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What is InfoNgen?

InfoNgen serves as an advanced text analytics and sentiment analysis platform that efficiently reveals actionable insights from vast amounts of information. By streamlining the process of making strategic decisions, teams can focus on sharing, analyzing, and disseminating only the most essential insights derived from both structured and unstructured data. With its innovative tools for sentiment analysis and text mining, InfoNgen enables users to identify significant patterns, trends, and anomalies within their datasets. Unlike other solutions, InfoNgen uniquely integrates its specialized use cases with robust functionalities, thereby enhancing decision-making capabilities for employees and accelerating their workflow. This powerful platform acts as a vital asset for businesses seeking to locate critical information quickly, featuring pre-built industry taxonomies alongside customizable delivery options to meet diverse needs. Consequently, organizations can leverage InfoNgen not only to improve efficiency but also to stay ahead in a competitive landscape.

What is Azure Text Analytics?

Harness natural language processing to gain valuable insights from unstructured text without requiring any machine learning knowledge, by utilizing an array of features from the Cognitive Services for Language. Elevate your understanding of customer emotions through sentiment analysis and identify key phrases and entities such as people, places, and organizations to uncover common themes and patterns. Use specialized, pretrained models to classify medical terminology specific to various fields. Evaluate text across multiple languages and reveal essential concepts within the content, which include key phrases and named entities that highlight individuals, events, and organizations. Delve into customer feedback regarding your brand while examining sentiments linked to specific topics through opinion mining techniques. Additionally, derive critical insights from unstructured clinical documents, including doctors' notes, electronic health records, and patient intake forms, by applying text analytics tailored for healthcare settings, ultimately enhancing patient care and informing decision-making processes. By integrating these advanced capabilities, organizations can stay ahead of trends and better meet the needs of their stakeholders.

Media

Media

Integrations Supported

AWS Elemental
Azure Marketplace
Dropbox
Google Cloud Document AI
Microsoft OneDrive
Microsoft SharePoint
Microsoft Teams
Salesforce
TAS Insight Engine
Unremot

Integrations Supported

AWS Elemental
Azure Marketplace
Dropbox
Google Cloud Document AI
Microsoft OneDrive
Microsoft SharePoint
Microsoft Teams
Salesforce
TAS Insight Engine
Unremot

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

EPAM Systems

Company Location

United States

Company Website

www.epam.com

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/services/cognitive-services/text-analytics/

Categories and Features

Enterprise Search

AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery

Insight Engines

AI / Machine Learning
Augmented Analytics
Data Aggregation
Data Classification
Data Extraction
Data Source Connectors
Full Text Search
Intent Recognition
Multiple Data Sources
Search / Filter
Sentiment Analysis

Categories and Features

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

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