# Vivodyne

**Canonical:** https://apis.io/providers/vivodyne/  
**Website:** https://www.vivodyne.com/  
**APIs profiled:** 0

Vivodyne is a bio-AI company working to make human biology computable through large-scale human tissue engineering. Its TissueDisk platform cultivates hundreds of self-assembling, vascularized human tissues on a single microfluidic disk, and its autonomous HIVE robotic labs cultivate, perturb, and analyze thousands of tissues to generate multi-modal phenomic, transcriptomic, and proteomic data at single-cell resolution for pharmaceutical drug discovery and AI model training. The company operates as a B2B partner to pharma and does not publish a public developer API; this profile tracks its public web surface within the API Evangelist network.

## Kin Score — 7.6 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 7.6).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 7.1 |
| Commercial Clarity | 10.5 |
| Access Clarity | 10.5 |

Regulatory layer — **Health**: 12.5 (matched via 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 |

## Access

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

## Security (1)

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

## Tags

Company, Biotechnology, Life Sciences, Drug Discovery, Artificial Intelligence, Lab Automation, Organ-on-a-Chip

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