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What is Iris.ai?
At Iris.ai, we have dedicated the past six years to developing an award-winning AI engine that excels in comprehending scientific texts. Our state-of-the-art algorithms for text similarity, extraction of tabular data, learning domain-specific entity representations, and entity disambiguation and linking rank among the finest globally. Additionally, our machine constructs an extensive knowledge graph that encompasses all entities and their interconnections, enabling users to learn from it, utilize it, and also provide feedback to enhance the system further.
The Iris.ai Researcher Workspace offers a versatile suite of tools that enables users to tackle projects from multiple perspectives. Its modules feature content-driven exploratory searches, analytical assessments of document collections, systematic extraction and organization of data points, automated summarization of various documents, and highly effective filters based on context descriptions, machine analyses, or targeted data points and entities. Furthermore, the Iris.ai engine for scientific text understanding is a robust interdisciplinary platform that can be automatically fine-tuned for specific research domains, allowing for a deeper machine comprehension without the need for human training or annotation, ultimately streamlining the research process.
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.
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
Iris.ai
Date Founded
2015
Company Location
Norway
Company Website
iris.ai/
Company Facts
Organization Name
Neotrope
Date Founded
2007
Company Location
France
Company Website
affect-tag.com
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
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
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
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