How I Stopped Manually Copying Data from PDFs
By Bildy Team
The Problem I Didn't Know I Could Solve
Every month, I get about 40 invoices as PDFs. Vendors email them, clients send them, they pile up in my inbox.
My process: Open each PDF. Manually type the invoice number, date, amount, line items into a spreadsheet. Close PDF. Open next PDF. Repeat.
Two hours. Every single month. For two years.
Then someone mentioned AI document extraction. I was skeptical (AI is overhyped for everything), but I tried it.
That two-hour task now takes 15 minutes.
What Changed
Instead of reading each PDF and typing data, I:
1. Define what fields I want (invoice number, date, total, vendor, etc.)
2. Upload PDFs
3. AI reads them and extracts the data
4. Export to CSV, import to my accounting software
Done.
The AI doesn't care if Invoice #1 formats dates as "01/15/2025" and Invoice #2 uses "Jan 15, 2025." It figures it out. The AI doesn't get bored on invoice #35 and start making typos. It extracts data the same way for invoice #1 and invoice #40.
How It Actually Works
You tell the system what data you're looking for. Give each field a name and description:
- Field: invoice_number
Type: text
- Field: total_amount
Type: number
- Field: line_items
Type: array
Upload your document (PDF, Word, Excel, whatever). The AI reads it and pulls out your fields. You get structured data in a clean format.
My Real Use Cases
Invoice Processing
The obvious one. 40 invoices, same fields every time.
I set up my schema once (invoice number, date, vendor, total, line items). Now I just upload PDFs as they arrive.
Before: Two hours of typing
After: 15 minutes of uploading and exporting
Resume Screening
We hired someone recently. Got 60 applications. Most were PDFs.
I needed: Name, email, years of experience, education, key skills.
Created a schema, ran it on all 60 resumes, exported to spreadsheet. Sorted by experience. Identified top candidates in like 20 minutes.
Way better than reading 60 resumes and taking notes.
Contract Dates
I have a dozen active contracts. Each has different start dates, end dates, renewal terms.
Used to track this in a janky spreadsheet I'd update manually whenever I remembered to.
Now: Extract key dates and terms from all contracts, export to CSV, import to my actual tracking system.
Actually know when renewals are coming now. Revolutionary.
The Schema Thing
First time using this, the "schema" concept confused me. Sounds technical. It's not.
A schema is just a list of what data you want, like a form: What's the customer name? What's the invoice number? What's the total? Write those as fields with descriptions and that's your schema. There's a visual builder if you want to click buttons, and there's a JSON editor if you're technical, but they're the same thing. Use whichever feels natural.
Pro tip: The AI can generate a schema for you. Upload a sample document, click "generate schema," and it suggests fields based on what it sees. Then you tweak it. Saves so much time.
What Works (And What Doesn't)
Forms, invoices, resumes, contracts with standard structures, and spreadsheets with clear data all work great. What gets messy is completely unstructured text like novels or essays, handwritten stuff unless it's really clear, super complex multi-column layouts, and scanned documents with terrible quality. If your document has clear structure—sections, labels, consistent formatting—this works. If it's free-form text or a mess, results vary.
It costs credits based on document size. PDFs run 1 credit per page, Word docs are 1 credit per 250 words, and Excel is 1 credit per 50 rows. Before you process anything, it shows the estimated cost so you know what you're paying. For my invoices at usually 1-2 pages each, that's 40-80 credits total, maybe $5-8 worth. For two hours of saved work? Easy decision.
Privacy Matters
All processing happens securely and documents get deleted immediately after extraction. This was huge for me because some of my invoices have sensitive client info and I didn't want them sitting on some server forever. Everything's processed, data extracted, documents deleted. No training AI models with my data, no retention.
Tips From Actually Using This
Write clear descriptions because "name" is vague while "Customer name, usually at the top right" is specific, and the AI uses these descriptions to find the right data. Test on a few samples first—before processing 100 documents, try 3-5 to make sure your schema captures everything you need, then adjust if needed.
Use appropriate data types too. If you're extracting a list like product line items or someone's skills, use "array" type and you'll get properly structured data. Missing fields are okay because not every document has every field. The AI will return "not found" or null, which is fine and better than forcing fake data. Batch process smartly by defining your schema, testing it, then running it on all similar documents instead of reinventing the schema for each PDF.
Here's my invoice workflow now: invoices arrive via email throughout the month, I save them all to a folder, and at the end of the month I open the document extraction tool, load my invoice schema that's saved from last month, upload all PDFs, review the extracted data which is usually fine, export to CSV, import to QuickBooks, and I'm done. From two hours to 15 minutes, plus fewer errors because I'm not the one typing.
When You Wouldn't Use This
If you're only processing one or two documents ever, manual copying is fine and the setup time isn't worth it. If your documents are super inconsistent where every one has a totally different format, this gets annoying because it works best when documents are structurally similar. If you need perfect 100% accuracy with zero errors, you'd still want human review since the AI is very good but not perfect. For everything else though, this is way better than manual data entry.
Don't overthink this. If you regularly copy data from documents into spreadsheets or databases, give this 10 minutes. Grab a document, define 3-5 fields you care about, upload it, and see what you get. If it works, great—you just saved yourself hours. If it doesn't, you wasted 10 minutes and the downside is minimal. I spent two years manually typing invoice data because I didn't know this existed. Don't be me. Try it now.