# Waabi

**Canonical:** https://apis.io/providers/waabi/  
**Website:** https://waabi.ai  
**APIs profiled:** 0

Waabi is a Toronto-based artificial intelligence company founded in 2021 by Raquel Urtasun, pioneering Physical AI for autonomous driving and starting with self-driving trucks. Its flagship product, the Waabi Driver, pairs a generative-AI, end-to-end interpretable and verifiable autonomy stack with sensors and compute, trained and validated in Waabi World, a high-fidelity neural closed-loop simulator, and designed for factory-level OEM integration and large-scale commercialization. Waabi is a product/technology company: as of this enrichment pass it publishes no public developer API, SDK, MCP server, or developer portal, so this profile carries identity and domain-security signal rather than API artifacts.

## Kin Score — 7.6 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 7.6).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 2.4 |
| Commercial Clarity | 10.5 |
| Access Clarity | 10.5 |

## 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)

- **Waabi Domain Security** — TLSv1.3 · DMARC

## Tags

Company, Artificial Intelligence, Autonomous Vehicles, Self-Driving, Trucking, Robotics, Simulation, Physical AI

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