Single Document Extraction
- Extract specific fields, entities, dates, and attributes from individual documents
- Apply extracted values as tags to organize and manage content
Transform Unstructured Data
Into Actionable Intelligence
Make Content Easier to Find
Add consistent metadata across your content so people and AI can surface the right information faster.
Strengthen Governance at Scale
Extract and label sensitive data across your content to support governance, compliance, and risk management.
Improve AI Accuracy at Lower Cost
Give AI richer business and industry context to deliver more accurate results while using fewer tokens.
Extract Key Data From Individual Documents
Get accurate, structured data from a single document without manual review or complex setup.
- Extract specific fields, values, dates, entities, and attributes
- Select auto-tags to automatically label documents based on extracted data
- Improve search, filtering, and AI results with consistent metadata
Tag Multiple Documents Instantly
Apply consistent tags across related documents at once, making the information within them easier to search, surface, and use with AI.
- Reduce manual effort—select multiple documents and apply tags in one action
- Tag content consistently based on extracted information
- Make it easier to find and synthesize information across large documents and content sets
Bulk Extraction and Tagging
Extract and structure information across large volumes of content to reduce manual work and create richer context for search, workflows, and AI.
- Extract metadata and key information across thousands of files
- Apply consistent tags and classifications at scale
- Turn large amounts of unstructured content into usable data and actionable context
Apply Industry-Specific Intelligence
Automatically classify specialized content and extract the information that matters to your industry.
- Auto-classify AEC content by document type, including drawings, specifications, RFIs, submittals, and more
- Extract relevant metadata from CAD, BIM, Revit, and other specialized AEC content
- Apply industry-specific metadata to improve search, AI, and downstream workflows
Key Features of Extraction and Tagging
Multi-Document Extraction
- Extract information across related documents to identify key data and relationships
- Bring findings together for review, analysis, and decision-making
Bulk Extraction
- Extract targeted data from large volumes of documents at scale
- Identify and tag sensitive or business-critical information to support governance and compliance
Industry Intelligence
- Extract specialized information from industry-specific documents and formats
- Automate review and analysis of complex content to support business workflows
Standardize Metadata
- Apply consistent extraction logic across every file, eliminating manual errors
- Standardize metadata to make content easier to search, govern, and use with AI
Content Enrichment
- Extract metadata from existing files, including legacy content stored across folders
- Tag new file uploads automatically in monitored folders as they arrive
Extract and Tag Critical Information
Across Industry-Specific Content
Accelerating Cost-Effective
Cloud Workflows
Nimbus Therapeutics Automates CRO Data Ingestion to Accelerate Deployment to Scientists
Success Metrics:
- Decreased resource needs from 3 FTEs to 1 PTE
- >99% of data upload cases were fully automated in 2021
- 71% of data are loaded into database and viewable within 30 minutes of being uploaded to Egnyte from CRO
- 29% of data issues identified and routed to primary contact for QC
"Egnyte is integral to our ability to automate much of our data management, which has made the often complex process of working with external partners much smoother, more reliable, and more efficient."
Honor Health Network Eliminates Paper From Data Capture With Egnyte
Success Metrics:
- 500+ forms and 184,000 submissions per day
- Improved efficiencies for office staff, home health aides and new job applicants
“Egnyte and Jotform Enterprise in combination are such a powerful force for improving our workflows…Things are fast, easy, and secure across the board.”
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Frequently Asked Questions
Have more questions? Contact Sales to get the answer you’re looking for.
How does AI metadata extraction work on unstructured files?
AI metadata extraction uses natural language processing (NLP) and optical character recognition (OCR) to parse unstructured files—such as PDFs, scanned images, contracts, and CAD drawings. It automatically identifies, extracts, and normalizes specific data points (e.g., dates, vendor names, contract terms, or project IDs) and applies them as structured metadata tags to improve file searchability and classification.
What file formats are supported for automated extraction and tagging?
Egnyte supports a wide range of document types and specialized formats. You can extract data from standard business documents (PDFs, Word, Excel, PowerPoint), scanned images, as well as industry-specific content like AEC design files (CAD, BIM, Revit).
Can we define custom tags and metadata schemas for our specific industry?
Yes. You can customize metadata templates and schema rules to match your organization’s tax taxonomy, project workflows, or industry standards. The AI dynamically applies your defined tags across single files or large document repositories.
How does automated metadata tagging improve AI accuracy and cut costs?
By attaching standardized, high-quality metadata to your content, enterprise AI search and RAG (Retrieval-Augmented Generation) systems can instantly filter and locate precise information. This reduces context window overload, minimizes token usage costs, and prevents AI hallucinations by ensuring model responses are grounded in exact, verified business data.
How does Egnyte ensure sensitive data security during extraction?
All extraction and tagging processes inherit Egnyte’s built-in enterprise security and role-based access controls. Sensitive information—such as PII or confidential financial metrics—can be detected, tagged for governance, and managed without compromising compliance or exposing restricted data to unauthorized users.
What is the difference between AI document classification and metadata extraction?
Document classification identifies the overall type or category of a document (e.g., classifying a file as an NDA, invoice, or AEC blueprint). Metadata extraction pulls specific, structured data fields and entities from within that document (such as key dates, amounts, parties, or drawing numbers) and applies them as tags.
Can metadata extraction handle scanned PDFs, images, and low-quality documents?
Yes. By combining Optical Character Recognition (OCR) with natural language processing (NLP), the system converts visual content in scanned PDFs, drawings, and images into machine-readable text before extracting and tagging key information at scale.
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