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What is Sci-Bot?

Sci-Bot is a cutting-edge research assistant that harnesses artificial intelligence and the resources of Sci-Hub to effectively tackle user questions by delving directly into scientific literature, rather than depending on generalized training data. This system meticulously scans a vast array of full-text academic articles, extracting vital information to create responses that are deeply grounded in current research, frequently including citations and links to the original sources. Users can engage with Sci-Bot via a user-friendly interface that supports multiple languages, as the platform automatically identifies relevant keywords, retrieves scholarly works, and organizes the findings into cohesive answers. In a manner akin to a human researcher, Sci-Bot employs a systematic technique that showcases intermediate searches and references before delivering a definitive response. By focusing on established scientific literature instead of generative outputs, the assistant seeks to reduce the potential for inaccuracies while facilitating direct access to complete documents, allowing users to verify and explore the original research themselves. This methodology not only bolsters the credibility of the information shared but also fosters a more profound engagement with academic studies, encouraging users to develop a critical understanding of the material. Ultimately, Sci-Bot serves as a bridge between users and the wealth of knowledge contained within scientific literature.

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.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Free
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

Sci-Bot

Company Location

United States

Company Website

sci-bot.ru/

Company Facts

Organization Name

Iris.ai

Date Founded

2015

Company Location

Norway

Company Website

iris.ai/

Categories and Features

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

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