Iris.ai
Enterprise retrieval and evaluation platform for secure agentic AI over private corpora with workflows for ingestion testing and governance.
Overview
ai focuses on safe high quality answers from your organization’s data. The platform ingests documents from repositories wikis and cloud storage then builds governed indexes and retrieval pipelines with access controls. Teams design workflows that define sources prompts guardrails and evaluation steps so results are measurable and compliant.
Evaluation runs combine automatic scores with rubric based human review to test coverage hallucinations and sensitivity before rollout. ai monitors usage and quality drift and supports red teaming to harden prompts and restrict unsafe behaviors. Integrations connect to popular vector stores model providers and identity systems so enterprises keep existing investments.
Reporting packages evidence for audits and vendor assessments a requirement in regulated sectors. Pricing targets enterprises and is offered by quote with pilots to validate accuracy privacy and impact on target use cases such as support knowledge search and research assistance.
Key features
- Governed Ingestion: Connect wikis drives and repos then normalize content with metadata access rules and retention policies for compliance
- Evaluation Workflows: Run automatic metrics and human rubrics to measure accuracy hallucination rate and coverage before launch
- Guardrails and Policies: Define prompts filters and safety limits that block sensitive data flow and unsafe responses in production
- Observability and Drift: Track quality usage and model costs then alert owners when performance moves outside accepted ranges
- Integrations: Use existing vector stores model providers and identity controls so deployments align with current architecture
- Red Teaming: Exercise prompts tools and environments to uncover jailbreaks and leakage risks before go live
- Approval Gates: Enforce staged reviews so only tested pipelines graduate to production for specific user groups
- Audit Ready Reports: Export evidence of controls evaluations and outcomes for regulators and procurement teams
Best for
- Stand up secure knowledge assistants for employees that search approved sources with clear citations
- Reduce support handle time by routing assistants to articles with evaluation backed accuracy and policy bounds
- Enable research teams to explore large archives and synthesize findings with traceable sources for compliance
- Run pilots that compare prompts models and retrieval settings to pick the highest quality approach
- Prepare audit evidence with documented controls and results to satisfy internal and external requirements
- Connect identity and permissions so assistants respect document level access across departments
- Monitor drift and improve routing as content changes and usage grows
- Roll out assistants globally with region aware storage and processing where required
Capabilities
Governed sources
Connect drives wikis and repos then normalize content with metadata and access controls that match corporate policy.
Quality and safety
Test prompts and settings with metrics and human review to reduce hallucinations and exposure risks.
Policies and guardrails
Apply filters roles and safety limits so assistants answer within approved boundaries.
Drift and reporting
Track quality usage and costs and export evidence for audits and stakeholder updates.
Frequently Asked Questions
What does pricing look like for Iris.ai?
Iris.ai sells to enterprises by quote with pilots to validate accuracy privacy and ROI on target use cases before broad rollout.
How do you ensure compliance readiness?
Governed ingestion policy control evaluation runs and reporting create a clear audit trail for regulators and procurement.
Will this work with our stack?
Integrations connect to common vector stores model APIs and identity systems so teams keep current investments.
How do you prevent unsafe outputs?
Guardrails policies and red teaming restrict unsafe behavior and block sensitive data flow in production.
Can we measure improvements over time?
Dashboards expose quality drift usage and costs so owners can iterate safely with evidence for each change
