As a developer, you've faced the chaos. It comes in the form of user-submitted bios, scraped product descriptions, PDF invoices, or raw email text. It's the wild, untamed world of unstructured data, and it's a notorious source of bugs, maintenance headaches, and brittle application logic.
For years, our playbook for taming this chaos involved a patchwork of regular expressions, complex parsing functions, and web scrapers that would break if a target website so much as changed a <div> class. These solutions are not just fragile; they're a tax on development time, demanding constant upkeep.
But what if we could scrap the old playbook? What if you could treat any piece of text—no matter how messy—as a predictable, queryable source? This is the new reality with AI-powered data extraction, a practical approach for building resilient applications that can finally handle the messy reality of text.
The fundamental problem with traditional methods is that they rely on rigid rules to interpret fluid information.
This fragility trickles down, resulting in bad data in your database, broken application features, and a constant cycle of reactive bug fixes. You're not building; you're just patching leaks.
Instead of telling a machine how to find data based on its position or pattern, the modern approach is to tell it what data to find based on its meaning. This is the core principle behind extract.do.
Unlike regex or a traditional web scraping API, an AI model doesn't just see a string of characters. It leverages a deep understanding of language and context. It knows that "Senior Product Manager" is a job title and "jane.smith@innovate.co" is an email, regardless of where they appear in a sentence.
This shift from pattern-matching to semantic understanding makes the extraction process:
So, how does this work in practice? It's shockingly simple. You need just two things: the unstructured text you want to process and a simple schema defining the data you want to get out.
Let's say you want to extract key details from a professional bio.
Step 1: Get your unstructured text. It can be from an email, a document, or a website.
Step 2: Define your desired output and make the API call.
import { Do } from '@do-sdk/core';
// Any unstructured text from documents, emails, or websites
const bio = `
Meet Jane Smith, a Senior Product Manager at Innovate Inc., located in San Francisco.
You can reach her at jane.smith@innovate.co.
`;
// Simply define the data structure you want
const structuredData = await Do.extract('extract.do', {
text: bio,
schema: {
fullName: 'string',
title: 'string',
company: 'string',
city: 'string',
email: 'email',
}
});
console.log(structuredData);
The Result:
{
"fullName": "Jane Smith",
"title": "Senior Product Manager",
"company": "Innovate Inc.",
"city": "San Francisco",
"email": "jane.smith@innovate.co"
}
That’s it. No regex, no custom parsers. You simply described the clean JSON you wanted, and the AI handled the entire data extraction and data transformation process. This simple, powerful workflow allows you to build robust systems on top of clean, predictable data inputs.
With a reliable way to structure any text, you can build more powerful and dependable applications.
By putting extract.do at the front of your data pipeline, you ensure that the rest of your application—your database, your business logic, your UI—is fed a consistent stream of clean, developer-ready JSON.
Ready to retire your brittle parsers? Turn data chaos into structured output with extract.do.
extract.do can process virtually any unstructured text source, including raw text, emails, PDFs, Word documents, HTML content from websites, and more. Just provide the text content, and our AI will do the rest.
You provide a simple JSON schema describing the fields and data types you need. The AI uses this schema as a guide to find and structure the relevant information from the source text, requiring no complex rules or templates.
Unlike brittle regex or scrapers that break with layout changes, extract.do uses AI to understand the semantic context of the data. This makes it far more resilient, flexible, and capable of handling varied data formats without manual rule-setting.
Yes. extract.do is built on a scalable architecture designed to handle high-volume, real-time data processing. It's ideal for powering applications, enriching user profiles, or feeding data into your analytics pipelines.