Iris.ai
AI knowledge foundation for regulated enterprises — turning complex data into AI you can trust
Enterprise·Technical·API available
Key strengths
Agentic RAG-as-a-Service for regulated enterprisesUnified ingestion of complex, heterogeneous enterprise dataReal-time monitoring and CI/CD pipelines for AI agentsOver 330M documents securely ingested at scale35%+ savings on LLM usage costs through optimized evaluation
Enterprise pricing
Oslo, Norway
Founded 2015
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Technical Documentation Overview
Platform Architecture
Iris.ai is built around an Agentic RAG-as-a-Service model. The platform unifies complex enterprise data into a structured knowledge layer that feeds AI agents. Key components include:
- Axion™ — Handles data ingestion and transformation, converting raw, chaotic enterprise data into AI-ready intelligence.
- Neuralith™ — Converts enterprise knowledge into an AI engine, serving as the backbone for knowledge retrieval and agent reasoning.
- RSpace™ — Provides precision intelligence tailored for complex R&D workflows, including cross-disciplinary research synthesis.
Integration & Deployment
- Supports ingestion of 330M+ documents from diverse enterprise sources
- Custom evaluation frameworks are built per deployment to measure LLM output quality (see their LLM Evaluation White Paper)
- CI/CD pipelines are supported for iterative agent development and promotion to production
- Real-time monitoring dashboards track agent performance across deployed use cases
Key Technical Metrics
- 35%+ savings on LLM usage costs through retrieval optimization
- 80%+ faster AI go-to-market via pre-built Agentic RAG scaffolding
- 200,000+ answers evaluated across 50+ enterprise use cases
Contact the Iris.ai team via contact@iris.ai or request a demo for API access and integration documentation.