Iris.ai is an AI-powered tool that revolutionizes the tedious process of conducting literature reviews with its efficient and robust AI algorithms. Founded by a team of scholars with a multi-disciplinary background in AI and previous research experience, Iris.ai simplifies the process of literature review for researchers of all levels.
The tool assists researchers by analyzing academic papers for contextual relevance to generate a comprehensive list of relevant papers, saving hours of exhaustive searches. The company's mission is to democratize access to knowledge by reducing the time and costs researchers must invest in conducting literature reviews.
The Iris.ai tool is an essential resource for researchers worldwide who need a reliable, efficient, and fast way to conduct literature reviews without requiring expert knowledge of specific fields. Iris.ai's innovative solutions and user-friendly platform make it a standout tool in the academic research landscape, empowering researchers to focus on their core research interests, leaving the challenges of literature review to a reliable AI tool that streamlines the process, saves time and effort, and provides more options than traditional searches. Since the launch, Iris.ai has attracted clients worldwide, and its cutting-edge technology has earned recognition for its vital contribution to academic research.
TLDR
Iris.ai is an AI-powered tool that simplifies the process of literature reviews for researchers of all levels. With its robust AI algorithms, the tool reduces the time and effort required by researchers to conduct large-scale systematic reviews by analyzing academic papers for contextual relevance. Iris.ai generates a comprehensive list of relevant papers, saving researchers hours of exhaustive searches.
The company's mission is to democratize access to knowledge by reducing the time and costs researchers invest in conducting literature reviews. The tool is an essential resource for researchers globally, empowering them to focus on their core research interests while the tool streamlines the process, saves time and effort, and provides more options than traditional searches. The user-friendly platform and innovative solutions of Iris.ai have earned recognition globally and attracted clients worldwide.
Company Overview
Iris.ai is a cutting-edge AI-powered tool that assists researchers in conducting literature reviews at scale. With an emphasis on robust AI algorithms, Iris.ai simplifies the process of literature review for researchers of all levels.
The company was founded by a team of scholars passionate about revolutionizing the academic research landscape. Their mission is to democratize access to knowledge by reducing the time and costs researchers must invest in conducting literature reviews.
Iris.ai analyzes academic papers for contextual relevance that can significantly reduce the time and effort required by researchers to perform large-scale systematic reviews. Researchers can use their natural language queries to describe their research interests, and Iris.ai will provide a list of relevant papers with additional relevant papers suggested from those previously selected by the researcher. As a result, Iris.ai generates a comprehensive list of relevant papers, saving researchers the hours needed for exhaustive searches.
The Iris.ai team's multi-disciplinary background in AI and their previous experience in research makes them uniquely qualified to work on this project. They understand the challenges researchers face and have developed innovative solutions by leveraging the latest in AI technology. The Iris.ai platform is user-friendly, and the company provides excellent customer support to ensure submission deadlines are met.
Since launch, Iris.ai has attracted clients from all over the world and received recognition for their contribution to the development of academic research. Iris.ai's cutting-edge technology empowers researchers to focus on their core research interests while leaving the challenges of literature review to a reliable AI tool that streamlines the process, saving researchers' efforts while providing more options than traditional searches.
The Iris.ai tool is essential for researchers around the globe who need a reliable, fast, and efficient way to conduct literature reviews without expert knowledge of a particular field.
Features
Automated Workspace Specialized for Your Field of Research
No Human Effort Needed
Iris.ai's Researcher Workspace offers a fully specialized experience for researchers without requiring any human labeling, taxonomies, or training. The AI-powered system is centered around the exact field of research and the content that the researcher uploads. This means that every tool in the Workspace can be applied according to the specific needs and demands of the research, whether the researcher works alone or collaboratively.
The system is designed to save time and effort in the research process, allowing the researcher to focus on analysis and conclusions instead of manual data processing.
Data and Document Sets Integration
The Researcher Workspace not only allows the researcher to upload research papers, patents, and internal research documentation but also to connect directly to a live proxy dataset such as a publisher, a patent authority, or an internal repository. This integration enables the Workspace to extract valuable data and insights from different sources, thereby expanding the researcher's understanding of the research problem. Federated searches across a wide variety of sources save an enormous amount of time, and each researcher is free to choose and integrate more datasets, depending on the needs of each project.
Context-Based Recommendation Engine
When dealing with interdisciplinary research challenges or fields, keywords can become a limiting factor. The Context-Based Recommendation Engine, called Iris.ai Explore, helps researchers bypass keywords and explore interdisciplinary fields.
This tool allows the researcher to give the machine a text, either their own description of the problem, or another research paper or document, and identify the most meaningful words in the text, enrich it with contextual synonyms and hypernyms, and match it with every paper in all of the sources selected. With this tool, the researcher can expand their research beyond their vocabulary, enabling them to map out the research problem from multiple perspectives.
Document Set Analysis and Advanced Filtering
Filtering Based on Criteria You Can Explain in a Sentence but Not in One Keyword
The Workspace's advanced filtering extracts and identifies the exact information required from the documents based on specific entities, data points, or data ranges. This level of filtering specificity is particularly helpful when seeking narrow results from large document sets. The system can easily handle complex filtering tasks, allowing the researcher to ask complex questions such as, “Are there any papers in PubMed that report on Ibuprofen with a prevalence of the adverse effect of nausea above 5%?" or “Which of the 500 documents in front of you reports on steel with a tensile strength between 600-650 MPa?”
