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Inside Nutrient

A guide to the invisible work behind documents Introducing Nutrient Documents for Salesforce: Native document generation and signing Document AI vs. traditional OCR: Choosing between OCR, AI, and hybrid pipelines PDF SDK compliance and security evaluation checklist for enterprise teams (2026) Invariant Corp replaces paper processes with Nutrient Workflow and scales without limits What is process mapping? A complete guide Nutrient vs. Conga Composer for Salesforce document generation (2026) Document routing: How to automate document distribution The CTO’s AI playbook: Why accountability architecture beats orchestration Compliance workflow automation: Why built-in compliance is table stakes Workflow diagrams: Examples, symbols, and how to build one that actually runs Digital forms: Replace paper forms with automated workflows Approval workflow software: How to automate approvals Why document-centric automation is different The CEO’s AI playbook: Why decision architecture beats model selection Nutrient SDK product updates for Q1 2026 PDF redaction verification: How to prove sensitive data is permanently removed What is a VPAT? The complete guide to accessibility conformance reports What is PDF/UA? The accessible PDF standard explained Salesforce eSignatures: Generate, sign, and track documents in one flow Online document viewer: Options, tradeoffs, and how to embed one Document viewer for web apps: React, Vue, Angular (2026) Best document viewers in 2026: A buyer’s guide How to edit a PDF in Python: Add text, images, and annotations Nutrient advances Workflow platform with agentic AI for enterprise-grade speed and consistency in document-heavy operations How to create a Salesforce quote template from opportunity data The business case for accessibility: Five ways it drives enterprise value Python PDF library comparison (2026): 7 libraries for developers Why your AI agent hallucinates PDF table data PDF.js limitations: When to upgrade to a commercial PDF SDK
Nutrient Data Extraction API: Turn documents into structu...
Marija Trpkovic · 2026-06-25 · via Inside Nutrient

Table of contents

    Nutrient Data Extraction API: Turn documents into structured data

    Documents contain some of the most important data in a business. But too often, that data is trapped inside PDFs, scans, images, forms, invoices, statements, records, reports, and other hard-to-process files. Many document workflows still depend on manual review, retyping, copy-paste steps, brittle templates, or one-off scripts. These tedious approaches slow teams down and make it more difficult to build truly reliable workflows for automation and AI.

    OCR makes scanned or image-based documents machine-readable, but extracting text is only the first step. Production-grade workflows need structured data. For audit and review, extracted text needs to be traceable back to the source document.

    As organizations bring AI into document-heavy workflows, the challenge is no longer accessing intelligence. It’s making that intelligence reliable enough to act on.

    That’s why we’re introducing Nutrient Data Extraction API. It turns complex documents into structured and auditable data for high-stakes workflows. It helps developers and agents parse documents, extract what matters, preserve source context, validate outputs, and deliver reliable data at scale.

    Screenshot of document parsed into blocks in Nutrient Data Extraction API Studio.

    Going beyond basic OCR

    Nutrient Data Extraction API goes beyond basic OCR by providing confidence scores and linking extracted text back to its source location, which preserves layout information. The result is data that’s easier to validate, audit, and use downstream.

    That means teams can work with documents that include:

    • Scanned text
    • Tables, forms, and key-value pairs
    • Complex layouts
    • Handwriting, formulas, checkboxes, and images
    • Extracted values that need to be traced back to their source location in the document

    Build deterministic document workflows for AI, automation, and human review

    Use Nutrient Data Extraction API to support workflows such as document intake, form and field extraction, table extraction, invoice and statement processing, search indexing, compliance workflows, and human review. It’s designed for teams that need to build document extraction directly into their own products and workflows. That makes the API useful for:

    • AI and data teams preparing documents for RAG, search, and knowledge systems
    • Backend teams automating intake, classification, and processing workflows
    • Product teams embedding document understanding into customer-facing applications
    • Compliance and operations teams that need reviewable, traceable outputs

    Choose the processing mode your workflow needs

    Some documents only require fast text output. Others require OCR-based structure, layout-aware parsing, schema-driven extraction, or source-grounded outputs that can be reviewed and validated.

    Data Extraction includes two APIs:

    • Parse returns the full document content as clean, LLM-ready Markdown or JSON blocks.
    • Extract pulls out specific fields — such as invoice number or total amount — that you define in a schema.

    Choose between multiple processing modes to balance extraction depth, speed, and cost — depending on the document and desired outcome:

    • Fast text mode — Choose it when you need low-cost text extraction from born-digital documents.
    • Structure mode — Use it when you need OCR-based parsing for scanned or image-based documents, including layout, tables, key-value regions, bounds, confidence, and page context.
    • Understand mode — Apply it when you need deeper parsing for complex layouts, handwriting, formulas, OCR correction, and richer document structure.
    • Agentic mode — Use it when you need advanced reasoning, review, and recovery for more complex extraction workflows with AI-guided support.

    This flexibility helps teams build document workflows that match the complexity of the files they process.

    Screenshot of handwritten medical registration form showing parse mode picker in Nutrient Data Extraction API Studio.

    Designed for developers and production teams

    Nutrient Data Extraction API is built for organizations that need to move quickly from evaluation to implementation.

    To test the API and evaluate if it fits your needs, get started with Data Extraction API Studio(opens in a new tab), where you can see structured data extracted from your documents in your browser and define your extraction schema. After that, integrate extraction into your real workflow with the REST API, or see code examples in JavaScript, Python, Java, and PHP.

    Screenshot of invoice data extraction results in Nutrient Data Extraction API Studio, showing extracted fields including document type, field sources, invoice date, payment due date, and payment method.

    Documents in. Reliable data out.

    Nutrient Data Extraction API gives teams an API-first way to move from trapped document data to structured, source-grounded outputs that can power automation, search, AI, compliance, and human review.

    Try Nutrient Data Extraction API for free(opens in a new tab), or view the documentation.

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