> ## Documentation Index > Fetch the complete documentation index at: https://docs.gp.scale.com/llms.txt > Use this file to discover all available pages before exploring further. # Introduction to Dex **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. *** ## 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 APIโ€”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. *** ## Core Primitives Dex is designed as a capability rather than a standalone product, centered around composable primitives that can be used, extended, and combined: ### 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. ### 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 ### 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 ### 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 *** ## 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 async wrapper for rapid development * **MCP Server:** Model Context Protocol integration for agent-based workflows (coming soon...) *** ## 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. *** ## 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](https://llms.reducto.ai/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. *** ## 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: ๐Ÿ‡ฟ๐Ÿ‡ฆ - Albanian: ๐Ÿ‡ฆ๐Ÿ‡ฑ - Arabic: ๐Ÿ‡ธ๐Ÿ‡ฆ - Armenian: ๐Ÿ‡ฆ๐Ÿ‡ฒ - Belarusian: ๐Ÿ‡ง๐Ÿ‡พ - Bengali: ๐Ÿ‡ง๐Ÿ‡ฉ - Bulgarian: ๐Ÿ‡ง๐Ÿ‡ฌ - Catalan: ๐Ÿ‡ช๐Ÿ‡ธ - Chinese: ๐Ÿ‡จ๐Ÿ‡ณ - Croatian: ๐Ÿ‡ญ๐Ÿ‡ท - Czech: ๐Ÿ‡จ๐Ÿ‡ฟ - Danish: ๐Ÿ‡ฉ๐Ÿ‡ฐ - Dutch: ๐Ÿ‡ณ๐Ÿ‡ฑ - English: ๐Ÿ‡ฌ๐Ÿ‡ง - Estonian: ๐Ÿ‡ช๐Ÿ‡ช - Filipino: ๐Ÿ‡ต๐Ÿ‡ญ - Finnish: ๐Ÿ‡ซ๐Ÿ‡ฎ - French: ๐Ÿ‡ซ๐Ÿ‡ท - German: ๐Ÿ‡ฉ๐Ÿ‡ช - Greek: ๐Ÿ‡ฌ๐Ÿ‡ท - Gujarati: ๐Ÿ‡ฎ๐Ÿ‡ณ - Hebrew: ๐Ÿ‡ฎ๐Ÿ‡ฑ - Hindi: ๐Ÿ‡ฎ๐Ÿ‡ณ - Hungarian: ๐Ÿ‡ญ๐Ÿ‡บ - Icelandic: ๐Ÿ‡ฎ๐Ÿ‡ธ - Indonesian: ๐Ÿ‡ฎ๐Ÿ‡ฉ - Italian: ๐Ÿ‡ฎ๐Ÿ‡น - Japanese: ๐Ÿ‡ฏ๐Ÿ‡ต - Kannada: ๐Ÿ‡ฎ๐Ÿ‡ณ - Khmer: ๐Ÿ‡ฐ๐Ÿ‡ญ - Korean: ๐Ÿ‡ฐ๐Ÿ‡ท - Lao: ๐Ÿ‡ฑ๐Ÿ‡ฆ - Latvian: ๐Ÿ‡ฑ๐Ÿ‡ป - Lithuanian: ๐Ÿ‡ฑ๐Ÿ‡น - Macedonian: ๐Ÿ‡ฒ๐Ÿ‡ฐ - Malay: ๐Ÿ‡ฒ๐Ÿ‡พ - Malayalam: ๐Ÿ‡ฎ๐Ÿ‡ณ - Marathi: ๐Ÿ‡ฎ๐Ÿ‡ณ - Nepali: ๐Ÿ‡ณ๐Ÿ‡ต - Norwegian: ๐Ÿ‡ณ๐Ÿ‡ด - Persian: ๐Ÿ‡ฎ๐Ÿ‡ท - Polish: ๐Ÿ‡ต๐Ÿ‡ฑ - Portuguese: ๐Ÿ‡ต๐Ÿ‡น - Punjabi: ๐Ÿ‡ฎ๐Ÿ‡ณ - Romanian: ๐Ÿ‡ท๐Ÿ‡ด - Russian: ๐Ÿ‡ท๐Ÿ‡บ - Serbian: ๐Ÿ‡ท๐Ÿ‡ธ - Slovak: ๐Ÿ‡ธ๐Ÿ‡ฐ - Slovenian: ๐Ÿ‡ธ๐Ÿ‡ฎ - Spanish: ๐Ÿ‡ช๐Ÿ‡ธ - Swedish: ๐Ÿ‡ธ๐Ÿ‡ช - Tagalog: ๐Ÿ‡ต๐Ÿ‡ญ - Tamil: ๐Ÿ‡ฎ๐Ÿ‡ณ - Telugu: ๐Ÿ‡ฎ๐Ÿ‡ณ - Thai: ๐Ÿ‡น๐Ÿ‡ญ - Turkish: ๐Ÿ‡น๐Ÿ‡ท - Ukrainian: ๐Ÿ‡บ๐Ÿ‡ฆ - Vietnamese: ๐Ÿ‡ป๐Ÿ‡ณ - Yiddish: ๐Ÿ‡ฎ๐Ÿ‡ฑ *** ## Key Features ### Citations and Traceability Every extracted field can be associated with its source location (page number, bounding box, text snippet), enabling auditability and human