In today's data-driven world, businesses are flooded with documents: invoices, receipts, contracts, resumes, and reports. Buried within this unstructured text is a goldmine of valuable information. The challenge? Manually extracting this data is a soul-crushing, error-prone task. Traditional automation using templates and regular expressions is brittle, breaking the moment a layout changes.
What if you could bypass the manual drudgery and the fragile scripts? What if you could convert any document, like a PDF, into clean, developer-ready JSON with a single command?
This isn't a futuristic dream; it's the power of AI-driven data extraction. Let's explore how you can automate document processing and turn data chaos into structured output in moments.
For years, developers have tried to tame unstructured documents with two main tools:
This approach is fundamentally flawed. It's rigid and high-maintenance. If a vendor changes their invoice template slightly—even just moving the date to the right side of the page—the entire parser breaks, requiring a developer to fix it. This simply doesn't scale.
Instead of relying on an exact position or a rigid pattern, modern AI models understand language and context. This is the principle behind extract.do.
extract.do is an AI-powered API that transforms any unstructured text into clean, structured JSON. You don't write parsing rules. You simply provide two things:
The AI handles the rest. It reads the text, understands the context semantically, and intelligently pulls the relevant information to fit your schema.
Let's see it in action. Imagine you have thousands of invoices arriving as PDFs. Your goal is to extract key details to feed into your accounting system.
First, you'll need a library to read the text content from your PDF file. In a Node.js environment, you might use a package like pdf-parse. The output is just a long string of text.
Let's say the extracted text from an invoice PDF looks like this:
Now for the magic. You don't need to tell the system that the invoice number is "after the text 'Invoice #:'". You just tell it you're looking for an invoice number. With extract.do, the code is stunningly simple.
In seconds, the API returns the perfectly structured data you asked for.
This JSON is now ready to be saved to a database, sent to a webhook, or used to power your application's workflow. It's that simple. Even if the next invoice has a completely different layout, the same code will work because the AI adapts to the context.
This technique isn't limited to invoices. You can apply the same principle to virtually any document-based workflow:
If you have a document with valuable data trapped inside, you have a use case for automated data extraction.
The era of building brittle, rule-based document parsers is over. With AI-powered tools like extract.do, developers can finally focus on building applications, not fighting with unstructured data formats.
Ready to turn your document processing from a costly bottleneck into a competitive advantage?
Invoice
Innovate Inc.
123 Tech Lane, Silicon Valley, CA 94043
Bill To: ACME Corp
Invoice #: INV-2023-001
Date: Oct 26, 2023
Due Date: Nov 25, 2023
Description Amount
Cloud Services $500.00
API Usage $150.00
Total Due: $650.00
import { Do } from '@do-sdk/core';
// The unstructured text from your PDF
const invoiceText = `
Invoice
Innovate Inc.
123 Tech Lane, Silicon Valley, CA 94043
Bill To: ACME Corp
Invoice #: INV-2023-001
Date: Oct 26, 2023
Due Date: Nov 25, 2023
Description Amount
Cloud Services $500.00
API Usage $150.00
Total Due: $650.00
`;
// Simply define the data structure you want
const structuredInvoice = await Do.extract('extract.do', {
text: invoiceText,
schema: {
vendorName: 'string',
customerName: 'string',
invoiceNumber: 'string',
invoiceDate: 'date',
totalAmount: 'number',
}
});
console.log(structuredInvoice);
{
"vendorName": "Innovate Inc.",
"customerName": "ACME Corp",
"invoiceNumber": "INV-2023-001",
"invoiceDate": "2023-10-26",
"totalAmount": 650.00
}