Iris.ai

Iris.AI is a research-focused platform built to reduce the manual effort involved in exploring scientific literature. Instead of browsing one paper at a time, users describe what they are looking for and let the system surface relevant documents, organize reading lists and highlight the Information that matters. It is aimed at people who work with large bodies of technical or academic text and need to move from a broad question to a structured overview.

What it does

The platform combines semantic search with tools for handling collections of papers. Users can run a search across a scientific corpus, APPly filters to nARRow results, and then work through a reading list where the system analyzes the selected documents. Rather than reading every abstrACT manually, researchers can request summaries and extracted details to speed up screening.

Key capabilities

• Intelligent search: query scientific literature using natural language rather than exact keyword matching.
• smart filters: refine result sets by criteria such as publication year, discipline or document type, depending on what the interface exposes.
• Reading list analysis: group papers into a collection and analyze them together instead of in isolation.
• Automatic summaries: generate condensed overviews of selected documents to support faster triage.
• Data extraction: pull structured information such as methods, datasets or findings out of papers and organize it systematically.

These features are designed to work as a pipeline: search, collect, analyze and extract. The value comes from keeping that sequence in one place rather than moving files between separate tools. Exact functionality and limits may vary by plan and change over time.

Who it is for

Iris.ai is most useful for academic researchers, graduate students, R&D teams and analysts who regularly review scientific or technical literature. It suits systematic literature reviews, competitive technical research, and early-stage exploration of an unfamiliar field. Teams that need to document how they screened sources may find the organized reading-list approach helpful, though it is not a substitute for a full systematic-review methodology.

How to get started

1. Open the Iris.ai website in a browser; the product runs on the web, so no local installation is required.
2. Create an account if one is needed for the features you want to try.
3. Enter a research question or topic to start a search.
4. Apply filters to focus the result set.
5. Add promising papers to a reading list and run the analysis and extraction tools on that collection.

Pricing and access caveat

Iris.ai has offered free entry points alongsIDE paid plans, but plan names, quotas and feature gating are subject to change and should be confirmed on the official site before committing to a workflow. Access Generally requires a stable internet connection, and full-text availability depends on the underlying sources rather than the platform alone. Verify current terms directly with the vendor.