review. ### Confidence Scoring Assigns confidence scores to extracted fields based on model outputs, helping you filter and prioritize results for downstream review. ### Flexible OCR Engine Support Choose from multiple OCR engines: * **Iris**: See **[When to choose Iris?](/docs/capabilities/ocr/when-to-choose-iris)**. * **Reducto**: see documentation [here](https://docs.reducto.ai/overview) ### Data Lifecycle Management Configurable retention policies automatically manage the lifecycle of files and processing artifacts, helping you meet compliance requirements and optimize storage costs. *** ## Infrastructure and Deployment ### 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 ### GPU and Hardware Requirements GPU requirements depend on which OCR engine and extraction models you use. **OCR Engine Options:** **Cloud Provider OCR (No provisioned GPU):** * AWS Textract * Azure Vision Read * GCP Vision API **Iris:** * For custom OCR needs with 15+ configurable models. See **[When to choose Iris?](/docs/capabilities/ocr/when-to-choose-iris)**. **Reducto Local OCR (Soon to be deprecated):** * **Small (CPU)**: No GPU. Germanic languages. \~3s/page latency. * **Medium**: A10G GPU. Germanic languages. * **Large**: 8xH100 GPUs. 40+ languages (no Arabic RTL, degraded CJK). **Extraction, Key-Value, Tables:** * Azure OpenAI or Claude via Bedrock (no GPU, recommended) * Hosted H100 (slower auto-scaling) ### Recommended Configuration **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 *** ## Getting Started To begin using Dex, you'll need a Scale account with SGP access. **Quick Links:** * **[Getting Started Guide](/docs/capabilities/document-understanding/getting-started-with-dex)**: Step-by-step tutorial for your first extraction * **[File Management](/docs/capabilities/document-understanding/file-management)**: Upload, pagination, and file types * **[Parse](/docs/capabilities/document-understanding/parse)**: Parse engines and async jobs * **[Chunking](/docs/capabilities/document-understanding/chunking)**: Chunking strategies * **[Vector Stores](/docs/capabilities/document-understanding/vector-stores)**: Semantic search and RAG * **[Extract](/docs/capabilities/document-understanding/extract)**: Data extraction and batch processing * **[Best Practices](/docs/capabilities/document-understanding/best-practices)**: Quick start and optimization * **[API Reference](/docs/capabilities/document-understanding/dex-sdk-api-reference)**: Complete SDK documentation Dex makes document understanding accessible to developers while delivering the power and accuracy required for production applications.