💡 Use Cases

Extract Structured Data from Unstructured Text

Jan 5, 2025 10 min read

Your data is trapped in messy text—emails, documents, web pages, notes. Learn how to extract names, dates, amounts, and any structured information using AI batch processing.

Extract Data at Scale

Turn messy text into structured data. Extract emails, phone numbers, dates, and custom fields from thousands of rows.

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The 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

Input Text

"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."

Prompt

Extract the following information from the text:
- Name
- Company
- Email
- Phone
- Date

Return as JSON.

Output

{
  "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, addresses from any text source

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

Prepare your data: Collect all text containing the information you need into a CSV file

Define what to extract: List the specific fields you want (names, dates, amounts, etc.)

Write the prompt: Describe the extraction task and desired output format

Process in batch: Run the extraction on your entire dataset

Export structured data: Download your extracted data as CSV or JSON

Pro Tips

Be specific: Clearly define the format you want (JSON, CSV, list, etc.)

Handle missing data: Tell the AI what to do when information isn't found

Validate results: Spot-check a sample to ensure extraction accuracy

Iterate: Refine your prompt based on initial results

Extract Data from Thousands of Rows

Turn your unstructured text into actionable data. Start with 20 free extractions.

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🧰 Templates

10 Ready-to-Use Prompts for Data Extraction

📊 Data Processing Tips