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Iris.ai

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Iris.ai offers a comprehensive AI-powered platform for researchers, providing features such as content-based search, context and data filtering, extraction and systematizing data, an adaptable researcher workspace, support for various document types, and an extraction tool with pre-defined data layouts. The platform enhances the research process by enabling users to explore interdisciplinary research, narrow down document sets based on contextual sentences or specific data points, automatically extract and systematize data, personalize the research experience, and systematically extract information from articles.

Paid Plan:

Free trial: 3 months

Free trial: 3 months, extended unless the pandemic is over

Features:

Iris.ai offers a content-based recommendation engine that allows users to bypass keywords and explore interdisciplinary research, enhancing the discovery process beyond traditional keyword-based searches.

The platform provides advanced filtering capabilities, enabling users to narrow down document sets based on contextual sentences or specific data points and ranges, without relying solely on keywords.

Iris.ai includes tools for automatic extraction and systematization of data from documents, facilitating the organization and analysis of research findings.

The Researcher Workspace is fully adaptable to the user's field of research without the need for human labeling, taxonomies, or training, offering a personalized and efficient research experience.

The platform supports a wide variety of datasets, including research papers, patents, and internal research documentation, accommodating diverse research needs.

The Extraction tool within the Researcher Workspace allows users to systematically extract specific information from articles using pre-defined and approved data layouts, enhancing data analysis and integration.

Iris.ai

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Last updated at Oct 31, 2025

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Pros

  1. Content-based search and interdisciplinary exploration: Iris.ai offers a unique content-based recommendation engine that allows researchers to bypass traditional keyword searches, facilitating interdisciplinary research exploration. This is a significant advantage over other platforms that rely heavily on keyword matching, which can limit the discovery of relevant research outside predefined terms.
  2. Automatic data extraction and systematization: The platform's ability to automatically extract and systematize data from a variety of documents, including research papers and patents, and then link this data across multiple articles into a comprehensive framework is a standout feature. This level of automation and data handling is not commonly found in similar research tools, which often require more manual effort for data extraction and organization.
  3. Adaptability to specific research fields without human effort: Iris.ai specializes the workspace on the user's exact field of research without any human labeling, taxonomies, or training. This full specialization is a unique pro, as many other platforms require significant setup time and manual effort to tailor their tools to a specific research domain.

Cons

  1. Limited data layout templates for extraction: While Iris.ai provides powerful data extraction tools, the current limitation to a few predefined data layout templates could restrict flexibility for researchers needing custom data extraction formats. This contrasts with some platforms that offer more customizable or extensive template options.
  2. Transition phase to new Researcher Workspace: As Iris.ai transitions users to the new Researcher Workspace, there may be temporary accessibility or functionality limitations compared to platforms that have a stable, long-standing interface. This transition phase could pose a temporary inconvenience for users accustomed to the previous version.
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Use case 1: Streamlining Literature Review for Academic Research

  • Leveraging AI for Comprehensive Literature Searches
  • An academic researcher embarks on a literature review for their thesis on a niche topic within cognitive psychology. Aware of the limitations of traditional search engines, they turn to Iris.ai's content-based recommendation engine. This allows them to bypass keyword limitations and discover a broader range of relevant articles, including interdisciplinary studies that offer unique insights into their topic.
  • Customized Filtering for Efficient Data Management
  • Faced with an overwhelming amount of literature, the researcher uses the Context and Data Filtering feature to refine their search results. By inputting specific criteria related to their research question, they efficiently narrow down the search to the most relevant studies, saving valuable time and effort in the review process.
  • Data Extraction for Effective Analysis
  • To analyze the selected studies systematically, the researcher employs the Extraction and Systematizing Data feature. This tool automatically extracts key data points and organizes them into a pre-defined format, enabling the researcher to easily compare results, identify trends, and draw conclusions for their thesis. This streamlined process not only enhances the quality of their literature review but also significantly reduces the time spent on manual data extraction and organization.

Use case 2: Enhancing Pharmaceutical Research with AI-driven Insights

  • Content-Based Search for Drug Discovery
  • A pharmaceutical researcher is exploring potential compounds for a new drug targeting a specific disease. Instead of relying on traditional keyword searches, which can miss relevant studies due to terminology differences, they use Iris.ai's Content-Based Search. This allows them to discover research based on the content and context of their initial research inputs, uncovering a wider array of potential compounds and related studies.
  • Context and Data Filtering for Precision
  • To narrow down the vast amount of data to the most pertinent studies, the researcher uses the Context and Data Filtering feature. By specifying the disease context and desired compound characteristics, they efficiently filter out irrelevant studies, focusing only on those with the highest potential for contributing to their drug development process.
  • Researcher Workspace Modules for Collaboration
  • As the drug discovery process advances, the researcher collaborates with colleagues across different departments. Utilizing the Researcher Workspace Modules, they share search results, analyses, and data extractions seamlessly within a unified platform. This collaborative environment not only speeds up the research process but also ensures that all team members are aligned and informed, facilitating more effective decision-making.

Use case 3: Accelerating Interdisciplinary Research for Climate Change Solutions

  • Identifying Relevant Research Across Disciplines
  • A climate scientist is tasked with developing innovative solutions to combat climate change. Recognizing the interdisciplinary nature of the challenge, they use Iris.ai's Interdisciplinary Research Discovery feature to identify relevant research across fields such as environmental science, engineering, and social sciences. This broadens their perspective and uncovers novel approaches previously unconsidered.
  • Custom Training for Deep Domain Focus
  • To refine their search and ensure the most relevant and up-to-date research is included, the scientist utilizes the Custom Training for Deep Domain Focus service. By feeding the AI a curated set of documents specific to their current focus—such as renewable energy sources or carbon capture technologies—they tailor the AI's search capabilities to their precise needs, enhancing the relevance of discovered documents.
  • Systematic Analysis and Comparison
  • With a comprehensive set of interdisciplinary research at their disposal, the scientist employs the Extraction and Systematizing Data feature to systematically analyze and compare findings from various studies. This process helps in identifying patterns, gaps, and opportunities for innovation in the field of climate change mitigation, guiding the scientist towards promising areas of further research or application.

FAQs

Frequently Asked Questions

Iris.ai offers the ability to custom train the AI tools on a specific domain using a collection of domain-related documents, enhancing the precision and relevance of search results for specialized research areas.

Iris.ai facilitates interdisciplinary research discovery by leveraging AI to identify relevant research across different disciplines, increasing the breadth of research exploration.

Iris.ai's Researcher Workspace Modules include tools such as Search, Filter, Analyze, Extract, Summarize, Automate, and Report, allowing for a flexible and adaptable research process tailored to the user's needs.

Iris.ai automatically extracts and systematizes data from research documents into a pre-defined format, enabling systematic analysis and comparison across multiple sources.

Iris.ai provides the ability to filter down document sets based on contextual criteria explained in a sentence or specific data points and ranges extracted from documents, offering a more nuanced search capability.

Iris.ai offers a content-based recommendation engine that allows users to bypass keywords and explore interdisciplinary research, enhancing the discovery process beyond traditional keyword-based searches.

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