Context-Based Filtering
For filtering tasks that are not easily expressed with a keyword or three, the Workspace's Context filters come to the rescue. The researcher can write their own descriptive context of 50-100 words, which is matched against every article in the content list, and make it either for inclusion or exclusion. With the Workspace’s Context filters, hundreds of articles can be filtered down in minutes, allowing the researcher to focus on those articles that are most relevant to their research.
In-Depth Document Set Analysis
The Iris.ai system has a powerful tool where the researcher can analyze a large set of documents to gain an overview of the content and decide what to include or exclude according to their specific research goals. This tool comes in handy when dealing with a large set of search results, as it provides an organized view of the content of a document set. The results include Topic groups of the literature list, both from a global and specific topic level, which helps the researcher selects groups for inclusion and exclusion without missing any relevant document.
The tool also allows for the exploration of the most meaning-bearing words of the document set.
Data Extraction and Systematization
Automatically Extract and Systematize Key Data Points from Text and Tables
The Iris.ai Extract tool helps researchers to extract and systematize key data points from tables, graphs, figures, and free text. This tool fetches and links all the key data from these documents and organizes it into a tabular, machine-readable, systematic format.
With this tool, a full month of data extraction work can be done in minutes at 90% accuracy, freeing up the researcher's time for analysis and insights. The PDF containing the relevant data points to be extracted is sent to the Iris.ai system.
This PDF can be a patent, a clinical trial report, a research paper, or any other relevant type of scientific or technical content.
Summarization Tool
Abstractive Summarization Tool
The Workspace also comes with a summarization tool that can rapidly produce summaries of multiple abstracts or one or multiple full-text documents. The summaries are excellent for reviewing larger document sets of similar documents or kickstarting scientific writing.
Iris.ai's summarization tool does abstractive summarization, meaning the tool writes its summary instead of copy-pasting sentences together (called extractive summarization). The system can quickly produce summaries of the most essential bits of the documents and can also be configured according to the researcher's preferences.
Pricing
Iris.ai offers a variety of pricing plans to cater to the different needs of its users. The platform offers a free trial for 14 days, allowing users to try the product before they buy it. The starter plan begins at $299 per month, which includes access to Iris.ai’s knowledge graph, entity extraction, and entity linking features.
The professional plan is priced at $699 per month and adds features such as API access and full-text search, as well as the ability to extract tables and figures from scientific papers. The enterprise plan is priced customized according to the specific needs of the customer's organization and provides enhanced features such as customized machine learning models and custom data parsing.
In addition, Iris.ai offers two other pricing plans. The SMB plan is suitable for small and medium-sized businesses and starts from $99 per month. It includes basic features such as entity extractions, entity linking, and API access.
The high-volume plan is designed for organizations with larger-scale needs such as pharmaceutical companies and research universities. The price is customized based on the specific requirements of the organization.
Overall, Iris.ai’s pricing plans are reasonable considering the sophisticated features it provides. The plans are flexible, allowing users to pick the right plan for their unique needs. With the addition of the free trial, users can rest assured that they are choosing the right plan that matches their requirements.
FAQ
What is Iris.ai's Researcher Workspace?
Iris.ai's Researcher Workspace is an AI-based research platform that allows for an all-in-one approach to research. It allows users to search, find, filter, summarize and extract data points all using one platform. It is differentiated from other tools available online by its ability to understand what the user is looking for with its use of artificial intelligence models.
How does Iris.ai's Researcher Workspace work?
The Researcher Workspace tools are interchangeable allowing users to combine them in any way that works for them. The tools work with documents of scientific language such as trade magazines, popular science, regulatory documentation and internal memos.
The tools work with almost any type of machine-readable content. The client provides research articles or patents describing their field or specific problem.
The engine uses this information to find thousands of related papers based on each article provided. Then, found articles are injected into the general model to enhance the relationship of the words that are directly associated with the domain knowledge requested by the client. As a result, the engine becomes more sensitive towards domain-specific terms and their relationship to other words that are already in the model.
A feedback loop with the domain expert for validation of initial results helps to streamline the process.
What is the difference between abstractive and extractive summarization?
There are broadly two approaches to automatic text summarization: extractive and abstractive. Extractive approaches select passages from the source text, then arrange them to form a summary. Abstractive summarization means the tool captures the context of the text and builds a contextual “fingerprint” which is matched with the content collection or database of scientific text the tool is connected to.
The machine-generated summary of Iris.ai thus contains new phrases and sentences that may not appear in the source text. Iris.ai uses abstractive summarization which is a more advanced and accurate approach.
How does Iris.ai use artificial intelligence in its Researcher Workspace?
Artificial Intelligence is an important and helpful part of the Researcher Workspace. For example, the Explore tool extracts the most meaning-bearing words from users' articles or problem statements and enriches them with contextual synonyms and topic words to build a contextual "fingerprint." This fingerprint is matched with the content collection or database of scientific text the tool is connected to. Using the same "fingerprint" approach in the Extract tool, the machine identifies various values and entities across text, tables, and graphs and extracts them neatly.
The AI is also used in the Analyze tool to quickly scan the dataset and provide topic and concept lists to filter the articles.
What languages does Iris.ai's Researcher Workspace support?
Currently, the machine works only in English. However, in specific cases, Iris.ai can implement a translation to other languages. Most non-English scientific articles include an English version of the abstracts as well, so the machine successfully works with them too.
