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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 CoVigilAI?

CoVigilAI represents a state-of-the-art innovation in the realm of medical literature monitoring, harnessing the power of artificial intelligence and advanced data analytics to proactively detect and manage adverse drug reactions, ensuring patient safety and compliance with regulatory requirements in real-time. This platform facilitates the efficient tracking of scientific and medical literature by retrieving data from prominent global databases such as PubMed and Embase, and it offers customizable search options that improve the pharmacovigilance literature review process. Furthermore, it conducts thorough evaluations of scientific articles and publications from various regional journals, guaranteeing an extensive oversight of both global and localized literature. With the implementation of sophisticated algorithms, the system categorizes Individual Case Safety Reports (ICSRs) into three distinct classifications: valid, potential, and invalid cases, while its automated key entity recognition feature proficiently pinpoints critical components such as patients, medications, adverse reactions, and pertinent medical events. This comprehensive strategy not only streamlines the monitoring procedure but also considerably boosts the precision and efficiency of drug safety evaluations, contributing to a safer healthcare environment. Ultimately, CoVigilAI serves as an indispensable tool for enhancing drug safety and fostering informed decision-making in the medical field.

Media

Media

Integrations Supported

Embase
PubMed

Integrations Supported

Embase
PubMed

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

CoVigilAI

Company Location

India

Company Website

covigilai.in/

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

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