Iris.ai

Iris.ai is an enterprise research platform from a Norwegian AI company (now called RSpace), designed for R&D teams and raising literature review efficiency by up to 90% through semantic search and automated data extraction. Since its founding in 2015, the platform has mainly served R&D-intensive, zero-tolerance industries such as pharmaceuticals, biotechnology, and materials science.

Core Capabilities

• Semantic search: Finds relevant literature that traditional search misses, based on a research-topic fingerprint rather than keyword matching
• Automated data extraction: Identifies and extracts data points from unstructured text, tables, and figures, organizing them into structured formats
• Intelligent summarization: Abstractive summarization of single and multiple documents, for quickly grasping core findings
• Private document integration: Securely connects internal enterprise knowledge bases, publisher databases, and patent office data
• Bias-reduced discovery: Focuses on research content rather than citation counts, helping surface overlooked high-value papers
• Agentic RAG services: Builds, manages, and monitors intelligent retrieval-augmented generation systems for enterprises

Use Cases R&D teams in chemistry, pharmaceuticals, and biotechnology conducting systematic literature reviews; intellectual property departments analyzing and searching large volumes of patent literature; interdisciplinary research projects that need to integrate knowledge across fields; and regulated industries needing a compliant, secure environment for literature processing. If your team spends days each week leafing through papers and patents, Iris.ai can compress that time into hours.

Unique Advantages Compared with free tools such as Semantic Scholar and Elicit, Iris.ai provides enterprise-grade security and private data integration. Training can be customized for specific industry terminology, giving it a clear advantage on the compliance needs of regulated industries. Its complete R&D workflow support (search + analysis + extraction + summarization) goes beyond single-function competitors.

Editor's Review Customer cases at globally leading companies such as ArcelorMittal prove it can shorten R&D cycles by weeks or even months, and it has securely processed more than 160 million documents. But enterprise custom pricing puts it out of reach for individual researchers, and the learning curve is steeper than free tools. If you are a funded enterprise R&D team with strict data security requirements, Iris.ai is one of the most professional AI literature analysis choices available today.