23 posts tagged with "automation"

Teams often automate PDF forms the same way: watch a cloud folder for new files, download the PDF bytes, then extract field names and values into structured data for sheets, CRMs, or databases. This guide builds that path in Make (Make.com): Dropbox – Watch Files on /Blog Data/Extract Form Data From PDF/, Dropbox – Download a File with the path coming from the trigger, and PDF4me – Extract PDF Form Data with File name and Document mapped from Download. The run output shows Form Data such as customer_name, email, item, quantity, and price for sample_form.pdf.

Make (Make.com) is a common choice when you want visual scenarios: drag modules, connect them, and map fields without writing code. This guide mirrors a working setup: Dropbox fetches an Excel file, PDF4me converts it to PDF, and Dropbox writes the PDF to an Output folder. The screenshots use /Blog Data/Excel to PDF/sample_excel_file.xlsx and /Blog Data/Excel to PDF/Output/. Your paths can differ; keep the same mapping idea—module 1 feeds PDF4me, module 2 feeds the final Upload.

This guide wires a four-node n8n workflow you can test with Execute workflow: pull an .xlsx from Dropbox, run PDF4me – Convert to PDF on the binary payload, then upload the resulting PDF to an output path. The screenshots use /blog data/excel to pdf/new invoice.xlsx as the source and /blog data/excel to pdf/output/Excel to Pdf.pdf as the destination filename. Swap paths and names to match your workspace.

This walkthrough builds a four-node n8n workflow: you run it manually, download a fillable PDF from Dropbox, extract field values with PDF4me – Extract form data from PDF, then upload a small text file back to Dropbox built from expressions (for example, name, email, and price). The sample paths match the screenshots: PDF at /blog data/extract form data from pdf/sample_form.pdf, text output at /blog data/extract form data from pdf/output/Form data.txt. Adjust paths to match your account.

Teams often want a single, reliable PDF after someone saves a spreadsheet to the cloud. This guide builds an automated cloud flow with three actions: Dropbox detects a new file, PDF4me – Convert to PDF turns the workbook into a PDF, and Dropbox – Create file writes the result into an output folder. The walkthrough uses folder /blog data/excel to pdf, sample workbook New Invoice.xlsx, and output path /blog data/excel to pdf/output. Replace those paths with yours, and keep the same wiring.

If you already store filled or fillable PDFs in Dropbox, you do not need to retype what is on the page. This walkthrough wires three steps: you start the flow yourself, Dropbox loads the PDF bytes, and PDF – Extract Form Data (PDF4me) turns AcroForm field values into structured data you can pass to Excel, Dataverse, email, or another API.

The screenshots use Dropbox path /blog data/extract form data from pdf/sample_form.pdf and a PDF4me File Name of Test.pdf. Swap in your folder, file name, and connection—the pattern stays the same.

Upload an invoice PDF to PDF4me, then run it through the AI-Invoice Parser. Two nodes. You get a processInvoiceModel with vendor name, invoice number, totals, line items, bill-to details—everything in a structured format you can map to databases, spreadsheets, or the next workflow step. n8n gives you Schema, Table, and JSON views so you can inspect the output however you like.

One PDF, multiple documents inside—batch scans, mail with separator sheets, or reports with barcode dividers. PDF4me Split PDF by Barcode finds barcodes (QR Code, Code 128, Data Matrix), filters by text (e.g. starts with PDF4me), and splits the file at each match. In n8n, you need just two PDF4me nodes: Upload file to PDF4me to host the PDF, then Split PDF by barcode to cut it into individual files. Execute the workflow and get PDF4me Barcode 1.pdf, PDF4me Barcode 2.pdf, and more—ready to download or pass to the next node.

Drop an invoice PDF into a Dropbox folder—and get a text file with the vendor name, invoice number, and any custom fields you need. No manual typing, no spreadsheets to copy into. PDF4me AI - Invoice Parser reads the PDF and extracts structured data; Create Text File writes it straight into your folder. Three steps, one Zap.

