# AI Orchestration

**Canonical:** https://apis.io/providers/ai-orchestration-jp/  
**Website:** https://ai-orchestration.jp/  
**APIs profiled:** 0

AI Orchestration is a Japanese AI consultancy and integration studio that designs and implements enterprise AI solutions. They offer AI infrastructure, ChatGPT plugin development, public grant search automation, and AI orchestration services, helping companies turn AI concepts into usable products. The team, led by founder Masaki Yamamoto, brings experience from Fujitsu, Dentsu, and Nomura Research Institute.

## Kin Score — 7.3 / 100 (minimal)

Scored 2026-11-06 under rubric 0.27.0. Trend: flat (-3.9 from 11.2).

| Facet | Score |
|---|---|
| Discoverability | 49.1 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 5.3 |
| Developer Ergonomics | 2.4 |
| Access Clarity | 10.5 |

Regulatory layer — **Horizontal (data, software, accessibility, platform)**: 9.1 (matched via fallback).

## Agent readiness — 3.1 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | documented |
| 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)

- **Ai Orchestration Jp Domain Security** — TLSv1.3 · DMARC

## Tags

Artificial Intelligence, Consulting, Japan, Enterprise AI, Integration

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