If you've ever written a web scraper, you know the cycle. You spend hours crafting the perfect CSS selectors or a labyrinth of regex patterns. It works beautifully. Then, a week later, a frontend developer changes a class name, and your entire script shatters. The maintenance nightmare begins. Traditional data extraction is brittle, time-consuming, and fundamentally broken.
But what if you could stop telling your script how to find the data and simply tell it what data you need? This is the promise of AI-powered agents, and it's changing the game for developers, data scientists, and businesses. Welcome to the future of data extraction.
For decades, we've relied on rule-based systems to pull information from unstructured sources:
The core problem is that these methods are tied to the structure of the data, not its meaning. They lack intelligence. When the layout changes, they fail, even if the information is still there.
This is where extract.do comes in. We're moving beyond brittle scripts with a new paradigm: Data as Code.
extract.do leverages AI agents to understand your data requirements. Instead of writing complex parsing logic, you simply describe the data you want and provide a schema for the output. Our agent intelligently analyzes any source—text, documents, images, or entire websites—and returns clean, structured JSON that matches your exact format.
Let's see how simple this is. Imagine you have a block of text and you want to pull out contact information. With extract.do, you don't need regex. You just need to describe what you're looking for.
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:
The AI agent handles the rest, understanding the context to correctly identify and map the name, email, and company to your specified fields.
While extract.do is a powerhouse for modern web scraping, its capabilities go much further. Because it operates on meaning rather than just structure, it can be your go-to tool for any data extraction task.
extract.do is source-agnostic. You can feed it raw text, HTML content, or a URL, and the agent gets to work.
The "Data as Code" philosophy is at the heart of extract.do. By defining your data structures (like the ContactInfo interface) directly in your application's codebase, you gain several powerful advantages over traditional ETL pipelines:
Switching to an AI-powered data extraction workflow offers clear and immediate benefits:
The days of fighting with regex and CSS selectors are over. It's time to build smarter, more resilient data workflows. The AI revolution isn't just coming for data extraction—it's here.
Ready to stop parsing and start extracting? Explore extract.do and run your first AI agent today!