Invoices piling up in cloud storage? PDF4me AI-Invoice Parser pulls vendor name, invoice number, totals, dates, line items—and even custom fields like Store Number or DepartmentCode—from PDF invoices automatically. Wire it into Make: download a file from Dropbox, parse it with AI, and get a structured object ready for Google Sheets, Airtable, databases, or any downstream app. No templates, no manual typing.

Invoices land in email, Dropbox, or SharePoint—and someone has to type the numbers into your system. PDF4me AI - Invoice Parser extracts vendor name, invoice number, amounts, dates, and line items from PDF invoices automatically. Wire it into Power Automate: watch a Dropbox folder, parse each new invoice with AI, and create a SharePoint list item with the extracted data. No manual data entry.

When you scan a stack of documents into one PDF—batch invoices, mail with separator sheets, or reports with barcode dividers—you need each document in its own file. PDF4me’s Split by Barcode action detects barcodes, filters by text (e.g. starts with PDF4me), and splits before, after, or at the barcode page. In Zapier, you can wire this into a three-step Zap: watch a Dropbox folder, split the PDF, and upload the results as individual files or a single ZIP.

Scanned invoices, faxed forms, or screenshots saved as PDFs—they look like documents, but the text is just pixels. You can’t select it, copy it, or feed it into your Zap. The workaround: run OCR first to make the PDF searchable, then extract the text. Here’s a three-step Zap that does exactly that, using Dropbox and PDF4me.

You have PDFs in Dropbox, SharePoint, or email—some are plain text, others are scanned or image-based—and you need the text out in one reliable flow. The catch: image-based PDFs have no selectable text. They need OCR first. Once they're searchable, PDF4me Extract Text and Images pulls everything in one call—no second read, no wasted API credits.

This guide walks you through a flow that handles both: trigger → get file content (Dropbox) → Convert PDF to editable PDF using OCR (for scanned docs) → Extract Text and Images (PDF4me). You get the exact output structure and links to troubleshoot and try the API yourself.

When you scan a stack of documents into one PDF—batch invoices, mail with separator sheets, or reports with barcode dividers—you need each document in its own file. PDF4me’s Split by Barcode module detects barcodes, filters by text (e.g. starts with PDF4me), and splits before, after, or at the barcode page—and Make’s Iterator turns the split results into individual files you can upload to Dropbox, Google Drive, or any storage.

This guide walks you through the exact modules, fields, and values to get from one PDF to many—with each split saved as its own file.

You have a multi-document PDF—batch-scanned invoices, mail with separator sheets, or reports with barcode dividers—and you need each document split into its own file. Doing that manually is tedious. PDF4me Split PDF by Barcode detects barcodes in the PDF, filters by text (e.g. starts with PDF4me), and splits before, after, or at the barcode page. In Power Automate, you wire it up in five steps: trigger → get file content (Dropbox) → Barcode – Split Document by Barcode (PDF4me) → Apply to each → Create file (Dropbox).

This guide is fact-checked against the Power Automate UI and the Split PDF by Barcode action reference. Every parameter, path, and output field matches a real run. At the end, we include the raw response structure and links to try the API interactively.

You have multiple PDFs in a Dropbox folder and want them merged into a single PDF automatically. The catch: PDF4me Merge Multiple PDFs expects an array of file data, but Dropbox Watch Files returns a list of file metadata—and Download a File returns one file at a time. To bridge that gap, you need Iterator and Array Aggregator: the Iterator processes each file one by one, and the Array Aggregator collects those results into the array that Merge expects.

This guide walks through the full Make scenario, with special focus on how the Iterator and Array Aggregator work and how to wire them to the PDF4me Make connector.

You get a mix of PDFs—invoices, contracts, receipts—and you need them sorted by type so the right workflow handles each one. Doing that by hand doesn't scale.

The fix: Define your classification rules on dev.pdf4me.com (using regex or JavaScript expressions), then run the same classification inside Zapier: when a new PDF lands in a folder → PDF4me Classify Document → use the returned Class Name to route or organize. Classification lives in your PDF4me account; Zapier just sends the file and gets back the class.

First, set up your classes on dev.pdf4me.com (e.g. pdf4me_invoice with a regex like invoice(.*)). Then, build the Zap in Zapier: New File in Folder → Classify Document. All steps and screenshots are fact-checked from the PDF4me and Zapier UIs.

