Iris.ai - Agentic RAG-as-a-Service for enterprise AI

What is Iris.ai

Building and maintaining reliable AI systems that deliver real business value remains one of the biggest challenges facing enterprises today. Many organizations invest heavily in AI initiatives only to find their RAG (Retrieval-Augmented Generation) systems become unreliable, drift out of sync with business needs, or fail to scale beyond proof-of-concept. This is exactly the problem Iris.ai was built to solve.

Iris.ai is an enterprise-grade Agentic RAG-as-a-Service platform designed specifically for organizations that need to build, manage, and monitor production-ready AI systems. Unlike generic LLM platforms that treat RAG as a simple feature, Iris.ai focuses on creating persistent, trustworthy agentic systems that evolve with your business requirements. The platform has securely ingested over 160 million documents and evaluated more than 200,000 answers across 50+ real-world use cases, delivering measurable ROI that enterprise leaders can trust.

The numbers speak for themselves: organizations using Iris.ai achieve over 35% savings on LLM usage costs while accelerating AI time-to-market by over 80%. These aren't theoretical projections—they're results from production deployments across manufacturing, government, and telecommunications sectors.

The platform serves demanding enterprise clients including ArcelorMittal (the world's largest steel manufacturer), the Finnish Food Authority, and global telecommunications companies. What unites these organizations is their need for AI systems they can depend on—systems that handle sensitive data securely, integrate with existing workflows, and deliver consistent, auditable results.

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Core Features of Iris.ai

What makes Iris.ai different from other AI platforms is its comprehensive approach to the entire AI system lifecycle. Rather than offering isolated tools, the platform provides a unified environment for building, deploying, and governing production-grade agentic RAG systems.

Agentic RAG System Development is the cornerstone capability. You can work with Iris.ai's expert team to build production-level intelligent agents that combine retrieval accuracy with autonomous decision-making. Most organizations see their first working agent within 30-60 days, with the system designed to handle complex, multi-step workflows rather than simple document Q&A.

The Real-Time Monitoring Dashboard gives you complete visibility into AI agent performance. Using custom monitoring frameworks, you can track response quality, latency, and accuracy metrics in real time. This isn't just about catching errors—it's about understanding how your agents perform under different conditions and continuously improving them based on data.

For organizations with large document repositories, the Secure Data Ingestion pipeline processes information at massive scale while maintaining enterprise security standards. With over 160 million documents processed, the platform has proven its ability to handle sensitive materials including patents, research papers, and internal documentation.

The Custom Evaluation Framework lets you define what "good" looks like for your specific business context. Built on experience from 50+ use cases and 200,000+ answer evaluations, you can create custom metrics that align with your business objectives—whether that's accuracy, relevance, compliance, or response style.

Additional capabilities include Prompt Engineering & Optimization with hands-on expert guidance, CI/CD Best Practices for continuous integration and deployment of AI agents, Team Certification Training to build internal capabilities, and ongoing Performance Governance & Compliance to ensure systems meet regulatory requirements as they scale.

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Who Uses Iris.ai

Understanding how similar organizations use Iris.ai helps you envision what's possible for your team. Here are the primary use cases the platform addresses.

Manufacturing R&D Optimization represents one of Iris.ai's strongest success stories. ArcelorMittal, the world's largest steel manufacturer, integrated Iris.ai's Axion product into their R&D workflow to address a critical bottleneck: patent review that previously took weeks to months. By embedding AI-powered retrieval directly into their research processes, the team dramatically shortened development timelines while actually reviewing more patents than before. The key insight was making relevant patents discoverable in minutes rather than manually searching through thousands of documents.

Public Sector Crisis Response is where the platform's speed and accuracy save real time. The Finnish Food Authority needed to quickly narrow down relevant research during real-time crisis situations—like avian flu outbreaks—where every hour matters. Using RSpace, researchers could rapidly locate relevant academic papers across disciplines, something that would otherwise require days of manual literature review. This capability proved invaluable when dealing with emerging health situations where traditional research methods simply couldn't keep pace.

Cross-Disciplinary Knowledge Management addresses a universal challenge in research-intensive organizations. Scientists and researchers often need information from fields outside their immediate expertise, but navigating unfamiliar literature is time-consuming and error-prone. Iris.ai enables quick discovery of relevant research across boundaries, helping teams identify connections they might otherwise miss.

Telecommunications Project Delivery demonstrates the platform's enterprise readiness. One global telecom company evaluated 21 different AI vendors before choosing Iris.ai—the only platform that could deliver a complete, production-ready solution within their tight timeline. The system went live within weeks, outperforming all competitors on both technical capability and time to value.

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Getting Started: Iris.ai Implementation Path

One of the things that sets Iris.ai apart is its structured approach to enterprise AI deployment. Rather than dropping a complex system on your team and hoping for the best, the platform follows a proven three-phase methodology that delivers value at each stage.

Phase 1: Co-Create (30-60 days) begins with deep collaboration between your team and Iris.ai experts. Together, you define business requirements, identify priority use cases, and build the first production agent. This phase ensures the system aligns with your actual workflows rather than theoretical requirements. Most organizations see their first working agent within this window, giving you something tangible to evaluate before deeper investment.

Phase 2: Enable (30-90 days) focuses on optimization and capability building. Your team works with Iris.ai specialists on prompt engineering to fine-tune agent responses, receives certification training to build internal expertise, and establishes CI/CD pipelines for ongoing deployment. By the end of this phase, your internal team has the skills and processes to manage agents independently.

Phase 3: Expand is about scaling success. Organizations typically deploy 3-5 AI agents with 5+ active use cases, continuously monitoring performance and expanding into new areas. This phase is ongoing, with Iris.ai providing governance support to ensure systems remain accurate and compliant as they grow.

Before You Begin, ensure you have clarity on three things: your primary business use case (start with one or two high-impact areas rather than trying to automate everything at once), your internal stakeholder requirements (getting alignment across IT, business units, and leadership smooths the path significantly), and a core team who will own the system (even with Iris.ai's support, successful deployment requires internal ownership).

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Iris.ai Pricing Plans

Iris.ai operates on an enterprise-custom pricing model, meaning specific costs depend on your organization's scale, use cases, and deployment requirements. This approach allows the platform to tailor solutions rather than forcing you into a one-size-fits-all package.

While exact pricing requires discussion with the sales team, the ROI data suggests significant value: over 35% savings on LLM costs and 80% acceleration in AI time-to-market. For enterprises spending significantly on AI development or struggling with failed proof-of-concepts, these savings often dwarf the platform investment.

The pricing model reflects the comprehensive nature of the service—you're not just getting software, but expert consultation, implementation support, and ongoing governance. This end-to-end approach is precisely why organizations like ArcelorMittal and major telecommunications companies chose Iris.ai over simpler, cheaper alternatives that couldn't deliver production-ready results.

To get accurate pricing for your organization, the best approach is requesting a demo and discussing your specific requirements. The team will work with you to understand your use cases and propose a scope that delivers measurable return on investment.

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Frequently Asked Questions