Iris.ai

Iris.ai is the Agentic RAG-as-a-Service platform built for regulated enterprises in life sciences, financial services, energy, manufacturing, agriculture, and materials science. It transforms complex, fragmented enterprise data into AI-ready intelligence for next-generation AI agents and applications. Founded in Oslo, Norway in 2015 by Anita Schjøll Abildgaard (CEO) and Jacobo Elosua, the company evolved from an academic scientific text understanding engine into an enterprise AI knowledge foundation. The platform delivers three integrated products: Axion (data chaos to AI-ready intelligence), Neuralith (enterprise knowledge into an AI engine), and RSpace (precision intelligence for complex R&D). Named customers confirmed on the official vendor homepage include USDA, Mercedes-Benz, L'Oréal Groupe, ArcelorMittal, Max Planck Gesellschaft, University of Oslo, Springer Nature, Syngenta, the NATO Communications and Information Agency (NCI Agency), and the Finnish Food Authority, spanning government, automotive, agriculture, cosmetics, steel, and academic research. Iris.ai is an API-first enterprise data platform that connects to existing enterprise data environments to build Agentic RAG knowledge foundations. It ingests and processes internal research repositories, regulatory filings, scientific literature, and proprietary enterprise databases to ground AI agents in enterprise-specific, traceable knowledge. Specific native connectors are not published in current documentation: connections to ERP systems (SAP, Oracle), CRM platforms (Salesforce), scientific databases (PubMed, Scopus, Web of Science), document management platforms (SharePoint, Box), and R&D systems (Veeva Vault) are established through a sales-led scoping engagement rather than as off-the-shelf integrations. No MCP server is published. Iris.ai is enterprise-only with no public self-serve pricing. No pricing page exists on the website. Contracts are sales-led and billed annually. There is no free trial, freemium tier, or self-serve evaluation path. Contact the Iris.ai team via the demo request form on iris.ai to begin an enterprise pricing discussion. Iris.ai is not designed for individual academic researchers, small organizations, or teams without dedicated data engineering capacity. Causaly (custom enterprise pricing) addresses pharmaceutical-specific biomedical knowledge graph needs with a drug discovery focus. Elicit ($49/mo billed annually) serves academic researchers with structured systematic review at accessible self-serve pricing. SciSpace ($12/mo billed annually) covers academic paper analysis with journal database access and no implementation overhead. For general-purpose AI research without the regulated-industry data foundation requirement, ChatGPT Deep Research ($20/mo) and Gemini Deep Research ($19.99/mo) handle broad research tasks without enterprise implementation complexity. Microsoft Copilot Studio provides broader enterprise AI agent building with established Microsoft ecosystem integrations for teams that do not require a specialized regulated-industry knowledge foundation. The defining recent positioning shift for Iris.ai is Agentic RAG-as-a-Service: the platform now explicitly positions around building, managing, and monitoring Agentic RAG systems for enterprise AI agent deployment, rather than the research search-engine positioning of earlier years. The company raised EUR 7.64M in May 2024 (Silverline Capital and the EIC Accelerator Fund), bringing total confirmed funding to $21.6M across six rounds. The G2 listing was removed in 2026, reflecting the pivot away from the broad research-tool market toward regulated enterprise AI infrastructure. The platform holds GDPR compliance as a Norwegian company operating under EU data regulations. A rolling customer logo wall on the official homepage confirms named enterprise deployment across Fortune 500, government, and Tier-1 research organizations, including USDA, Mercedes-Benz, ArcelorMittal, Max Planck Gesellschaft, and the NATO Communications and Information Agency.