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Dex is Scale’s document understanding service that transforms unstructured documents into actionable, structured data. It is a comprehensive platform that combines advanced OCR, natural language processing, and machine learning to extract meaningful information from PDFs, images, spreadsheets, and more.

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Why Use Dex?

Around 80-90% of enterprise data lives within unstructured formats such as PDFs and DOCX files. Dex solves the most common challenges of programmatic document processing:
  • Format Diversity: Process any document type with a single API—business reports, financial documents, legal contracts, healthcare records, and more.
  • Unstructured Data: Convert complex layouts into structured JSON with semantic understanding, including text, tables, charts, and infographics.
  • Quality Variations: Handle scanned, handwritten, and low-quality documents with high accuracy across multiple languages.
  • Scalability: Process thousands of documents efficiently with built-in scalable infrastructure.
  • Flexibility: Choose from multiple OCR engines and customize extraction with your own tools and workflows.

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Core Primitives

Dex is designed as a capability rather than a standalone product, centered around composable primitives that can be used, extended, and combined:

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File Management

Upload, retrieve, and securely store confidential documents. Supports persistent storage with metadata tracking, secure access patterns, and configurable data retention policies for automatic lifecycle management.

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Parse

Convert documents into machine-readable formats using multiple OCR engines. Dex extracts:
  • Plain text in multiple languages (English, Spanish, Arabic, German, and more)
  • Tables including small and large tabular data (up to 500+ rows)
  • Checkboxes for form processing
  • Images and figures with bounding box information
  • Charts for data visualization analysis

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Vector Stores

Vectorize and index parsed documents for semantic search and retrieval. Vector stores enable:
  • Semantic search over document chunks with embedding-based similarity
  • Context management for multi-file processing and large documents
  • Regex search for pattern-based extraction (dates, IDs, emails, etc.)
  • Document summarization for quick overview generation

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Extract

Extract structured data from parse results or document collections using:
  • Custom schemas defined with Pydantic models
  • Natural language prompts to guide extraction
  • Citations that link extracted data to source locations
  • Confidence scores for quality assessment
  • RAG-enhanced extraction using vector store context
  • Agentic extraction with custom MCP tools for advanced workflows

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Ways to Interact with Dex

Dex provides multiple interfaces to support different use cases:
  • REST API: OpenAPI-documented endpoints for direct integration
  • Python SDK: High-level wrapper for rapid development with both sync and async support
  • MCP Server: Model Context Protocol integration for agent-based workflows (coming soon…)

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Common Use Cases

  • Financial Services: Automate invoice processing, tax document analysis, and financial report extraction.
  • Healthcare: Extract patient information from medical records, insurance claims, and healthcare forms.
  • Legal: Analyze contracts, process discovery documents, and extract key clauses and obligations.
  • Business Operations: Process HR documents, supply chain orders, customer service tickets, and business reports.

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Understanding Industry Document Challenges

Different industries face unique document processing challenges based on their document types and layouts. For a comprehensive overview of typical document formats and layout challenges across finance, healthcare, insurance, and legal sectors, see Industry Document Types and Layout Challenges. This guide covers:
  • Finance: SEC filings, research reports, and financial statements with multi-column layouts, complex footnotes, and embedded visualizations
  • Healthcare: Medical records and clinical documentation with handwritten elements, scanned materials, and variable form structures
  • Insurance: Claims forms (CMS-1500, UB-04) combining typed prompts with handwritten responses on poor-quality scans
  • Legal: Contracts and court filings requiring hierarchical structure preservation through complex sections and redlined annotations
Understanding these document-specific challenges can help you optimize your Dex configuration for better extraction accuracy and results.

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Language Support

Dex supports multi-language document processing with good support for germanic languages. For non-germanic, there are 35 languages including but not limited to: Afrikaans: 🇿🇦 - Albanian: 🇦🇱 - Arabic: 🇸🇦 - Armenian: 🇦🇲 - Belarusian: 🇧🇾 - Bengali: 🇧🇩 - Bulgarian: 🇧🇬 - Catalan: 🇪🇸 - Chinese: 🇨🇳 - Croatian: 🇭🇷 - Czech: 🇨🇿 - Danish: 🇩🇰 - Dutch: 🇳🇱 - English: 🇬🇧 - Estonian: 🇪🇪 - Filipino: 🇵🇭 - Finnish: 🇫🇮 - French: 🇫🇷 - German: 🇩🇪 - Greek: 🇬🇷 - Gujarati: 🇮🇳 - Hebrew: 🇮🇱 - Hindi: 🇮🇳 - Hungarian: 🇭🇺 - Icelandic: 🇮🇸 - Indonesian: 🇮🇩 - Italian: 🇮🇹 - Japanese: 🇯🇵 - Kannada: 🇮🇳 - Khmer: 🇰🇭 - Korean: 🇰🇷 - Lao: 🇱🇦 - Latvian: 🇱🇻 - Lithuanian: 🇱🇹 - Macedonian: 🇲🇰 - Malay: 🇲🇾 - Malayalam: 🇮🇳 - Marathi: 🇮🇳 - Nepali: 🇳🇵 - Norwegian: 🇳🇴 - Persian: 🇮🇷 - Polish: 🇵🇱 - Portuguese: 🇵🇹 - Punjabi: 🇮🇳 - Romanian: 🇷🇴 - Russian: 🇷🇺 - Serbian: 🇷🇸 - Slovak: 🇸🇰 - Slovenian: 🇸🇮 - Spanish: 🇪🇸 - Swedish: 🇸🇪 - Tagalog: 🇵🇭 - Tamil: 🇮🇳 - Telugu: 🇮🇳 - Thai: 🇹🇭 - Turkish: 🇹🇷 - Ukrainian: 🇺🇦 - Vietnamese: 🇻🇳 - Yiddish: 🇮🇱

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Key Features

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Citations and Traceability

Every extracted field can be associated with its source location (page number, bounding box, text snippet), enabling auditability and human review.

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Confidence Scoring

Assigns confidence scores to extracted fields based on model outputs, helping you filter and prioritize results for downstream review.

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Flexible OCR Engine Support

Choose from multiple OCR engines:

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Data Lifecycle Management

Configurable retention policies automatically manage the lifecycle of files and processing artifacts, helping you meet compliance requirements and optimize storage costs.

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Infrastructure and Deployment

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Core Components

Dex is deployed as a Kubernetes service with the following components: Application Pods:
  • API Pod: Handles REST API requests for document processing
  • Temporal Worker Pod: Processes document understanding workflows
  • Both use the same Docker image with different entry points
Infrastructure Services:
  • Temporal Server: Workflow orchestration (v1.25.0)
  • Postgres Database: Application data and Temporal state (Postgres 17)
  • Object Storage: S3, MinIO, or Azure Blob Storage for document artifacts
Deployment:
  • Deployed via Helm charts on Kubernetes
  • Supported on AWS, Azure, and GCP
  • Requires namespace and service configuration

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GPU and Hardware Requirements

GPU requirements depend on which OCR engine and extraction models you use. OCR Engine Options: Reducto Local OCR:
  • Small (CPU): No GPU. Germanic languages. ~3s/page latency.
  • Medium: A10G GPU. Germanic languages.
  • Large: 8xH100 GPUs. 40+ languages (no Arabic RTL, degraded CJK).
Cloud Provider OCR (No provisioned GPU):
  • AWS Textract
  • Azure Vision Read
  • GCP Vision API
Iris: Extraction, Key-Value, Tables:
  • Azure OpenAI or Claude via Bedrock (no GPU, recommended)
  • Hosted H100 (slower auto-scaling)
For minimal GPU requirements:
  • OCR: Reducto Small (CPU) or Azure Vision Read
  • Extraction: Azure OpenAI or Claude via AWS Bedrock
  • Infrastructure: Kubernetes cluster with Postgres and object storage

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Getting Started

To begin using Dex, you’ll need a Scale account with SGP access. Quick Links: Dex makes document understanding accessible to developers while delivering the power and accuracy required for production applications.