firms, and utility companies. A healthcare organization processes invoices from medical supply distributors, pharmaceutical companies, lab services, and facilities management vendors. Each invoice contains the same fundamental data — who is billing, for what, how much, and when payment is due — but presents that data in a completely different visual layout.
The gap between receiving a PDF invoice and having its data in a usable spreadsheet format is where AP teams spend most of their manual effort. An AP clerk processing 200 invoices per week spends roughly 3 to 5 minutes per invoice on data entry: reading the vendor name, typing the invoice number, entering each line item, and verifying the totals. That adds up to 10 to 17 hours per week of pure data entry labor, not counting the time spent correcting transcription errors that inevitably occur during manual keying of dollar amounts, quantities, and part numbers.
Lido uses AI to extract structured data from invoice PDFs regardless of format, vendor, or layout. Upload a stack of invoices from any combination of vendors and get a clean spreadsheet with every field captured: invoice number, date, due date, vendor details, line items with quantities and prices, tax amounts, and totals. The AI reads each invoice independently without templates, so adding a new vendor requires zero configuration. Start with 50 free pages.
Line item tables with variable structure. The line item section is the most complex part of an invoice to extract. Some invoices list items in a clean table with clear column headers: description, quantity, unit price, amount. Others use a condensed format where quantities and prices are embedded within the description text. Some invoices include additional columns for item codes, units of measure, discount percentages, or tax per line. The number of columns, their order, and their labeling varies between vendors, and the AI must identify the table structure dynamically rather than relying on a fixed column mapping.
Multi-page invoices with continuation pages. Long invoices for large orders or project-based billing frequently span multiple pages. The line item table starts on page one and continues on page two or three, often with repeated column headers and page-specific subtotals. The AI must recognize that these are continuation pages of the same invoice, not separate invoices, and stitch the line items together into a single continuous list. Some vendors also place summary information like taxes, shipping, and grand total only on the final page, which means the extraction must process all pages before producing complete results.
Header-level field placement varies wildly. Invoice number, date, PO reference, payment terms, vendor address, and bill-to address appear in different positions on every vendor's invoice. Some place the invoice number in the upper right, others embed it in a header table, and some include it as part of a reference block in the middle of the page. The AI identifies these fields by their labels and contextual position rather than by absolute coordinates, which is why it works across vendor formats without per-vendor setup.
Invoices from different jurisdictions apply taxes differently. US invoices may show a single sales tax line. Canadian invoices separate GST and PST or show a combined HST. European invoices include VAT with reverse charge mechanisms for cross-border transactions. The AI recognizes these tax patterns and extracts the applicable tax type, rate, and amount. Discount handling is similarly varied: some invoices show early payment discounts as a separate line item, others apply discounts as negative amounts within the line item table, and some show the discount only in the payment terms text. Multi-currency invoices that display amounts in both the billing currency and the vendor's local currency require the extraction to identify which amounts represent the actual billed values.
AI-powered invoice extraction works in three phases. First, the document layout analysis phase identifies the visual structure of the invoice: where the header block is, where the line item table starts and ends, and where the totals section appears. This layout understanding is computed independently for each invoice, which is why no templates are needed. The AI recognizes that a block of text in the upper portion containing a date, a number labeled "Invoice," and a company name is the header section, regardless of how that header is visually designed.
Second, the field extraction phase reads specific values from each identified section. From the header, the AI captures invoice number, invoice date, due date, PO number, payment terms, vendor name, vendor address, and billing or shipping addresses. From the line item table, it captures each row with its description, quantity, unit price, and amount, plus any additional columns like item codes or tax per line. From the totals section, it captures subtotal, tax, shipping, discounts, and the grand total. Each extracted value is validated against expected formats: dates look like dates, currency amounts contain numbers, and line item totals equal quantity times unit price.
