Iris.ai

Iris.ai suits research teams working with a defined technical corpus and domain-specific questions. Its current product family includes RSpace alongside broader enterprise knowledge tools. Agree on the exact product and data scope during evaluation. A map, extraction or generated answer should lead back to source evidence rather than becoming a substitute for reading the relevant scientific work.

A practical workflow

1. Define the research corpus and permitted source systems.
2. Arrange access to the relevant Iris.ai product.
3. Evaluate a small discovery or extraction question.
4. Check output against source documents and document exceptions.

Illustrative input

This example describes a task, not a tested output. Adjust the source material and settings to your own situation.

From these permitted materials papers, identify the reported test conditions and measured outcomes, leaving missing fields explicitly unresolved.

Result check: Compare extracted values and terminology with the papers and inspect how the system handles absent evidence.

Access and alternatives

Current access is sales-led, and a complete public price and recurring-usage boundary was not established. Pricing is marked unconfirmed. Confirm product modules, corpus size, integrations, deployment and service scope before purchasing; a demo is not a continuing free production plan.

Choose another workflow when its emphasis fits better: SciSpace for a more individual paper-reading workflow; Elicit for a different structured research approach.