No free plan. The cheapest way in is Enterprise at Custom. Consider free alternatives in the ai memory & search category if budget is tight.
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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Last verified March 2026