Master Iris.ai with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Iris.ai requires a demo request to begin. Visit iris.ai and click 'Request a Demo' to start the evaluation process. The implementation follows three phases: Co
60 days) builds your first production agent, Enable (30
90 days) trains your team and scales to 3
5 agents, and Expand (ongoing) supports full organizational scaling.
💡 Quick Start: Follow these 4 steps in order to get up and running with Iris.ai quickly.
Explore the key features that make Iris.ai powerful for ai research workflows.
Goes beyond traditional RAG with AI agents that plan multi-step research workflows, cross-reference findings across document collections, and proactively identify knowledge gaps. Agents reason over documents rather than simply retrieving and summarizing them.
An R&D team needs to assess whether a new polymer material could replace existing components — the agent automatically searches patents, research papers, supplier specs, and internal test reports to build a comprehensive assessment.
Interactive collaborative workspace where researchers work alongside AI agents on complex analytical tasks. Supports iterative query refinement, cross-disciplinary narrowing, and real-time collaboration on literature review and data extraction workflows.
A public health team rapidly reviews avian flu literature across virology, epidemiology, and veterinary science databases during a disease outbreak, narrowing thousands of papers to actionable findings in hours instead of weeks.
Securely ingests and processes 160+ million documents including scientific papers, patents, technical reports, regulatory filings, and internal knowledge bases. Handles diverse document formats while maintaining data security and audit trails.
A pharmaceutical company ingests its entire patent portfolio plus competitor filings and regulatory submissions to build an AI-powered competitive intelligence system.
Built-in evaluation system that has assessed 200,000+ answers across 50+ use cases. Provides real-time monitoring dashboards tracking answer quality, response times, and cost metrics. Delivers 35%+ savings on LLM usage costs through intelligent optimization.
An enterprise AI team monitors answer accuracy across their deployed agents, catching quality degradation before it impacts researchers and optimizing model routing to reduce costs.
Breaks down research silos by enabling AI agents to identify relevant connections across scientific disciplines. Surfaces insights from adjacent fields that traditional domain-specific searches miss entirely.
A materials science team discovers relevant biological self-assembly research that inspires a novel manufacturing approach for nanostructured composites.
Three-phase deployment methodology — Co-Create (30-60 days), Enable (30-90 days), and Expand (ongoing) — that ensures production-grade AI agents, trained internal teams, and sustainable scaling with continuous governance.
A telecommunications company goes from zero AI research capability to 5+ production AI agents handling technology scouting, patent monitoring, and standards compliance within six months.
Agentic RAG extends traditional Retrieval-Augmented Generation by adding planning and reasoning capabilities. While standard RAG retrieves relevant documents and generates a response, Agentic RAG agents can plan multi-step research workflows, decide which document collections to query, cross-reference findings, and proactively identify gaps in the available information. This makes them significantly more capable for complex research tasks.
Iris.ai follows a three-phase approach. The Co-Create phase takes 30-60 days and results in a production-grade AI agent with a monitoring dashboard. The Enable phase (30-90 days) trains your team and expands to 3-5 production agents. The Expand phase is ongoing and focuses on scaling and governance. Most organizations see initial value within the first 60 days.
The platform supports scientific papers, patents, technical reports, regulatory filings, internal knowledge bases, and other structured and unstructured document types. It has ingested over 160 million documents across manufacturing, pharmaceutical, telecommunications, and public sector use cases.
Iris.ai is currently positioned as an enterprise platform with custom pricing and structured implementation. Individual researchers and small teams may find alternatives like Semantic Scholar, Connected Papers, or Elicit more accessible. Iris.ai is best suited for organizations with significant document volumes and complex research workflows.
Iris.ai is designed from the ground up for regulated industries. The platform includes enterprise-grade security controls, audit trails, access management, and compliance features appropriate for sectors like pharmaceuticals, manufacturing, and government. Specific security certifications and deployment options should be discussed during the demo process.
Iris.ai reports 35%+ savings on LLM usage costs through intelligent caching, optimized retrieval strategies, and model routing. Beyond direct cost savings, organizations typically see significant time savings — ArcelorMittal reported cutting weeks to months from R&D timelines, and an 80%+ acceleration on AI go-to-market has been documented across implementations.
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Tutorial updated March 2026