Iris.ai

What is Iris.ai?

Iris.ai is an AI-based scientific research assistant founded in 2015 by Anita Schjøll Brede, Jacobo Elosua, and Victor Botev. Focused on the corporate research and development (R&D) and patent market, Iris.ai acts as an advanced semantic search and academic text analysis engine, processing millions of publications and patent documents to synthesize data and save months of human reading of complex scientific articles.

How it works

Unlike common search engines that cross-reference only literal keywords, Iris.ai processes the semantics of long texts. The user enters an article abstract, a research project description, or a patent draft. The artificial intelligence reads and understands the scientific context, extracting key concepts into a visual concept map. Then, the platform sweeps world data repositories to map, group into thematic categories, and summarize all corresponding articles and patents, automatically extracting internal tabular data.

Key features

• Researcher Workspace: An integrated work environment to import, catalog, and monitor bibliographic literature reviews.
• Patent Mapping: Deep comparative analysis in global patent registry databases to verify the originality of technological inventions.
• AI Filters and Classification: Intelligent algorithm that removes irrelevant articles from the search list based on real semantic context, refining thousands of results down to a few dozen qualified ones.
• Systematic Data Extraction: AI that reads articles and automatically extracts data tables, dosages, materials used, and experimental metrics into structured Excel format.
• Smart Summaries: Textual condensation of multiple articles for fast batch reading and comparison of results.

Available integrations

Iris.ai connects to major academic repositories and intellectual property databases, such as OpenAlex, PubMed, WIPO (World Intellectual Property Organization) patent repositories, and EPO (European Patent Office), integrating via API with private corporate R&D systems.

Who it is for

• R&D (Research and Development) departments of companies in the chemical, pharmaceutical, automotive, and new materials industries.
• Patent Lawyers and Engineers who need to analyze the viability of new intellectual registrations.
• Academic institutions and large-scale research laboratories managing large volumes of scientific bibliographies.

Real-world use cases

1. Development of New Materials: A chemical company uses Iris.ai to scan 50,000 academic articles on biodegradable polymers, extracting in a few hours a table containing the melting points and tensile strength of each compound described in the literature.
2. Patent Originality Audit: Before submitting a new electric battery technology for official registration, a law firm uses the tool to ensure no similar patent has been registered in the last 20 years in Asia or Europe.

Pricing

Pros and Cons

Pros:

• High-performance semantic search engine for advanced scientific terminologies.
• Automated data extraction from PDF internal tables saves weeks of work for interns and research assistants.
• Comprehensive tracking of hard-to-navigate international patent bases.

Cons:

• Interface and workflow focused on an advanced niche, requiring initial training to use the platform.
• Corporate pricing not friendly to independent researchers or individual students.

Alternatives to Iris.ai

The main alternatives in the scientific literature search and intelligence niche are:

• Elicit: Scientific search and synthesis engine focused on answering research questions based on peer-reviewed articles intuitively.
• Connected Papers: Excellent visual generator of article connections ideal for initial exploration of academic topics.
• CAS SciFinder: The classic historical leader in the chemistry and biology research market, which added intelligent molecular structure search capabilities.