In today's digital world, businesses are inundated with data. But there's a catch: an estimated 80% of it is unstructured. This valuable information—hidden in emails, PDFs, customer reviews, support tickets, and web pages—is chaotic, inconsistent, and notoriously difficult to leverage at scale.
Traditional methods like manual data entry are slow and error-prone, while technical solutions like regular expressions (regex) or custom scrapers are brittle and break the moment a website layout changes.
But what if you could turn all that chaos into clean, structured, and developer-ready JSON with a single API call? That's the power of AI-powered data extraction. Tools like extract.do use artificial intelligence to understand the meaning and context of your data, allowing you to pull out precisely what you need, no matter how messy the source.
Let's explore five high-impact use cases where an AI data extraction API can transform your business operations.
The Problem: Your sales and marketing teams interact with hundreds of potential leads through email. Every email signature is a goldmine of contact information—name, title, company, phone number. Manually copying this data into your CRM is a tedious, time-consuming task that salespeople dread.
The AI Solution: You can automate this entire process. By piping email content into a data extraction API, you can instantly pull out key contact details and automatically create or enrich records in your CRM (like Salesforce, HubSpot, or a custom internal system).
Example:
Imagine you receive an email. You can feed its content to extract.do with a simple schema.
import { Do } from '@do-sdk/core';
const emailBody = `
Thanks for the chat.
Best,
--
Sarah Jones
VP of Operations, Globex Corporation
123 Innovation Drive, Techville
Direct: 555-123-4567
sarah.j@globex.com
`;
const leadData = await Do.extract('extract.do', {
text: emailBody,
schema: {
fullName: 'string',
title: 'string',
company: 'string',
phone: 'string',
email: 'email',
}
});
/*
Output:
{
"fullName": "Sarah Jones",
"title": "VP of Operations",
"company": "Globex Corporation",
"phone": "555-123-4567",
"email": "sarah.j@globex.com"
}
*/
The Problem: The accounts payable process is often a bottleneck, relying on staff to manually key in data from countless PDF invoices and receipts. This leads to slow payments, data entry errors, and high processing costs.
The AI Solution: An AI data extraction API can read text from a PDF or a scanned image and intelligently identify and extract critical fields like vendor name, invoice number, total amount, due date, and line items. This structured data can then flow directly into your accounting software, automating reconciliation and payment workflows.
Example:
After converting an invoice PDF to text, you can extract the structured data.
import { Do } from '@do-sdk/core';
const invoiceText = `
INVOICE #INV-8821
To: Acme Co.
From: Supplies R Us
Date: Oct 26, 2023
Due: Nov 25, 2023
Total Amount: $450.75
`;
const invoiceDetails = await Do.extract('extract.do', {
text: invoiceText,
schema: {
invoiceNumber: 'string',
vendorName: 'string',
dueDate: 'date',
totalAmount: 'number',
}
});
/*
Output:
{
"invoiceNumber": "INV-8821",
"vendorName": "Supplies R Us",
"dueDate": "2023-11-25",
"totalAmount": 450.75
}
*/
The Problem: You need to know what people are saying about your brand, competitors, and industry across news articles, blogs, and social media. Manually tracking mentions is impossible, and keyword alerts often lack the necessary context.
The AI Solution: Set up a pipeline that scrapes relevant web pages and feeds the content to an extraction API. You can extract not just the mention of your brand, but also the sentiment (positive, negative, neutral), the author, the key topics discussed, and the source. This gives you a real-time, structured feed of market intelligence.
Example:
Let's analyze a fictional product review.
import { Do } from '@do-sdk/core';
const reviewText = `
"The new Albatross v3 drone from Sky-High Tech is a game-changer.
The battery life is incredible, but I found the mobile app a bit buggy."
- Review by TechGuru on gadget.com
`;
const mentionAnalysis = await Do.extract('extract.do', {
text: reviewText,
schema: {
productName: 'string',
companyName: 'string',
sentiment: 'string', // e.g., "Mixed", "Positive", "Negative"
keyPositive: 'string',
keyNegative: 'string',
}
});
/*
Output:
{
"productName": "Albatross v3",
"companyName": "Sky-High Tech",
"sentiment": "Mixed",
"keyPositive": "incredible battery life",
"keyNegative": "mobile app is a bit buggy"
}
*/
The Problem: A single job posting can attract hundreds of resumes in different formats (PDF, DOCX, etc.). Recruiters spend hours sifting through them to find qualified candidates, manually entering profiles into an Applicant Tracking System (ATS).
The AI Solution: By converting resumes to raw text, an AI API can parse the unstructured content and extract a standardized profile for every applicant. This includes their contact info, work experience (company, title, dates), education, and skills. This allows recruiters to search, filter, and compare candidates with incredible efficiency.
Example:
From a resume text, create a structured candidate profile.
import { Do } from '@do-sdk/core';
const resumeText = `
John Doe - j.doe@email.com
Experience:
2020-Present: Senior Developer at Tech Solutions LLC
2018-2020: Junior Developer at Web Widgets Inc.
Skills: JavaScript, React, Node.js, Python
`;
const candidateProfile = await Do.extract('extract.do', {
text: resumeText,
schema: {
name: 'string',
email: 'email',
skills: ['string'],
experience: [{
title: 'string',
company: 'string',
startDate: 'date'
}]
}
});
/*
Output:
{
"name": "John Doe",
"email": "j.doe@email.com",
"skills": ["JavaScript", "React", "Node.js", "Python"],
"experience": [
{ "title": "Senior Developer", "company": "Tech Solutions LLC", "startDate": "2020-01-01" },
{ "title": "Junior Developer", "company": "Web Widgets Inc.", "startDate": "2018-01-01" }
]
}
*/
The Problem: Financial analysts and researchers often need to aggregate data from non-traditional sources to gain an edge. This could mean extracting data from government filings, news releases, shipping manifests, or product listings to predict market trends or company performance. This data is messy, varied, and hard to normalize.
The AI Solution: An extraction API can be a core component of an alternative data strategy. By pointing it at diverse document types, you can create structured datasets on demand. For example, you could track key executive changes from press releases or extract product specifications from thousands of e-commerce listings to analyze market trends.
Example:
Extracting key information from a company press release.
import { Do } from '@do-sdk/core';
const pressReleaseText = `
FOR IMMEDIATE RELEASE -- GlobeCom Inc. announces today that effective
January 1st, 2024, an quarterly dividend of $0.25 per share will be
issued to all shareholders of record. Furthermore, CFO Bob Smith will be retiring.
`;
const financialEvents = await Do.extract('extract.do', {
text: pressReleaseText,
schema: {
events: [{
eventType: 'string', // e.g. "Dividend Announcement", "Executive Change"
details: 'string'
}]
}
});
/*
Output:
{
"events": [
{ "eventType": "Dividend Announcement", "details": "$0.25 per share" },
{ "eventType": "Executive Change", "details": "CFO Bob Smith retiring" }
]
}
*/
These five examples are just the beginning. The core power of an AI data extraction API like extract.do lies in its flexibility. Unlike brittle scrapers, it doesn't care about HTML tags or layout. You simply provide the raw text and a schema describing what you want. The AI does the heavy lifting, understanding the context to deliver clean, predictable JSON.
Stop letting valuable data sit untapped in documents and websites. Start building more intelligent applications, automating tedious workflows, and unlocking powerful new insights.
Ready to turn data chaos into developer-ready output? Get started with extract.do today.