Unstructured Data
ConceptsUnstructured data is content without a fixed schema—raw documents such as PDFs, images, Word files, spreadsheets, and plain text. Unlike databases or JSON, unstructured data has no predefined fields or structure. DocLD parses unstructured documents into structured content (text, tables, layout) that can be chunked, embedded, searched, and extracted.
Why Unstructured Data Matters
Most business documents are unstructured: contracts, invoices, resumes, reports. Traditional systems require manual data entry or brittle rules. Document intelligence automates understanding of unstructured data using AI—parsing, OCR, extraction, and RAG.
How DocLD Handles Unstructured Data
| Step | What Happens |
|---|---|
| Parse | Parsing extracts text, tables, and layout from PDFs, images, spreadsheets, presentations |
| OCR | For scanned documents or images, OCR converts pixels to text |
| Chunk | Content is chunked into segments for embedding and search |
| Extract | Extraction pulls structured data using a schema |
| Search | Vector search finds relevant content by meaning |
DocLD supports a wide range of file formats: PDF, images, DOCX, XLSX, PPTX, CSV, HTML, and more.
Best Practices
- Use native formats — Prefer PDFs with embedded text over scanned versions when possible
- Clear images — For OCR, use high-resolution images with readable text
- Define schemas — For extraction, use prebuilt schemas or custom schemas to structure output
- Batch processing — Use batch processing for large volumes of unstructured documents
Related Concepts
Parsing and OCR convert unstructured data into machine-readable content. Extraction pulls structured data using schemas. Document intelligence encompasses the full pipeline for unstructured data.