Extract Structured Data from Unstructured Text
Authorized data often arrives as messy text—emails, documents, web pages, or notes. This guide shows how to request names, dates, amounts, and other structured fields, then validate the AI output.
Extract Data at Scale
Turn authorized text into draft structured data, process it within plan limits, and review a representative sample before use.
Start ExtractingThe Data Extraction Challenge
Most businesses have valuable data locked in unstructured formats:
- • Customer emails buried in support tickets
- • Order details in free-form text fields
- • Contact information scattered across documents
- • Dates and amounts in invoice notes
How AI Batch Extraction Works
Instead of writing complex regex patterns or manual parsing, you simply tell the AI what you want to extract:
Example: Extracting Contact Information
"Hi, this is John Smith from Acme Corp. You can reach me at john.smith@acme.com or call 555-123-4567. Our meeting is scheduled for March 15th at 2pm."
Extract the following information from the text: - Name - Company - Email - Phone - Date Return as JSON.
{
"name": "John Smith",
"company": "Acme Corp",
"email": "john.smith@acme.com",
"phone": "555-123-4567",
"date": "March 15th"
}Common Extraction Use Cases
Contact Info
Extract emails, phone numbers, and addresses from text you are authorized to process
Financial Data
Pull amounts, dates, invoice numbers from documents
Entities
Identify people, companies, locations, products mentioned
Custom Fields
Extract any specific data pattern you define
Step-by-Step Workflow
Pro Tips
Build a Reviewed Data Extraction Workflow
Use authorized text, validate important fields, and keep human review in the workflow. Free accounts include 20 monthly credits.
Get Started Free