Iris.ai
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About
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.
Pros & Cons
Proven capacity to securely ingest and process over 160 million technical documents.
Demonstrated reduction of LLM usage costs by 35% or more for enterprise users.
Significant acceleration of AI go-to-market timelines by up to 80% through structured workflows.
Provides a dedicated evaluation framework to measure and audit AI performance scientifically.
Includes a structured enablement plan to ensure internal teams gain full ownership of AI agents.
No self-serve pricing or public trial available without a formal demo request.
Full-scale implementation requires a minimum commitment of 30 to 180 days.
Highly specialized features require internal team certification for optimal management.
Use Cases
Intellectual Property managers can automate patent reviews to cut weeks or months out of research and development timelines.
R&D departments in manufacturing can ingest millions of external documents to synthesize insights and identify new innovation opportunities.
Enterprise AI leads can use the monitoring dashboard to govern multiple agentic workflows and ensure compliance with internal accuracy standards.
Platform
Features
• real-time monitoring dashboard
• llm usage cost optimization
• multi-agent governance
• ci/cd workflow management
• team certification training
• secure document ingestion
• custom evaluation framework
• agentic rag orchestration
FAQs
What is the typical timeline for deploying an AI agent?
The initial co-creation phase usually takes 30 to 60 days to build a production-grade agent, while full internal enablement and scale can take between 90 to 180 days.
How does the platform handle large-scale data ingestion?
The system is built for enterprise scale and has a proven track record of securely ingesting and processing over 160 million documents for global industry leaders.
Can Iris.ai help reduce the operational costs of using LLMs?
Yes, users typically see savings of 35% or more on LLM usage costs through optimized orchestration and more efficient workflow management provided by the platform.
Does Iris.ai provide tools for monitoring AI performance?
Every deployment includes a real-time monitoring dashboard and a custom evaluation framework to measure quality, evaluate answers, and ensure continuous operational excellence.
Pricing Plans
Enterprise
Unknown Price• Agentic RAG workflows
• Custom evaluation framework
• Real-time monitoring dashboard
• Secure document ingestion
• Team training and certification
• CI/CD best practices
• Ongoing platform updates
• Dedicated expert support
Job Opportunities
Senior NLP / ML Researcher (LLM Evaluation & Agentic Systems)
Deploy and manage agentic RAG workflows for enterprise scale with a platform that orchestrates data ingestion, automated evaluation, and real-time monitoring.
Education Requirements:
PhD degree
Experience Requirements:
Experience with Python (Programming Language)
Experience with Machine Learning
Experience with Natural Language Processing (NLP)
Experience with Large Language Models (LLM)
Other Requirements:
Published research papers or contributions to research
Participation in open source ML/NLP projects
Located within a European time zone (UTC+0 to UTC+3)
Leading successful EU or national R&D grant proposals
Responsibilities:
LLM Evaluation
Agentic Systems
Research & Development
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