What Is PDF Data Extraction?

Data extraction goes beyond converting a PDF to text — it identifies structured data (tables, form fields, key-value pairs) and exports it in a machine-readable format you can use directly in a spreadsheet, database, or code pipeline.

What the AI Extracts

• Tables — rows and columns detected by spatial alignment, exported as separate sheets
• Form fields — named fields and their filled values (e.g., Name: John Smith)
• Key-value pairs — invoice fields like Vendor, Date, Total, Line Items
• Lists — bulleted or numbered lists structured as array data in JSON
• Named entities — companies, dates, amounts, addresses identified and labelled

Common Automation Use Cases

• Invoice processing — extract vendor, amount, line items, and due date from supplier invoices; pipe into accounts payable system
• Contract data extraction — extract parties, dates, obligations, and payment terms for contract management tools
• Research data harvesting — extract tables from academic papers or government reports for analysis
• Medical records — extract lab values, medication lists, and diagnosis codes (for authorised healthcare workflows)
• Real estate documents — extract property details, price, parties, and dates from deeds and listing documents

Choosing the Right Export Format

• CSV — best for importing into Excel, Google Sheets, or any spreadsheet. One table per file; multiple tables produce a ZIP.
• JSON — best for developers who want structured data for an API or database pipeline. Preserves nested structures (e.g., line items inside an invoice object).
• XLSX — best when you want structured data with formatting preserved. Multiple tables go into separate sheets.

Building a Python Pipeline with the API

Pro and above users can access our extraction endpoint programmatically. See the API documentation for the endpoint schema and authentication.