# Distributional

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

Distributional is an a16z-backed company (security contact Scott Clark) tracked in the API Evangelist network. As of July 2026 distributional.com serves a pre-launch holding page ("Something new is coming") with no public API, backend, or authentication surface. The site does publish machine-readable discovery surfaces, however: an RFC 9116 security.txt at /.well-known/security.txt and an llms.txt with plain-markdown page twins for direct LLM ingestion. This profile captures those published surfaces and probed domain-security posture, and will be enriched further once the company launches a developer or API offering.

## Kin Score — 8.2 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+1.1 from 7.1).

| Facet | Score |
|---|---|
| Discoverability | 68.5 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 10.5 |
| Developer Ergonomics | 0.0 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 2.6 (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 | yes |
| Agent Card | no |
| Dry Run Mode | no |

## Access

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

## Security (2)

- **Distributional Domain Security** — TLSv1.3 · HSTS · DMARC
- **Distributional Vulnerability Disclosure** — security.txt · contact published

## Tags

Company, Artificial Intelligence, Machine-Learning, AI Testing, Reliability, MLOps

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