# Standard Intelligence

**Canonical:** https://apis.io/providers/standard-intelligence/  
**Website:** https://si.inc  
**APIs profiled:** 0

Standard Intelligence (si.inc) is an aligned AGI research lab based in San Francisco building AI models that explore and learn like humans do. Its work includes hertz-dev, an open-source 8.5B-parameter autoregressive base model for interactive, full-duplex conversational audio; a fully general computer-action model that navigates websites, executes CAD sequences, and drives autonomously at 30 FPS; and The Heap, a 30-petabyte data-storage system built in San Francisco for under $500,000. The company has raised $75M from Sequoia and Spark Capital. It publishes research posts and open-source code on GitHub but does not currently expose a public developer API.

## Kin Score — 5.3 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 5.3).

| 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 |

## 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 Intelligence Domain Security** — TLSv1.3 · DNSSEC

## Tags

Company, Artificial Intelligence, AGI, Machine-Learning, Foundation Models, Speech AI, Research, Open-Source

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