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Description
At Iris.ai we have spent the last 6 years building an award-winning AI engine for scientific text understanding. Our algorithms for text similarity, tabular data extraction, domain-specific entity representation learning and entity disambiguation and linking measure up to the best in the world. On top of that, our machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it and also give feedback to the system.
The Iris.ai Researcher Workspace is a flexible tool suite that allows to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.
Description
The simplest method for pulling data points from unstructured text involves simultaneously scanning research documents, prospectuses, and customer feedback to identify, track, and assess significant, user-defined data metrics. You can access over 100 distinct data points to enhance your investment and risk management strategies effectively. By searching and assembling customized datasets from EDGAR and various public or private resources, you can optimize your deal underwriting process. Additionally, this approach can streamline the legal workflows within capital markets and structured finance. Instantly retrieve over 100 data points to help categorize, compare, and collaborate with your clients more effectively. Deconstructing unstructured text from sources like PubMed and clinical trial data allows you to break down information into categories such as diseases, genes, proteins, and symptoms, ensuring that all your research is consolidated in one location. You can incorporate research from any source into your workspaces effortlessly with our convenient Chrome plug-in, which also enables the transformation of digital PDFs into machine-readable formats. Furthermore, you will receive outputs in JSON and HTML formats that include a detailed section hierarchy, as well as the removal of watermarks, multi-level tables, lists, headers, and footers, making your data more accessible and manageable than ever before. This comprehensive solution not only simplifies data extraction but also enhances your overall analytical capabilities.
API Access
Has API
API Access
Has API
Integrations
Google Chrome
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Iris.ai
Founded
2015
Country
Norway
Website
iris.ai/
Vendor Details
Company Name
NLMatics
Founded
2019
Country
United States
Website
www.nlmatics.com
Product 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
Product Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction