Iris.ai

Iris.ai Review

Iris.ai is a research discovery and extraction platform that helps R&D teams find papers, map topics, and pull structured data from PDFs. It builds domain maps from a problem statement, clusters related work, and automates extraction of entities, methods, and results into spreadsheets. Users annotate to improve accuracy, track literature over time, and export reports for review. Compliance features handle access control and audit logs for regulated environments. Typical workflows include prior-art scans, systematic reviews, and competitive landscape mapping. The value is accelerated research with traceable evidence and less manual reading.

Things to Know About Iris.ai

Iris.ai drawbacks: Literature coverage can be patchy across disciplines and paywalled journals, and PDF parsing struggles with equations, tables, and figure captions. Concept extraction may miss domain nuance, requiring manual curation. Collaboration, versioning, and export to reference managers are lighter than established academic tools. For confidential R&D, cloud processing and retention policies need scrutiny, and large-scale usage can raise costs.

Top Features

• Research assistant that maps and summarizes scientific literature
• Topic discovery with concept extraction and clustering
• Contextual search over abstracts and full texts
• Reading lists, highlights, and auto-generated summaries
• Claim extraction with links to sources
• Quality signals by methodology and venue
• Upload PDFs for focused Q&A
• Exports to BibTeX/CSV/Markdown
• Team workspaces and shared projects
• Browser tools for quick capture

Iris.ai Pricing

Iris.ai pricing: quote-based packages for research teams that scale by users, document processing volume, and custom ontologies; enterprise tiers add integrations, governance, and support; pilots are typical before large rollouts.

How to use Iris.ai

To use Iris.ai, create a research topic, run an exploration, and review clustered results by concept. Drill into abstracts, filter by methodology or time, and build a reading map. Export citations and notes, and iterate queries to refine the literature set.

Alternatives & Competitors

Iris.ai competes with Elicit, Consensus, and Semantic Scholar (Ask)—research assistants for literature discovery and summaries. Overlap includes concept search, filters, and evidence extraction. Rivals may provide claim verification, systematic-review workflows, and reference manager exports. Iris.ai’s value is mapping research spaces and digesting findings. Gaps include coverage limits beyond open-access domains and fewer collaboration/reporting features for teams.

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