Vendors send invoices with merged cells. Clients submit onboarding forms in their own layout. Every Excel file is different. AI reads them all and outputs clean, structured data. No templates. No per-format rules.
Merged cells, irregular layouts, multi-sheet workbooks — upload the file that breaks your current process. Structured data in seconds, no setup required.
No templates. No training data. No per-format configuration.
Merged cells, multi-sheet workbooks, logos in headers, free-text fields mixed with numbers. The AI reads file content by meaning, not cell address. Upload an Excel file the same way you'd upload a PDF.
Define the fields you need once (vendor name, invoice number, line items, totals). The AI finds them in any Excel layout. When a sender changes their format, the extraction adapts automatically.
Structured output goes to Excel, CSV, Google Sheets, or via API directly into NetSuite, SAP, QuickBooks, or Xero. Webhook support for Zapier, Make, and custom automations.
“What used to take us 20 hours each week now takes just 30 seconds per document. We handle more accounts with fewer reps.”
Finance and AP teams processing high volumes of Excel-format invoices and purchase orders have reduced manual data entry by 80–90% after switching to AI-powered extraction.
“We get invoices from 60+ vendors, every one in a different Excel format. Used to take two people a full day to rekey everything into NetSuite. Now it runs automatically and we just review the flagged exceptions.”
“Every new client sends us their data in a different Excel layout. Onboarding used to take three days just to normalize everything. Now we upload the file and the data comes back structured in minutes.”
“Our procurement team processes about 400 purchase orders a week in Excel. Merged cells, different column orders, summary rows that break every parser we tried. This was the first tool that actually handled all of them.”
Last updated: June 2026
The Excel files that arrive in real business workflows bear little resemblance to tidy spreadsheets. They are invoices where header cells span multiple columns, purchase orders with line items beginning at different rows in every file, and onboarding forms that each sender has arranged in their own idiosyncratic way.
Conventional tools expect data already organized into consistent rows and columns. Power Query transforms structured inputs. VBA macros operate on predetermined layouts. VLOOKUP references specific cell addresses. Every one of these methods fails as soon as the layout shifts or a new vendor submits a file structured differently.
AI-powered extraction works on a different principle. Rather than looking at cell B7, it looks for the invoice number. It processes Excel files the same way it handles scanned PDFs: locating fields by their semantic meaning instead of their position. Merged cells, floating text boxes, and cross-sheet references pose no obstacle because the AI comprehends the document's content rather than navigating its grid coordinates.
This is the identical technology already deployed by finance and operations teams for PDF invoices, scanned receipts, and photographed documents. Excel files represent just another input type. The extraction pipeline processes them uniformly and delivers clean, structured output no matter where the data originated.
Organizations using AI extraction on messy Excel files consistently report an 80–90% reduction in manual data entry, whether they handle vendor invoices, customer onboarding records, or purchase orders arriving from dozens of different suppliers.
For an in-depth comparison of every extraction method available, read the complete guide to Excel data extraction.
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Upload the file to an AI-powered extraction tool like Lido. Define the fields you need (invoice number, line items, totals), and the AI identifies and extracts them regardless of the file's layout, including merged cells, irregular formatting, and multi-sheet workbooks. The structured output exports to Excel, CSV, Google Sheets, or directly to your ERP via API.
Yes. AI extraction tools process the content of the file by meaning, not cell address. Merged cells, which break traditional formulas and scripts, are handled natively because the AI understands what the merged region represents in context (a header, a subtotal label, a multi-line description) and extracts accordingly.
Lido processes .xlsx, .xls, and .csv files with any layout. Merged cells, multi-sheet workbooks, mixed data types, embedded images, irregular formatting, free-text fields mixed with numbers. If a human can read the file, Lido can extract structured data from it. Common inputs include vendor invoices, purchase orders, customer onboarding forms, and financial reports.
Power Query and VBA work on files with known, consistent structures. They rely on fixed cell references and predefined rules. Lido works on files where the layout varies between senders or changes over time by understanding the content semantically. If you receive Excel files from one source that never changes, Power Query is fine. If you receive files from many sources with different layouts, AI extraction is the better approach.
Yes. Connect an email inbox (Gmail or Outlook), and Lido automatically pulls Excel attachments from incoming emails, extracts structured data, and sends it to your target system. This is common for AP teams receiving vendor invoices and procurement teams receiving purchase orders by email.
Lido achieves 99%+ field-level accuracy on standard header fields and line items. Low-confidence extractions are flagged for human review, so your team only touches the exceptions. Unlike manual data entry, AI doesn't get fatigued, skip fields, or transpose digits on the 200th file of the day.
Excel, CSV, Google Sheets, QuickBooks, NetSuite, SAP, Xero, and any system via API or webhook. Data exports in a consistent, structured format regardless of the source file layout. Lido's REST API also returns JSON with confidence scores for each extracted field.
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