I
About
Iris.ai is an enterprise-grade AI platform purpose-built for deploying secure, high-quality Agentic RAG (Retrieval-Augmented Generation) systems. It provides a single cohesive environment for ingesting massive document corpora, orchestrating multi-step AI agents, evaluating LLM output quality, and monitoring production workflows over time. Its three flagship products — Axion™, Neuralith™, and RSpace™ — address different facets of the enterprise AI lifecycle, from rapid data ingestion and search to deep research synthesis and real-time monitoring. Organizations benefit from a structured onboarding journey: a co-creation phase to build an initial agent with Iris.ai's expert team, an enablement phase to train internal staff and expand use cases, and an ongoing expansion phase where clients scale and own their AI workflows independently. Proven results across manufacturing, public sector, and telecommunications include ingesting over 160 million documents, evaluating 200,000+ answers across 50+ use cases, achieving 35%+ savings on LLM usage costs, and accelerating AI go-to-market by over 80%. Iris.ai is designed for enterprises that require privacy, governance, and production reliability — not just prototypes — making it the platform of choice for R&D teams, IP departments, and knowledge-intensive organizations worldwide.
Key Features
- Agentic RAG Workflow Orchestration: Build and manage multi-step Agentic RAG pipelines that connect, orchestrate, and retrieve information from large enterprise document repositories in a single platform.
- Scalable Document Ingestion: Securely ingest and process millions of documents — Iris.ai has handled over 160 million documents — across diverse formats and domains for enterprise knowledge retrieval.
- LLM Evaluation & Quality Monitoring: Evaluate AI-generated answers across dozens of use cases with a custom evaluation framework and real-time monitoring dashboards to ensure output quality and reliability.
- Flagship Product Suite: Axion™, Neuralith™, and RSpace™ address distinct enterprise needs including data ingestion, research synthesis, and collaborative AI-powered knowledge workspaces.
- Structured Enterprise Onboarding: A three-phase journey (Co-Create, Enable, Expand) guides enterprises from initial agent deployment to full internal ownership of scalable AI workflows within 90 days.
Use Cases
- Accelerating R&D processes by enabling researchers to rapidly ingest, search, and synthesize insights from millions of patents, papers, and technical documents.
- Deploying enterprise knowledge bases that allow employees to query internal documentation and get accurate, cited answers without manual search.
- Building AI-powered competitive intelligence and patent analysis systems for IP departments in manufacturing and technology firms.
- Supporting real-time crisis research in public sector and health organizations by rapidly surfacing relevant literature across disciplines.
- Enabling telecommunications companies to streamline project delivery and extract insights from niche technical research at speed.
Pros
- Production-Ready from Day One: Iris.ai delivers working, production-grade AI agents — not prototypes — within weeks, as validated by enterprise clients across multiple industries.
- Significant Cost & Time Savings: Clients report 35%+ reductions in LLM usage costs and 80%+ acceleration in AI go-to-market timelines, delivering measurable ROI.
- Enterprise Security & Governance: Built with enterprise-grade security, privacy, and governance controls, making it suitable for sensitive R&D, IP, and regulated industry use cases.
- Expert-Guided Implementation: The co-creation model pairs clients with Iris.ai's expert team, ensuring AI agents are tailored to specific organizational workflows rather than generic out-of-the-box solutions.
Cons
- Enterprise-Only Pricing: Iris.ai is positioned exclusively for enterprise customers with no self-service or SMB tier, requiring a demo request to access pricing and onboarding.
- Implementation Timeline Required: The structured onboarding process takes 30–90+ days, which may not suit organizations looking for an immediately deployable off-the-shelf solution.
- Limited Public Documentation: As an enterprise platform, detailed technical documentation and pricing are not publicly available, making initial evaluation dependent on a sales conversation.
Frequently Asked Questions
What is Agentic RAG and how does Iris.ai implement it?
Agentic RAG (Retrieval-Augmented Generation) combines autonomous AI agents with retrieval systems that pull relevant information from large document corpora before generating answers. Iris.ai provides a full platform to build, deploy, and monitor these multi-step agent workflows tailored to enterprise needs.
What are Axion™, Neuralith™, and RSpace™?
These are Iris.ai's three flagship products addressing different enterprise AI needs: Axion™ focuses on data ingestion and processing, Neuralith™ on AI orchestration and agent management, and RSpace™ on collaborative research and knowledge synthesis workspaces.
How long does it take to deploy a production AI agent with Iris.ai?
The co-creation phase (30–60 days) delivers an initial production-grade AI agent and monitoring dashboard. The full enablement phase (30–90 days additional) trains internal teams and scales to 3–5 live agents.
Which industries does Iris.ai serve?
Iris.ai serves a wide range of knowledge-intensive industries including manufacturing (e.g., ArcelorMittal), public sector research organizations, and global telecommunications companies.
How does Iris.ai help reduce LLM costs?
By optimizing retrieval pipelines, reducing unnecessary LLM calls, and implementing robust evaluation frameworks, Iris.ai clients achieve over 35% savings on LLM usage costs compared to unoptimized deployments.
