AI & Automation
What is Structured Data Extraction?
Structured data extraction converts unstructured source documents (PDFs, emails, images) into organized, machine-readable data with defined fields — like invoice number, vendor name, line items, and totals.
Explanation
Most financial documents are unstructured: a PDF invoice doesn't natively expose its data as a spreadsheet row. Structured data extraction is the process of reading that document and producing the organized data your systems need. The challenge is that the same data (e.g., 'total amount due') appears in different positions, formats, and labels across different documents. AI-based structured data extraction handles this variation by understanding document layout and context rather than relying on fixed positions. The output is clean, validated data ready for ERP entry, reconciliation, or reporting — without manual keying.
How Rima relates
Rima performs structured data extraction across invoices, bank statements, receipts, and financial reports, outputting clean data directly into Excel or your ERP.
Explore data extractionRelated Terms
OCR (Optical Character Recognition)
Technology that converts scanned documents and images into machine-readable text.
AI Document Processing
Using artificial intelligence to automatically extract, classify, and process data from documents.
Unstructured Data
Data that doesn't have a predefined format or organization — like PDFs, emails, and scanned documents.
Data Extraction
The process of retrieving specific data from source documents or systems for further processing.
See it in action
Rima automates the manual document workflows accounting teams spend hours on every week.