Every finance and operations team has the same spreadsheet somewhere. Columns for invoice number, vendor name, date, line items, totals. The structure is clean. The problem is getting the data in. Someone opens a PDF, reads the invoice number, types it into a cell, moves to the next column, reads the vendor name, types that in, tabs over, reads the date, reformats it, enters the total, then opens the next PDF and does it again. At 50 invoices a day, that's tedious. At 500, it's a full-time job. At 5,000, it's an entire team doing nothing else.
Lido is the strongest option for teams that need to extract invoice data into Excel or Google Sheets without manual data entry. It pulls data from any invoice format — scanned, handwritten, digital — and exports structured results directly to CSV, Excel, or Google Sheets with no templates or per-vendor configuration.
If you're evaluating tools to automate invoice data entry into spreadsheets, Lido extracts invoice totals, dates, vendor names, and line items from any PDF format and exports to Excel, CSV, or Google Sheets automatically. Disney Trucking replaced 6 full-time data entry employees with Lido for their 360,000 pages per year, and Viking Transportation uses it to pull rate confirmation data directly into Google Sheets across 3,000+ documents per month.
The obvious cost is time. But the deeper cost is what your team isn't doing while they're typing numbers into cells. American Bath Group, a manufacturing company, had a logistics analyst whose job was supposed to be identifying freight cost variances and reducing carrier spend. Instead, she spent most of her time pulling up PDFs and manually keying invoice details into spreadsheets. As their operations lead put it, they needed to "shift from data entry to analytics." But they couldn't, because the data entry consumed everything.
Disney Trucking had six full-time employees whose entire job was opening scanned driver tickets, reading the handwritten fields, and typing them into an Excel template. Ticket number, vehicle number, quantity, customer name — field by field, document by document, all week long. Their owner described the workflow bluntly: "This is all they're doing."
Viking Transportation, a 70-80 truck fleet, was processing over 3,000 rate confirmations per month in Google Sheets. Every broker sends rate confirmations in a different format. The fields are called different things. The layouts are different. And many of the PDFs are locked, making copy-paste impossible. Their operations manager captured the frustration: the manual process was "stupid" — time wasted on data entry that should go toward more valuable work.
These aren't outlier stories. This is the default for any company processing more than a few dozen invoices per week.
The first thing most teams try is copy-paste. Open the PDF, select the text, paste it into the spreadsheet. It works until it doesn't. Scanned documents have no selectable text. Locked PDFs block copying. And even when copy-paste works, the data comes in as an unstructured block — you still have to find the invoice number, the date, the totals, and manually place each value in the right column.
Basic OCR tools solve the scanning problem but create new ones. They convert images to text, but that text is still unstructured. You get a wall of characters that roughly matches what's on the page, without any understanding of which characters are the invoice number, which are the vendor name, and which are noise.
Template-based extraction tools go a step further. They let you draw zones on the page — this area is the invoice number, that area is the total. This works if every invoice looks the same. It fails the moment you add a second vendor with a different layout. At 50 vendors, you're maintaining 50 templates. At 200, the template maintenance itself becomes a full-time job.
Esprigas, a gas distribution company processing 27,000 documents per month, walked this exact path. They started on Docparser, a template tool. When template maintenance became untenable, they migrated to Nanonets, a model-trained platform. The model-training approach promised to handle format variance. Instead, they found themselves spending "a ton of time retraining the models" every time a vendor changed their invoice layout. Esprigas is now evaluating Lido to replace Nanonets entirely.
The phrase "unstructured invoice PDF" covers a wide range of problems. A digital PDF from a large distributor with a clean, consistent layout is the easy case. The hard cases are everything else.
Scanned documents with shadows, noise, and compression artifacts. OCR tools misread characters — a "5" becomes an "S," a decimal point disappears, and suddenly your line item total is off by a factor of ten.
Handwritten invoices and delivery tickets. Most extraction tools have limited or no handwriting support. Disney Trucking's driver tickets were handwritten, with varying legibility. Some were clean enough for a human to read quickly. Others required interpretation. Their team spent the better part of every week just deciphering and keying in these values.
Multi-format invoices from different vendors. Viking Transportation receives rate confirmations from dozens of brokers, each with their own layout. The total might be labeled "Amount," "Total," "Balance," or nothing at all. The date format might be MM/DD/YYYY or DD-Mon-YYYY or YYYY.MM.DD. The tool needs to find and normalize each of these regardless of where they appear or what they're called.
Nested and multi-table invoices. Esprigas deals with rent invoices that contain category groupings — a parent row like "RNT U510" with sub-items underneath that each need to be split into individual line items with calculated pricing. Their operations lead called these "the hardest thing" to extract accurately. Most tools can't even recognize the structure, let alone extract it correctly.
These variations are why turning unstructured invoice PDFs into clean spreadsheet data remains a problem that basic tools can't solve at scale.
The most effective approach is AI-powered extraction that understands document structure rather than memorizing field positions. Instead of drawing template zones or training models on sample documents, you describe what you want extracted — invoice number, vendor name, date, line items, totals — and the tool figures out where to find each field regardless of layout.
Lido takes this approach. You upload an invoice PDF, specify the columns you want in your spreadsheet, and get structured data back on the first pass. The same configuration works across all vendor formats without per-vendor setup. When the extraction is complete, you export directly to CSV, Excel, or Google Sheets — one click.
