# Arzana

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

Arzana is an AI-powered Office Execution System for US manufacturers and distributors, founded in 2025 in San Francisco and backed by Y Combinator (Spring 2026). Arzana trains custom models on a factory's historical data and deploys agents that automate back-office work — quoting, estimating, order entry, purchasing, prospecting, outreach, CRM, AP 3-way match, AR tracking, and customer service — alongside a customizable ERP/CRM/MES with every agent pre-installed. Forward-deployed engineers configure each agent per operation; Arzana reports going live in 120 days with 99%+ accuracy. Arzana currently exposes no public developer API, SDK, or documentation surface; it does publish a /llms.txt for agent discovery.

## Kin Score — 10.4 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 10.4).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 2.4 |
| Commercial Clarity | 21.1 |
| Access Clarity | 21.1 |

## 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)

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

## Tags

Company, Manufacturing, ERP, AI Agents, Automation, CRM, Office Automation, Distribution, Y Combinator

---

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