About Iris.ai
Iris.ai is an enterprise-grade AI development and operations platform designed to help large organizations build, manage, and monitor Agentic Retrieval-Augmented Generation (RAG) systems. The platform serves as a centralized hub where teams can connect various data sources, orchestrate complex AI workflows, and evaluate model performance with scientific precision. By moving beyond simple chat interfaces, the system focuses on creating autonomous agents capable of handling sophisticated research tasks while maintaining the high standards of accuracy and transparency required in corporate environments.
The platform operates through a suite of tools including Axion, Neuralith, and RSpace, which together facilitate the end-to-end lifecycle of an AI agent. It allows for the secure ingestion of massive datasets—having already processed over 160 million documents—and provides a custom evaluation framework to measure the reliability of AI-generated answers. A key component of the workflow is the real-time monitoring dashboard, which gives administrators visibility into agent performance and system health, ensuring that deployments remain compliant and effective over time.
Iris.ai is specifically tailored for research-intensive industries such as manufacturing, chemistry, automotive, and defense. It is an ideal solution for Research & Development (R&D) departments, Intellectual Property (IP) teams, and knowledge management professionals who need to extract insights from vast quantities of technical documentation, patents, and scientific papers. The platform is currently utilized by global leaders like ArcelorMittal and L'Oreal to accelerate innovation cycles and reduce the manual labor involved in information synthesis.
What distinguishes Iris.ai from general AI development tools is its research-first philosophy and structured implementation methodology. The company offers a clear roadmap from initial co-creation to full-scale internal ownership, including a certification process for internal teams on CI/CD best practices and prompt engineering. This approach minimizes the "black box" nature of AI, offering measurable results such as a 35% reduction in LLM usage costs and an 80% faster go-to-market for new AI-driven features.
