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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.
What is Allganize?
Allganize provides exceptional AI solutions aimed at boosting the effectiveness of customer and employee support within organizations. After just four months of deployment, businesses can automate around 72% of their monthly support inquiries, greatly reducing the workload on their teams. Our AI technology is adept at handling simple customer requests, which frees up support agents to tackle more complex issues. Employees can also interact conversationally to retrieve information from diverse document formats. The conversational AI chatbot, which is pre-trained and tailored for your website, optimizes customer service workflows. Moreover, our advanced search capability quickly retrieves accurate answers from any document type, identifying key terms and organizing them to generate valuable insights. The system excels in understanding the context of product reviews by leveraging natural language processing to determine whether customer experiences are positive or negative. Additionally, it categorizes customer support dialogues into specific groups, allowing for a precise understanding of user intent and enhancing service delivery. This holistic approach not only boosts operational efficiency but also significantly improves the overall experience for both customers and employees, ultimately driving business success. As a result, organizations can look forward to a more streamlined and effective support system.
Integrations Supported
Unremot
Azure Marketplace
Facebook
KakaoTalk
LINE
LINE WORKS
Microsoft Teams
Slack
TAS Insight Engine
Integrations Supported
Unremot
Azure Marketplace
Facebook
KakaoTalk
LINE
LINE WORKS
Microsoft Teams
Slack
TAS Insight Engine
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
$2 per month
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
Microsoft
Date Founded
1975
Company Location
United States
Company Website
azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
Company Facts
Organization Name
Allganize
Company Location
United States
Company Website
allganize.ai/
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
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