Unstructured Technologies

Automatically extracts text content from PDF documents while preserving structural information like headings, paragraphs, and formatting. Uses vision models to handle scanned PDFs and complex layouts that traditional text extraction tools fail on.

Identifies and extracts tabular data from PDFs and images, converting tables into structured formats like CSV or JSON. Preserves table relationships and cell content accurately even in complex multi-column layouts.

Allows teams to fine-tune parsing models for specialized document types like medical forms, legal contracts, or industry-specific formats. Improves accuracy on custom document types through training.

Analyzes extracted content to assess quality and identify potential issues like incomplete extraction, OCR errors, or structural problems. Provides confidence scores and validation reports.

Performs optical character recognition on image files and scanned documents to extract readable text. Uses vision models to understand document layout and preserve context beyond simple character recognition.

Automatically breaks down large documents into semantically meaningful chunks optimized for LLM processing and vector database storage. Respects document structure to avoid splitting related content.

Automatically identifies and extracts metadata from documents including title, author, creation date, and document type. Classifies documents into categories based on content and structure.

Analyzes document visual layout including spatial relationships between elements, preserving information about positioning, hierarchy, and visual structure. Maintains context that would be lost in simple text extraction.

Processes multiple documents in bulk through the parsing and extraction pipeline. Handles large-scale document transformation with progress tracking and error handling for production workflows.

Automatically formats extracted and chunked documents for direct ingestion into vector databases. Prepares content with metadata and embeddings-ready structure for RAG systems.

Integrates directly with popular LLM frameworks and prepares extracted document content in formats optimized for language model consumption. Handles context window management and prompt formatting.

Provides open-source library option for running document parsing and extraction on-premises or in private infrastructure. Offers same core capabilities as API but with full control over data and deployment.