# Standard Cognition

**Canonical:** https://apis.io/providers/standard-cognition/  
**Website:** https://standard.ai/  
**APIs profiled:** 0

Standard Cognition (now Standard AI) is a San Francisco company that builds AI-powered autonomous checkout and computer-vision technology for physical retail, letting shoppers pick up items and walk out without scanning or waiting in line. Founded in 2017 and backed by Initialized Capital, it pairs ceiling-mounted cameras with real-time deep-learning models to power cashierless stores and, more recently, retail inventory and shopper-behavior analytics. The company runs an open-source GitHub organization for its infrastructure tooling but publishes no public developer API, SDK, or OpenAPI surface.

## Kin Score — 1.9 / 100 (minimal)

Scored 2026-08-19 under rubric 0.12.0. Trend: flat (-4.3 from 6.2).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 2.6 |
| Developer Ergonomics | 0.0 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

Regulatory layer — **Payments**: 9.4 (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)

- **Standard Cognition Domain Security** — TLSv1.3 · DMARC

## Tags

Company, Enterprise Saas, Computer Vision, Artificial Intelligence, Retail Technology, Autonomous Checkout, Retail Analytics

---

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