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What is Zuva DocAI?
Effortlessly gather critical information across your organization with remarkable accuracy. Utilize context-aware machine learning models to efficiently pull relevant details from your documents. Our sophisticated classifiers allow you to distinguish among various business document types, such as employee contracts, leases, supply agreements, and more. Quickly identify the language of your documents, including English, Portuguese, German, and others. Furthermore, you can generate and retrieve OCR text and images from over 20 distinct file formats, including emails, Word documents, and PDFs. Take advantage of our extensive library containing more than 1000 pre-built clause and provision models, all designed by our expert team to streamline your initial setup. Zuva DocAI operates on Zuva's proprietary machine learning technology, which is relied upon by top law firms and organizations for its superior accuracy in recognizing, extracting, and analyzing document content. In addition, you are empowered to develop custom AI applications tailored to meet your specific needs, significantly boosting your operational efficiency. This holistic approach ensures that your data management processes are both comprehensive and adaptable.
What is XtractEdge?
Enhance your document management capabilities by adeptly scaling and processing vast quantities of documents across your enterprise. While a one-size-fits-all solution for document extraction, processing, and comprehension may often falter in practical business scenarios, leveraging a specialized platform such as XtractEdge becomes imperative to unlock business potential from your diverse documents. XtractEdge employs advanced AI functionalities, integrating a combination of Machine Learning and Deep Learning strategies, in addition to flexible data management and analytics workflows. This innovative platform systematically organizes complex multi-document data from various parts of the world, making it easily accessible to unveil hidden opportunities within the business landscape. By optimizing the entire document extraction, processing, and understanding process, XtractEdge empowers organizations to hasten their progress toward realizing substantial business benefits and operational efficiencies. Furthermore, such a customized approach allows businesses to quickly adapt to changing data requirements and seize insights with agility, ensuring they remain competitive in a rapidly evolving market.
Integrations Supported
Additional information not provided
Integrations Supported
Additional information not provided
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Zuva
Company Location
Canada
Company Website
zuva.ai/docai/
Company Facts
Organization Name
EdgeVerve
Date Founded
2014
Company Location
India
Company Website
www.edgeverve.com/xtractedge-platform/
Categories and Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction
OCR
Batch Processing
Convert to PDF
ID Scanning
Image Pre-processing
Indexing
Metadata Extraction
Multi-Language
Multiple Output Formats
Text Editor
Zone Selection Tool
Categories and Features
Text Mining
Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering