Iris.ai

Iris.ai is an AI research workspace aimed at scientific and R&D teams rather than casual users. Its purpose is systematic literature work: point it at a large body of papers and it maps the field, extracts key findings and data points, and helps synthesise across many documents at once. The Norwegian-founded company has long targeted “science-grade” rigour, with an emphasis on traceability so extracted claims link back to their source.

Compared with lighter tools like Semantic Scholar or Elicit, which excel at search and quick summaries, Iris.ai leans toward structured, repeatable workflows — technology scouting, competitive intelligence, patent and literature landscaping, and systematic reviews. That makes it a fit for corporate R&D departments and research organisations that need to process hundreds of documents defensibly rather than skim a handful.

Key Features

• Bulk document ingestion and field mapping
• AI synthesis across large sets of papers
• Structured data and entity extraction with source links
• Systematic review and screening workflows
• Technology scouting and landscaping tools
• Shared team workspace for collaborative research
• API access for integrating into internal pipelines

Pricing

• Free / Trial: Limited document allowance to test the workspace
• Individual/Researcher (paid monthly): Higher document limits and full AI features
• Enterprise: Custom pricing with API, on-prem or private deployment, and support
• Exact figures depend on volume and deployment; the platform is oriented toward paid team use.

Best For

Corporate R&D teams, technology scouts, and research organisations that must synthesise insights across hundreds of scientific papers or patents with an audit trail — work that is impractical to do manually.

Limitations

For a quick literature search or a single paper summary, Iris.ai is heavier than needed — Semantic Scholar or Elicit will be faster and simpler. Its enterprise positioning means pricing is not always transparent, and the systematic-review workflows carry a learning curve that casual users may find excessive.