Extract Structured Data

Most business documents are fundamentally structured data, dressed up in document formatting. An invoice has fields: vendor, invoice number, date, line items, total, due date. A resume has fields: name, contact, education, work history, skills. A contract has fields: parties, effective date, term, governing law, termination clause. Extracting these fields into structured JSON unlocks automation — you can post the JSON to an accounting system, a CRM, a contract management tool, without manual data entry.

The challenge is that documents are inconsistent. Two invoices from two different vendors use different field labels ('Invoice #' vs 'Invoice No' vs 'No.'), different layouts, different ways of presenting line items. A pure pattern-matching tool would need separate rules for each vendor. An AI-based tool reads the document semantically — it understands that 'Invoice #: INV-1234' and 'No. INV-1234' are both referring to the same field, and extracts the value correctly.

For common document types (invoices, resumes, receipts, contracts), we'll provide ready-made schemas that you can use without configuration. For custom document types, you'll be able to define your own schema by listing the fields you want extracted. The output is clean JSON you can pipe into your downstream systems.