# Efference AI

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

Efference AI is a robotics perception and compute company building the hardware-plus-software foundation for data-driven robotics at scale. Founded in 2025 by Gianluca Bencomo and based in San Francisco (Y Combinator Fall 2025), Efference treats machine vision as a software problem, pairing affordable stereo-camera hardware with learned 3D-perception foundation models ("cheap hardware + great algorithms"). Its M1 device targets distributed, robotics-grade data collection at scale, while the H1 stereo system is built for real-time cloud inference with foundation models. The company serves autonomous vehicles, humanoid robots, and drones, and publishes hardware-interfacing documentation and firmware through its GitHub organization. As of this profile Efference exposes no public developer API, OpenAPI, or authenticated service surface.

## Kin Score — 8.2 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 8.2).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 2.6 |
| Developer Ergonomics | 14.3 |
| 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)

- **Efference Ai Domain Security** — TLSv1.3

## Tags

Company, Robotics, Computer-Vision, Perception, Hardware, Stereo Cameras, Edge AI, Autonomous Systems, Foundation Models

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