# Iris.ai

**Canonical:** https://apis.io/providers/irisai/  
**Website:** https://iris.ai  
**APIs profiled:** 0

Iris.ai is a Norwegian AI company providing an AI knowledge foundation for regulated enterprises, turning complex enterprise and scientific data into trustworthy, compliant AI. Its platform spans three products — Axion (data organization for AI-ready intelligence), Neuralith (enterprise knowledge integration into AI engines), and RSpace, the Researcher Workspace that lets R&D teams, librarians, and academics search, filter, analyze, extract, summarize, and report across scientific literature. Iris.ai is backed by 500 Global. The company does not currently publish a public developer API, SDKs, or a developer portal; this profile captures its public identity and domain-security posture pending any future developer surface.

## Kin Score — 6.0 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 6.0).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 4.8 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (1)

- **Irisai Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, Machine-Learning, Research, Knowledge-Management, Enterprise, Data

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/irisai/). Scores are computed from the provider's own public artifacts under a published rubric.