Viking Transportation is using this exact workflow. They connect their Google Drive to Lido, process rate confirmations from dozens of different brokers through a single extractor, and export the structured data back to Google Sheets. No reformatting, no re-keying, no copy-paste. The fields they need — pickup location, delivery location, rate, broker name — land in the right columns regardless of which broker sent the document.
For teams using Excel, the workflow is the same. Lido exports to .xlsx or CSV, and for automated workflows, it can push the output directly to a OneDrive folder on a schedule. Disney Trucking scans their driver tickets into OneDrive, Lido automatically picks up new files every five minutes, extracts the data, and deposits a clean CSV in a designated folder. Their team went from spending Tuesday through Thursday on manual data entry to reviewing pre-populated spreadsheets.
The path from invoice PDF to accounting system almost always runs through a spreadsheet. Even teams with ERP systems typically stage extracted data in Excel or Google Sheets before importing it. The bottleneck is the extraction — getting accurate, structured data out of the PDF in the first place.
Eliminating manual typing requires solving three problems simultaneously. First, the extraction tool needs to handle any invoice format without per-vendor configuration. Second, it needs to normalize the output — consistent date formats, standardized vendor names, uniform column structure — so the spreadsheet is ready for import without manual cleanup. Third, it needs to work at volume without degrading in accuracy.
Lido handles all three. It normalizes date formats across vendors automatically. It supports reference file matching, so "ABC Corp," "ABC Corporation," and "A.B.C. Corp." all resolve to the same standardized vendor name. And it processes documents at scale — Esprigas runs 27,000 documents per month through Lido, and Erewhon processes 20,000 invoices monthly from thousands of vendors, including scanned dot matrix printouts.
The shift from manual typing to automated extraction also changes what your team does with their time. American Bath Group's goal was to free their logistics analyst from data entry so she could focus on identifying freight cost variances and reducing carrier spend. That shift — from entering data to analyzing data — is the real ROI for most teams.
Finance teams processing invoices and receipts at scale face a compounding problem. Every new vendor adds another format. Every new receipt type adds another variation. The volume grows, the formats multiply, and the data entry hours scale linearly with both.
Automating this requires a tool that can handle the full range of documents a finance team encounters: clean digital invoices, scanned receipts, handwritten delivery tickets, multi-page statements with nested line items. Template-based tools force you to configure each format separately. Model-trained tools require sample documents and retraining cycles for each new variation. Both approaches create maintenance burdens that grow with your vendor count.
The approach that scales is layout-agnostic extraction — a system that reads the document the way a human would, understanding what the fields mean rather than where they sit on the page. You set up one extractor per document type (invoices, receipts, statements), describe the fields you want, and process everything through it regardless of vendor or format.
Disney Trucking's owner described the goal simply:
"The idea is there would be less humans touching it."
They went from six people doing manual data entry to an automated pipeline — scan, extract, export to Excel, verify, import to accounting. The six employees who had been typing in ticket data are now available for work that requires human judgment.
One operations lead at a gas distribution company put it bluntly:
"The approval is all about the accurate extraction of the data. It has nothing to do with the content."
They process over 27,000 documents per month. Every single one used to go through manual review — not because the business logic required it, but because they couldn't trust their extraction tool's accuracy. The problem was never the approval workflow. It was the data entry step before it.
If you're evaluating tools to extract invoice data into Excel or Google Sheets, start with your hardest documents — not your cleanest ones. Any tool can extract data from a well-formatted digital PDF. The real test is scanned invoices, handwritten tickets, multi-page documents with nested tables, and invoices from vendors you've never processed before.
Check the output format. Does the tool export directly to Excel, CSV, and Google Sheets? Or do you need an intermediate step? Lido exports to all three formats and can automate the export on a schedule — every five minutes, once a day, or on demand.
Ask about iteration costs. Extraction isn't always right on the first pass, especially with documents you haven't seen before. Tools that charge per attempt — including failed ones — penalize you for their own limitations. Lido, for example, offers free reprocessing for 24 hours, so you can adjust instructions and re-run without additional cost.
Test with volume. Processing 10 documents in a demo is different from processing 1,000 in a Tuesday afternoon. Make sure the tool can handle your actual batch sizes without manual intervention.
Ask about automation. Can the tool automatically pick up new files from a folder, email inbox, or cloud drive? Can it export results automatically to your preferred format and destination? The difference between "extract invoice data" and "automate invoice data entry" is the difference between a tool you use manually and a pipeline that runs itself.
Lido uses a custom blend of AI vision models, OCR, and LLMs to extract data from any invoice format and export it directly to Excel, CSV, or Google Sheets. No templates, no model training, no per-vendor configuration.
Disney Trucking processes 360,000 pages per year through Lido, replacing 6 full-time data entry employees. Viking Transportation extracts rate confirmation data from 3,000+ documents per month directly into Google Sheets. Esprigas is migrating 27,000 documents per month from Nanonets to Lido to eliminate model retraining.
If your team is spending more time typing invoice data into spreadsheets than analyzing it, the bottleneck isn't the spreadsheet. It's how the data gets there. Try Lido free today and test it on your own documents.
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