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What is DeepAffex?

Our cutting-edge emotional AI platform, DeepAffex, detects, measures, and interprets human emotions expressed through both physiological and psychological indicators. This cloud-hosted solution employs advanced machine learning algorithms to seamlessly process these emotional data, ensuring all computations are efficiently managed within the cloud infrastructure. Developers can leverage our SDK/API to create tailored applications or software solutions, allowing them to access measurement results from the DeepAffex cloud service across multiple devices like smartphones, tablets, computers, kiosks, and web browsers. Furthermore, the process of receiving updates from our cloud service is simple and efficient, enabling users to easily expand their features with new measurement capabilities. The DeepAffex dashboard functions as a web-based management tool specifically crafted for developers and system administrators, offering a secure platform for authorized users to monitor their organization's private accounts within the DeepAffex cloud. With its intuitive design, DeepAffex strives to simplify the integration of affective analysis into diverse applications, promoting a higher level of emotional intelligence in technology. This seamless approach not only enhances user experience but also encourages innovation in the field of emotional AI.

What is Affect-Tag RX?

Assessing emotions can yield critical insights for objective evaluations. By integrating cognitive data into your consumer and user research, you can make decisions that are based on genuine emotional reactions. The Affect-tag RX solution provides reliable and insightful emotional metrics through a secure web service and a customized dashboard. These metrics are obtained from physiological information collected via the Affect-tag RX mobile application and its unique smartband. This tool is designed for efficient data collection, fitting effortlessly into a variety of research methodologies. Participants can fully engage in their experiences without any potential biases interfering with the outcomes. Furthermore, our unique algorithms analyze physiological data in just a few minutes, granting researchers quick access to impactful insights. This rapid processing empowers researchers to respond promptly to the emotional data gathered, thereby enriching the overall research process and fostering a deeper understanding of consumer behavior. Ultimately, leveraging such technology not only streamlines research but also enhances the quality of insights derived from emotional analyses.

Media

Media

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

$1500 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

NuraLogix

Company Location

Canada

Company Website

nuralogix.ai/deep-affex/

Company Facts

Organization Name

Neotrope

Date Founded

2007

Company Location

France

Company Website

affect-tag.com

Categories and Features

Categories and Features

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

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