In today's digital world, we are drowning in data. But not all data is created equal. While neatly organized databases are a dream, the reality is that over 80% of business data is "unstructured"—a chaotic mix of emails, PDFs, customer reviews, web pages, and documents. This messy content holds immense value, but unlocking it has traditionally been a complex, frustrating, and expensive process.
What if you could tame that chaos? This guide provides actionable strategies and introduces powerful tools, like extract.do, to help you turn your messy text, documents, and web content into valuable, structured assets.
For years, developers and data engineers have relied on a fragile toolkit to parse unstructured data:
These methods share a common flaw: they are brittle. When a website redesigns its layout or a document format is updated, your painstakingly crafted parsers break, leading to data loss and engineering fire drills.
Enter the AI Agent. Modern AI has fundamentally changed the game. Instead of telling a program how to find data with specific selectors or patterns, you can now simply tell an AI what data you want.
This is the core principle behind extract.do, an AI-powered platform designed for intelligent data extraction and transformation. It replaces brittle scripts with a simple API call, allowing you to define your data needs as code.
extract.do lets you transform any unstructured text, document, or website into clean, structured JSON. The concept is simple yet powerful: Your data, your format, instantly.
Instead of wrestling with parsers, you give the AI agent three things:
The AI handles the rest.
Let's see how easy it is to extract contact information from a simple string of text. With extract.do, you can do this directly in your application with just a few lines of code.
import { DO } from '@do-inc/sdk';
// Initialize the .do client
const secret = process.env.DO_SECRET;
const digo = new DO({ secret });
// Define the source text and desired data structure
const sourceText = 'Contact John Doe at j.doe@example.com. He is the CEO of Acme Inc.';
interface ContactInfo {
name: string;
email: string;
company: string;
}
// Run the extraction agent
const extractedData = await digo
.agent<ContactInfo>('extract')
.run({
source: sourceText,
description: 'Extract the full name, email, and company from the text.'
});
console.log(extractedData);
// {
// "name": "John Doe",
// "email": "j.doe@example.com",
// "company": "Acme Inc."
// }
In this example, we simply provided the source text and defined a Typescript interface. The extract.do agent intelligently identified the name, email, and company and returned it in the exact format we requested. No regex, no splitting strings, no hassle.
A: extract.do is designed to be source-agnostic. You can provide raw text, HTML content, URLs to websites, or even text from documents and images. The AI agent intelligently parses the content to find the data you need, adapting to the context it's given.
A: You define the output structure by providing a simple JSON schema or, as shown above, a Typescript interface. The AI agent uses this schema to understand precisely what fields to look for (e.g., 'name', 'email', 'invoice_amount') and returns the data in that exact format. This ensures predictable and clean output every time.
While extract.do is exceptional for web scraping, its capabilities go much further. It's a comprehensive tool for any task that requires turning unstructured information into structured data.
Essentially, extract.do replaces brittle, single-purpose scripts with a flexible and intelligent AI agent that understands your goals.
For many modern data tasks, extract.do offers a superior alternative to traditional ETL pipelines.
| Traditional ETL | extract.do (AI Agent) |
|---|---|
| Complex Pipelines: Requires building and connecting multiple stages. | Simple API Call: A single, declarative API call does the work. |
| Brittle & Rigid: Breaks when source format changes. | Resilient & Adaptable: The AI understands context and can adapt to layout changes. |
| High Maintenance: Demands constant monitoring and updates. | Low Maintenance: No parsers to maintain. Just define what you want. |
| Slow to Implement: Weeks or months of development time. | Fast to Implement: Get up and running in minutes. |
The era of fighting with unstructured data is over. With AI-powered tools like extract.do, you can finally focus on using your data, not just fighting to get it. By treating data extraction as a simple declaration in your code, you can build faster, create more resilient systems, and unlock the value hidden in your unstructured content.
Stop wrestling with regex and complex parsers. Visit extract.do and experience the future of intelligent data processing.