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Description
Microsoft Dynamics 365 Contact Center serves as a comprehensive cloud-based solution for customer service, combining customer engagement, communication, and analytics into a single platform. This system enables businesses to handle customer interactions through various channels, such as voice, chat, email, and social media, thereby delivering a cohesive omnichannel experience. By leveraging AI and automation, the platform boosts agent efficiency through functionalities like sentiment analysis, real-time insights, and guided workflows. Additionally, it seamlessly integrates with other Microsoft products, including Teams and Power BI, to support collaboration and data-driven decision-making. Organizations benefit from customizable dashboards and analytics that allow them to track key performance indicators (KPIs) and refine their customer service approaches. Microsoft Dynamics 365 Contact Center is particularly well-suited for businesses aiming to elevate customer satisfaction and enhance operational efficiency. Ultimately, this robust solution not only improves service delivery but also empowers organizations to adapt to evolving customer needs.
Description
Discover the transformative capabilities of large language models as they redefine Natural Language Processing (NLP) through Spark NLP, an open-source library that empowers users with scalable LLMs. The complete codebase is accessible under the Apache 2.0 license, featuring pre-trained models and comprehensive pipelines. As the sole NLP library designed specifically for Apache Spark, it stands out as the most widely adopted solution in enterprise settings. Spark ML encompasses a variety of machine learning applications that leverage two primary components: estimators and transformers. Estimators possess a method that ensures data is secured and trained for specific applications, while transformers typically result from the fitting process, enabling modifications to the target dataset. These essential components are intricately integrated within Spark NLP, facilitating seamless functionality. Pipelines serve as a powerful mechanism that unites multiple estimators and transformers into a cohesive workflow, enabling a series of interconnected transformations throughout the machine-learning process. This integration not only enhances the efficiency of NLP tasks but also simplifies the overall development experience.
API Access
Has API
API Access
Has API
Integrations
ALBERT
APIFuzzer
Apache Spark
BERT
Conda
Databricks Data Intelligence Platform
Dynamics 365 Customer Service
ELMO
Facebook
Flair
Integrations
ALBERT
APIFuzzer
Apache Spark
BERT
Conda
Databricks Data Intelligence Platform
Dynamics 365 Customer Service
ELMO
Facebook
Flair
Pricing Details
$110/month
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
www.microsoft.com/en-us/dynamics-365/products/contact-center
Vendor Details
Company Name
John Snow Labs
Country
United States
Website
sparknlp.org
Product Features
Call Center
Blended Call Center
Call Logging
Call Recording
Call Scripting
Campaign Management
Database
Escalation Management
IVR / Voice Recognition
Inbound Call Center
Manual Dialer
Outbound Call Center
Predictive Dialer
Progressive Dialer
Queue Management
Real-time Chat
Reporting/Analytics
Product 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