# AV女優判定AI

**Canonical:** https://apis.io/providers/ai-for-av-com/  
**Website:** https://ai-for-av.com/  
**APIs profiled:** 0

AV女優判定AI provides an AI-powered service that identifies adult video actresses from package images or screenshots with high accuracy. The Japanese website offers a searchable database of performers, leveraging machine learning to match visual inputs to actress names. It includes affiliate links and adult content warnings, targeting users over 18. The service aims to streamline content management and fan identification in the adult entertainment industry.

## Kin Score — 6.0 / 100 (minimal)

Scored 2026-11-02 under rubric 0.26.0. Trend: flat (+0.0 from 6.0).

| Facet | Score |
|---|---|
| Discoverability | 48.2 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 0.0 |
| Access Clarity | 10.5 |

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

## 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)

- **Ai For Av Com Domain Security** — TLSv1.3 · HSTS · DNSSEC · DMARC

## Tags

Adult, Artificial Intelligence, Video, Japanese, Entertainment

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