# Aquin

**Canonical:** https://apis.io/providers/aquin/  
**Website:** https://aquin.app/  
**APIs profiled:** 0

Aquin Labs provides a developer environment for foundation models, offering libraries, frameworks, and tools to build, train, and interpret large AI models. Their platform supports applied machine learning across physics, life sciences, industrial and frontier verticals, emphasizing safety, precision, and open-source tooling. Backed by programs like Emergent Ventures, NVIDIA Inception, and Founders Inc, Aquin aims to accelerate ML development with reproducible, responsible AI workflows.

## Kin Score — 9.8 / 100 (minimal)

Scored 2026-11-06 under rubric 0.27.0. Trend: flat (-4.0 from 13.8).

| Facet | Score |
|---|---|
| Discoverability | 49.1 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 13.2 |
| Developer Ergonomics | 11.9 |
| Access Clarity | 10.5 |

Regulatory layer — **AI & Biometrics**: 9.1 (matched via residual_tags).

## 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 |
| Delegated Identity | no |
| Protected Resource Metadata | no |
| Dynamic Client Registration | no |
| Agentic Commerce | no |
| Pagination Ratio | na |
| Operation Distinctiveness | na |
| MCP Tool Quality | na |

## Access

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

## Security (1)

- **Aquin Domain Security** — TLSv1.3 · HSTS · DNSSEC · DMARC

## Tags

Artificial Intelligence, Machine Learning, Foundation Models, Open Source, Research

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