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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 CATMA?

CATMA is a cutting-edge online platform specifically crafted for the purposes of text annotation, analysis, and visualization, designed to mirror the flexible methodologies found in hermeneutic text interpretation. The platform enables users to interact according to their research needs, whether they lean towards qualitative or quantitative techniques, exploratory methods, or structured, taxonomy-based analyses, thereby facilitating both solo and group collaboration. The most recent release, Version 6, brings a wide array of new features, organizing its structure around specific projects that can include various components such as Documents, Annotations, Tagsets, and Members, which significantly improves the organization of collaborative annotation through enhanced roles-and-rights management. Additionally, the analysis capabilities have seen notable upgrades, including improved query options and seamlessly integrated visualizations, along with a repository of standard queries and new visualization tools. The overall user interface has also been thoroughly revamped, resulting in a more efficient and user-friendly experience that optimizes usability. With these enhancements, CATMA continues to meet the diverse requirements of researchers, making text analysis not only more efficient but also more accessible to a wider audience. In this way, CATMA not only aids in academic pursuits but also fosters a collaborative environment for sharing insights.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Webinars

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Iris.ai

Date Founded

2015

Company Location

Norway

Company Website

iris.ai/

Company Facts

Organization Name

CATMA

Date Founded

2009

Company Location

Germany

Company Website

catma.de/

Categories and Features

AI Science

Not specified

Data Extraction

Not specified

Categories and Features

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Multi-Language
Statistical Analysis
Text Analytics

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