# Unconventional AI

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

Unconventional AI (unconv.ai) is rethinking the foundations of the computer to optimize energy efficiency for AI, working to bring biology-scale efficiency to artificial intelligence through analog, dynamical-system hardware co-evolved with neural software. Founded by experts in AI systems, analog circuits, computing theory, and neuroscience, and backed by Felicis, the company publishes research including the Un-0 image-generation model built on coupled Kuramoto oscillators and an instruction set architecture for dynamical-system hardware, but does not yet publish a public API, developer portal, or SDKs.

## Kin Score — 4.9 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 4.9).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 2.6 |
| Developer Ergonomics | 2.4 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

Regulatory layer — **Energy & Utilities**: 8.1 (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)

- **Unconv Domain Security** — TLSv1.3 · DMARC

## Tags

Company, Artificial Intelligence, AI Hardware, Analog Computing, Energy Efficiency, Machine-Learning, Semiconductors

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