Cardinal

Product Introduction

1. Cardinal is a developer-first document intelligence API designed to process complex, unstructured documents with high accuracy and structured output. It combines advanced OCR capabilities with layout analysis to extract precise data while preserving spatial relationships through bounding box coordinates. The product specializes in handling messy real-world documents like invoices, receipts, utility bills, and scanned files that traditional OCR tools and LLMs struggle to interpret correctly.
2. The core value lies in delivering production-ready document processing that eliminates manual verification through forensic-level accuracy in text extraction and structural preservation. Unlike basic OCR solutions, Cardinal maintains contextual relationships between data points while avoiding the hallucinations common in LLM-based approaches. This enables developers to build reliable document workflows for mission-critical applications in finance, healthcare, and enterprise operations.

Main Features

1. Split automatically classifies document types and separates multi-page files into logical components using layout patterns and content analysis. This feature handles mixed document batches by detecting boundaries between invoices, contracts, and receipts within a single file. It supports auto-detection of 50+ common document categories out of the box.
2. Extract enables schema-specific data extraction through customizable templates that define required fields, data types, and spatial relationships. The system returns JSON outputs with exact coordinates for each extracted value, maintaining table structures, line items, and annotations from original documents. This supports nested data extraction from complex financial reports and medical forms.
3. Markdown conversion transforms scanned documents and PDFs into clean text while preserving original formatting through semantic segmentation. The feature reconstructs paragraphs, headers, and lists with 98% layout fidelity, making it suitable for RAG implementations and archival systems. It handles right-to-left languages and mixed-language documents through integrated Unicode normalization.

Problems Solved

1. Cardinal addresses the critical gap between unreliable OCR outputs and overconfident LLM hallucinations in document processing pipelines. Traditional solutions fail to maintain structural context for numerical data in tables and forms, leading to mismatched account numbers or financial figures. Manual verification processes that typically consume 40-60% of data entry budgets are eliminated through deterministic extraction.
2. The product serves enterprises in regulated industries requiring audit-proof document processing, including accounting teams processing expense reports, healthcare providers digitizing patient forms, and logistics companies handling shipping manifests. Developers building document automation platforms for financial reconciliation or compliance reporting are primary adopters.
3. Typical use cases include automated utility bill processing with exact line-item extraction, pharmaceutical trial document analysis with signature verification, and supply chain invoice matching that preserves vendor-specific formatting. Energy companies use it to parse complex meter reading sheets with handwritten annotations and overlapping data fields.

Unique Advantages

1. Unlike traditional OCR engines, Cardinal achieves 99.5% field-level accuracy on scanned documents through hybrid image-text analysis that cross-validates optical character recognition with layout semantics. GPT-4-based solutions fail to provide the coordinate-level precision required for legal document processing and financial auditing.
2. The platform introduces forensic signature analysis that verifies handwritten marks against document context, preventing false positives in contract processing. Multi-angle barcode scanning decodes damaged or partially obscured codes through 3D perspective correction unavailable in open-source OCR tools.
3. Competitive advantages include native support for 100+ languages with mixed-script handling, SOC2/HIPAA-compliant on-premises deployment options, and subsecond latency for high-volume processing. The bounding box output format enables downstream applications to implement pixel-perfect data validation interfaces.

Frequently Asked Questions (FAQ)

1. How does Cardinal handle documents with multiple formats in a single file? Cardinal's Split feature automatically detects page-level document types using convolutional neural networks trained on 10M+ documents, segregating invoices from contracts or receipts within multi-page PDFs. Each section is processed through dedicated extraction pipelines while maintaining file structure metadata.
2. What makes Cardinal more accurate than GPT-4 for document processing? Unlike LLMs that hallucinate missing data, Cardinal uses deterministic computer vision models that strictly adhere to document visuals, providing confidence scores for each extracted field. The system cross-references text against layout positioning and neighboring data points to prevent context drift.
3. Does Cardinal support enterprise security requirements? Yes, the platform offers end-to-end encryption, RBAC controls, and audit logging compliant with SOC2 Type II and HIPAA standards. For sensitive workloads, full air-gapped on-premises deployments are available with automatic model update synchronization through secured channels.