Third, the structuring phase maps all extracted data into spreadsheet columns. Each invoice becomes one or more rows in the output, depending on whether you want header-level data (one row per invoice) or line-item-level data (one row per line item with header fields repeated). This output structure is immediately usable for import into accounting systems, ERP platforms, and AP automation workflows. For organizations also extracting data from other PDF document types, the same AI handles receipts, purchase orders, and statements with the same template-free approach.
AP departments processing hundreds of invoices per month benefit most from batch extraction. Upload an entire folder of vendor invoices received during a billing cycle and get a single consolidated spreadsheet in return. The AI processes each invoice independently, handles the format differences between vendors automatically, and produces a unified output where every invoice's data follows the same column structure. This eliminates the per-invoice manual effort entirely and reduces the invoice processing cycle from days to minutes. Discrepancies between extracted data and expected values are flagged for review rather than silently accepted, which means the downstream accounting data is actually more accurate than manual entry, not just faster.
Construction and project-based billing. Construction companies receive invoices from material suppliers, subcontractors, and equipment rental companies that reference specific projects, cost codes, and retention amounts. AI extraction captures these project-specific fields alongside standard invoice data, enabling project managers to match invoice charges against budgets and change orders. Progress billing invoices that show percentage-of-completion and previously billed amounts are parsed correctly, with current and cumulative amounts distinguished in the output.
Healthcare and medical supply procurement. Hospitals and clinics process invoices from medical device manufacturers, pharmaceutical distributors, lab supply companies, and facilities vendors. These invoices often include catalog numbers, lot numbers, expiration dates, and contract pricing references that must be captured for compliance and inventory tracking. AI extraction handles these healthcare-specific fields and maps them to columns that integrate with materials management and group purchasing organization reporting systems.
Manufacturing and supply chain. Manufacturing companies receive invoices tied to purchase orders with complex line item structures: raw materials priced by weight, components priced by unit, and services priced by hour. Invoices may reference blanket PO numbers with release numbers, include freight and handling charges as separate lines, and show multiple ship-to locations within a single invoice. The AI captures the full line item detail including units of measure, PO references, and shipping information, producing output that procurement teams can reconcile against purchase orders without manual cross-referencing.
Professional services and consulting. Service-based invoices from law firms, consulting firms, and marketing agencies contain time-based billing with hours, rates, and matter or project references. These invoices are typically text-heavy with narrative descriptions for each time entry rather than simple product descriptions. AI extraction parses time entries, identifies billable hours and rates, and captures matter references and expense reimbursement lines that appear alongside time charges. The output enables clients to analyze spending by matter, timekeeper, and rate category across all their professional service vendors.
Upload invoices from any vendor and get structured spreadsheet data with every field captured accurately
Yes. AI-powered extraction reads multi-page invoice PDFs and captures every line item across all pages, including description, quantity, unit price, and line total. The AI maintains the relationship between header-level fields like invoice number and vendor name and the line items that belong to each invoice. This works regardless of whether the invoice spans two pages or twenty, and handles page breaks that split a single line item across pages.
Layout-agnostic AI reads each invoice independently by identifying field labels and their corresponding values visually, rather than relying on fixed coordinates or templates. This means invoices from different vendors, ERP systems, and billing platforms are all processed with the same engine. A QuickBooks invoice, a SAP-generated PDF, and a custom-designed invoice from a freelancer are all handled without per-vendor configuration.
The AI extracts all standard invoice fields: invoice number, invoice date, due date, PO number, vendor name, vendor address, bill-to and ship-to addresses, payment terms, line item descriptions, quantities, unit prices, line totals, subtotal, tax amounts, shipping charges, discounts, and grand total. Currency symbols and codes are preserved. Custom fields unique to specific vendor invoices are also captured when present.
Yes. Scanned invoice PDFs are processed with OCR-level recognition combined with invoice-specific AI understanding. The AI handles low-resolution scans, skewed pages, faded ink, and stamps or handwritten annotations that appear on paper invoices. For invoices with especially poor scan quality, the AI flags low-confidence fields for human review rather than silently producing incorrect data. Start with 50 free pages to test on your actual invoice scans.
50 free pages. All features included. No credit card required.