ByHeather MacAvelia·Last verified Jun 18, 2026

Iris.ai

Name: Iris.ai
Author: Iris.ai

★3.5/ 5

by Iris.ai

Independently Reviewed

Visit iris.ai →

AI knowledge foundation for regulated enterprises with Axion, Neuralith, and RSpace for Agentic RAG. Trusted by USDA, Mercedes-Benz, and ArcelorMittal. Custom enterprise pricing.

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Iris.ai is the Agentic RAG-As-A-Services platform built for regulated enterprises in life sciences, financial services, energy, manufacturing, agriculture, and materials science that need to transform 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, NCI Agency (NATO), and the Finnish Food Authority, spanning government, automotive, agriculture, cosmetics, steel, and academic research sectors. Iris.ai is positioned as 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. Notable integration gaps not confirmed in current official documentation: specific native 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 not published. Integration depth requires a sales-led scoping engagement before specific connections can be evaluated. 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 ($12/month) serves academic researchers with structured systematic review at accessible self-serve pricing. SciSpace ($12/month) 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/month) and Gemini Deep Research ($19.99/month) 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. As of Q2 2026, Iris.ai's defining positioning shift is Agentic RAG-As-A-Services: 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 €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 product's pivot away from the broad research tool market toward regulated enterprise AI infrastructure. The platform holds GDPR certification 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 globally.

Pricing

custom

Segment

enterprise

Setup

easy

Verified

Jun 18, 2026

Transparency

Quote Only

Contract

Annual Only

Data training

Not Disclosed

Autonomy

Human Required

Capabilities

literature-reviewsystematic-reviewcitationsdata-analysisdeep-research

Pros & Limitations

Editorial assessment

Pros

✓Named enterprise adoption across Fortune 500 and government organizations: Mercedes-Benz, ArcelorMittal, USDA, Max Planck Gesellschaft, and Springer Nature appear on the official vendor homepage, providing procurement validation that is rare in the regulated enterprise AI infrastructure category.
✓Three-product architecture covers the full data-to-AI lifecycle: Axion handles data preparation into AI-ready intelligence, Neuralith powers the enterprise knowledge graph engine, and RSpace delivers precision R&D intelligence, providing end-to-end coverage that fragmented stacks requiring separate vendors for data preparation and AI agent deployment cannot match.
✓Ten-year track record from 2015 through multiple product pivots demonstrates operational maturity: the evolution from academic research AI through scientific language models to Agentic RAG-As-A-Services shows sustained development velocity and enterprise customer retention that AI challengers founded after 2020 cannot replicate.

Limitations

⚠No public independent review trail creates procurement friction: the G2 listing was removed in 2026, Trustpilot returns 404, and no Capterra presence exists, making third-party validation outside the vendor-provided homepage customer logos difficult; tools such as Elicit ($12/month) and SciSpace ($12/month) offer G2-verified reviews for procurement teams requiring independent evidence.
⚠Named integration depth is not published in official documentation: the Agentic RAG positioning implies enterprise data connectivity, but specific native integrations with SAP, Salesforce, Veeva Vault, SharePoint, PubMed, or Scopus are not confirmed, requiring sales-led scoping before integration depth can be evaluated.
⚠Enterprise-only entry with no self-serve evaluation path excludes individual researchers, academic teams, and smaller organizations: the platform requires sales-led engagement, custom data engineering, and sustained implementation investment with no trial access, while Elicit ($12/month) and SciSpace ($12/month) serve research use cases at a fraction of the cost and without implementation complexity.

Technical Details

Deployment

web

Model architectureProprietary

Avg setup time8-16 weeks (sales-led discovery, data discovery, knowledge graph construction, AI agent configuration, enterprise system integration, team rollout)

Autonomous rateAgentic: Iris.ai autonomously processes enterprise data into AI-ready knowledge (Axion), constructs enterprise knowledge graphs (Neuralith), and delivers R&D intelligence (RSpace) within governed guardrails. Enterprise teams approve all decision-supporting outputs from deployed AI agents. The Agentic RAG-As-A-Services platform builds and operates AI agent pipelines grounded in enterprise-specific knowledge.

Integrations

ZoteroMendeleyPDF imports

Security

GDPR