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Structured Data Extraction

The process of transforming unstructured document content into organized, machine-readable data with defined fields and formats.

Structured data extraction goes beyond information extraction to produce outputs that conform to a specific schema — a JSON object, a database table, a spreadsheet row — that downstream systems can process without additional parsing. For a contract, structured extraction produces a consistent schema: {"party_a": "...", "party_b": "...", "effective_date": "...", "term_years": "...", "payment_amount": "...", "governing_law": "..."}.

The challenge in structured extraction is handling the enormous variation in how the same information is expressed across documents. "This Agreement shall commence on..." and "The term begins on..." and "Effective as of..." all express the same concept in different ways. Modern LLM-based extraction handles this variation far better than traditional rule-based or template-matching approaches, enabling structured extraction across heterogeneous document sets without requiring document-type-specific templates.

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