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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- Building production-grade Agentic RAG systems: Developers use Iris.ai's platform to construct, evaluate, and deploy multi-agent RAG pipelines against proprietary enterprise data with custom retrieval configurations.
- LLM cost optimization: Engineering teams leverage Iris.ai's evaluation framework to measure and reduce LLM call costs by 35%+ through smarter retrieval filtering and prompt optimization.
- CI/CD for AI agents: Teams implement continuous integration and deployment workflows for AI agents, enabling iterative improvement and regression testing of agent behavior over time.
- Custom evaluation framework design: Data scientists co-create evaluation benchmarks specific to their domain (e.g., R&D, telecom) to objectively measure answer quality across 50+ use cases.
- Cross-disciplinary document ingestion: Backend engineers integrate diverse data sources — scientific literature, patents, internal reports — into a unified knowledge store ingesting 330M+ documents.
- Real-time agent monitoring: Platform engineers instrument deployed agents with live dashboards to track accuracy, latency, and usage patterns across all active use cases.
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