You use PDF4me in Zapier to extract data from PDF invoices—but mapping every field from the PDF into your next step can feel unclear. Which action should you use? How do you get vendor name, invoice number, amounts, and dates out of the PDF and into a usable format?

This guide shows a simple, repeatable flow: When a new invoice PDF lands in a Dropbox folder, PDF4me AI - Invoice Parser extracts structured fields (invoice number, vendor name, vendor address, store number, delivery date, subtotal, total, and more). You then upload a text file to the same folder with the extracted data as the file content and the invoice number as the filename. So you get both the original PDF and a small .txt file (e.g. Pdf4me-202503-25041.txt) containing the parsed data—ready for downstream Zaps, spreadsheets, or ERP.

Every step, field name, folder path, and mapping below is fact-checked from the Zapier and Dropbox screenshots. Use it as your step-by-step setup.

You get a mix of PDFs—invoices, contracts, receipts—and you need them sorted by type so the right workflow handles each one. Doing that by hand doesn't scale.

The fix: Define your classification rules on dev.pdf4me.com (using regex or JavaScript expressions), then run the same classification inside n8n: download a PDF (e.g. from Dropbox) → PDF4me Classify Document → use the returned className to route or organize. Classification lives in your PDF4me account; n8n just sends the file and gets back the class.

This guide has two parts. Part 1 is on dev.pdf4me.com: where to go and how to set up your first class (e.g. pdf4me_invoice with a regex like invoice(.*)). Part 2 is on n8n: a three-node workflow (Trigger → Download a file → Classify document) and how to read the result. All steps and screenshots are fact-checked from the PDF4me and n8n UIs.

You get a mix of PDFs—invoices, contracts, receipts—and you need them sorted by type so the right workflow handles each one. Doing that by hand doesn't scale.

The fix: Define your classification rules on dev.pdf4me.com (using regex or JavaScript expressions), then run the same classification inside Power Automate: get a PDF (e.g. from Dropbox) → PDF4me Classify Document → use the returned Class Name to route or organize. Classification lives in your PDF4me account; Power Automate just sends the file and gets back the class.

This guide has two parts. Part 1 is on dev.pdf4me.com: where to go and how to set up your first class (e.g. pdf4me_invoice with a regex like invoice(.*)). Part 2 is on Power Automate: a flow (Get file content using path → PDF - Classify Document) and how to read the result. All steps and screenshots are fact-checked from the PDF4me and Power Automate UIs.

PDFs with plain pages—reports, invoices, contracts—often need consistent branding: your logo, document metadata, page numbers, or legal footers. Doing that by hand for every file doesn't scale.

Here's the approach: A Zapier Zap in three steps: New File in Folder (trigger) → Add HTML Header/Footer (PDF4me) → Upload File. When a new PDF lands in your folder, Zapier sends it to PDF4me, you paste your HTML in the action, and the branded PDF is uploaded back. No code—just configure, map fields, and turn on the Zap.

Result: A plain PDF like sample_3_page.pdf becomes Header_PDF_ZAP.pdf with a professional header showing Document Type, ID, Title, Author, Date, and Revision—ready to share or archive.

Every step name, field, folder path, and mapping below is fact-checked from the Zapier UI in the screenshots. Use this as your setup guide from scratch.

You get a mix of PDFs—invoices, contracts, receipts—and you need them sorted by type so the right workflow handles each one. Doing that by hand doesn’t scale.

The fix: Define your classification rules on PDF4me.com (using regex or JavaScript expressions), then run the same classification inside Make: download a PDF (e.g. from Dropbox) → PDF4me Classify Document → use the returned Class Name to route or organize. Classification lives in your PDF4me account; Make just sends the file and gets back the class.

This guide has two parts. Part 1 is on PDF4me.com: where to go and how to set up your first class (e.g. pdf4me_invoice with a regex like invoice(.*)). Part 2 is on Make: a two-module scenario (Dropbox Download a File → PDF4me Classify Document) and how to read the result. All steps and screenshots are fact-checked from the PDF4me and Make UIs.