Iris.ai

Iris.ai is an AI-driven research platform that provides researchers with tools and applications to process and make sense of scientific data. It eliminates the arduous task of manually exploring numerous research papers, patents and internal documents by providing researchers with a platform that specializes in their specific field of research, allowing them to concentrate on creating value rather than spending countless hours collating data. Iris.ai has incorporated specialized features such as smart search, reading list analysis, filters, autonomous data extraction and systematizing to create an efficient and accurate research experience for its users. The research platform's AI technology provides a high level of accuracy by adapting to various types of scientific content, making it effortless to use for all research fields. Implementing Iris.ai's platform saves researchers 75% of their time by automating most research processes, significantly enhancing data accuracy and accelerating data analysis. Furthermore, Iris.ai does not require any special training, meaning users can quickly acquaint themselves with the software's various tools and start their research with ease.

TLDR

Iris.ai is an AI-based research platform that offers researchers specialized tools to analyze and process scientific data. This tool is field-specific and offers improved accuracy, faster data organization, and categorization, thereby saving researchers 75% of their time. Iris.ai's various features, including smart search, autonomous data extraction, and filtering, make it a user-friendly platform that requires no special training. The platform enables the user to conduct interdisciplinary research challenges and identify the most meaning-bearing words and data points from various research papers. Iris.ai makes research more efficient, effective, and accurate, allowing the researcher to focus on creating value by interpreting data and coming up with new ideas.

Company Overview

Iris.ai is a comprehensive platform that provides AI tools and applications for researchers to process and make sense of scientific data. Its platform offers advanced features like smart search, smart filters, reading list analysis, auto-generated summaries, and autonomous extraction and systematizing of data. All these tools are field-specific, meaning Iris.ai is specialized in your field of research, which allows it to provide a higher level of accuracy than humans. The platform's AI technology adapts to all types of content, making it easy to use for all research fields.

One of the biggest benefits of using Iris.ai's platform is that it saves researchers 75% of their time by automating the research process. It also significantly improves the accuracy of the data by eliminating the need for human input. The AI technology takes care of the data organization, categorization, and structuring, which makes data analysis faster and more accurate.

Iris.ai does not require any training or labeling, which saves researchers time and makes the platform incredibly user-friendly. The company's main goal is to help humans focus on value creation, which means researchers can spend more time interpreting the data and coming up with new ideas, instead of spending countless hours digging through research materials.

Iris.ai has made it on Fast Company's most innovative companies of 2017 and many other popular publications like Wired and TechCrunch. The company plans to expand further in the AI research field, becoming the go-to platform for researchers who want to streamline their research processes while producing more accurate data.

Features

Specialization of Workspace

Effortless Specialization

Without any human labeling, taxonomies or training, the Iris.ai Workspace can be fully specialized based on your exact field of research. You don't have to exert any effort to achieve this. The Workspace centers on your content, allowing for a wide range of processes that can be completed by you or through collaboration with colleagues.

Addition of a Variety of Datasets

Diverse Data Sets

The Iris.ai Researcher Workspace has a wide range of datasets that you can add to your Workspace. These include research papers, patents, internal research documents, and much more. The addition of these datasets will help you to have a clearer understanding of your research terminology, and help you connect to a live proxy data set relevant to your research.

Content-Based Recommendation Engine

Explore Interdisciplinary

The Iris.ai Explore search is a Content-Based Recommendation Engine that helps you bypass keywords and explore interdisciplinary research challenges. Your starting point is to give the tool a text description of the problem you are attempting to map out or another research paper or document. The engine identifies the most meaning-bearing words in your text and enriches them with contextual synonyms and hypernyms. It then matches your text with the fingerprint of every paper fed into it, including research papers both from open-access and paid content sources.

Filtering based on Information Extracted from Documents

Advanced Filtering

The Iris.ai Workspace has an Advanced Filtering tool that allows you to filter down a document set based on criteria you can explain in a sentence but not put into one keyword. You can easily filter your documents on a Named Entity, a specific Data point, or a Data range. The Advanced Filtering tool identifies the exact information from the documents, and you can use the identified variables to filter down your list.

Automated Extraction of Key Data Points

Efficient Data Extraction

The Iris.ai Workspace has an Extract tool that fetches and links all the key data from scientific and technical documents into a tabular, machine-readable, and systematic format. This tool can do a month's data extraction work in a matter of minutes, and it does so with 90% accuracy. After extracting the text and identifying domain-specific entities, the tool locates the tables and extracts the data, linking the data between the text and table. Furthermore, the engine populates a pre-defined output in an excel sheet or database-like format.

FAQ

What is the Researcher Workspace?

The Researcher Workspace is an all-in-one AI-based research platform that allows science enthusiasts and researchers to start an interdisciplinary search, filter and summarize relevant articles, and extract valuable data points using a single platform. The platform is based on artificial intelligence models, allowing it to understand what users are looking for and find scientific articles that other keyword-based search engines may not.

What are the benefits of using the Researcher Workspace?

The Researcher Workspace provides many benefits to its users, such as saving time by filtering the most relevant articles, simplifying the research process, and extracting valuable data points through AI. The platform is continuously updated and improved based on user feedback, making it a one-stop-shop for science enthusiasts and researchers.

What types of content do the tools work with?

The tools within the Researcher Workspace work with almost any type of machine-readable content. The platform is optimized to work with scientific language such as trade magazines, popular science, regulatory documentation, and internal memos. However, the further away from pure science we get, the less accurate the tools will be.

What is the summarization approach used by Iris.ai?

Iris.ai uses abstractive summarization to generate summaries. This means that the tool captures the context of the text and writes its own summary, unlike extractive summarization approaches that select and arrange passages from the source text to form a summary. The machine-generated summary contains new phrases and sentences that may not appear in the source text and is unbiased by the authors.

How does the AI function in the Researcher Workspace?

The AI functions in different tools on the platform to enhance the research process. In the Explore tool, it extracts the most meaningful words from an article or problem statement and enriches them with contextual synonyms and topic words to build a contextual "fingerprint". This fingerprint is then matched with the content collection or database of scientific text the tool is connected with. The AI similarly functions in the Extract tool to neatly identify various values and entities across text, tables, and graphs. Additionally, the Analyze tool enables quick scanning of datasets to provide topic and concept lists to filter articles.