Cradl AI

Extracts structured data from documents without requiring extensive labeled training datasets or manual template configuration. Uses pre-trained models to identify and pull relevant fields from unseen document types on first encounter.

Processes large volumes of diverse document types in a single workflow without requiring separate pipelines or manual routing. Automatically classifies and extracts data from mixed batches containing invoices, receipts, forms, and other supported document types.

Processes and extracts data from documents in multiple languages without requiring separate language-specific models. Automatically detects document language and applies appropriate extraction rules.

Enables creation of automated workflows for document processing including approval steps, human review queues, and conditional routing based on extracted data. Integrates human-in-the-loop validation for high-value or uncertain extractions.

Exports extracted document data to various formats and systems including CSV, JSON, databases, and accounting software. Supports direct integration with ERP, accounting, and workflow management systems.

Automatically extracts key fields from invoices including line items, amounts, dates, vendor information, and tax details without custom configuration. Handles variations in invoice formats and layouts across different vendors.

Extracts expense-relevant data from receipts and expense documents including merchant name, amount, date, category, and itemization. Supports both digital and scanned receipt formats for expense management workflows.

Identifies and extracts data from form fields across various form types including checkboxes, text fields, signatures, and tables. Works with both printed and digital forms without requiring manual field mapping.

Automatically classifies incoming documents by type and routes them to appropriate processing pipelines or workflows. Identifies document categories from 100+ pre-built types including invoices, POs, customs declarations, and more.

Allows users to train the system on custom document types by providing labeled examples. Enables extraction from proprietary or industry-specific documents not covered by pre-built models.

Provides REST API endpoints for integrating document extraction into existing applications and workflows. Enables programmatic submission of documents and retrieval of extracted data for system-to-system automation.

Evaluates document quality and identifies potential issues that may affect extraction accuracy such as poor image quality, skewed scans, or missing fields. Provides confidence scores for extracted data.

Extracts tabular data and structured information from documents including line items, multi-row tables, and nested data structures. Preserves table relationships and hierarchy during extraction.