# Nuance Labs

**Canonical:** https://apis.io/providers/nuance-labs/  
**Website:** https://www.nuancelabs.ai/  
**APIs profiled:** 0

Nuance Labs is an early-stage AI research company based in Seattle, Washington, building a human foundation model with emotional intelligence. It is developing a real-time, full-duplex audiovisual system that sees, hears, reasons, speaks, and expresses simultaneously, reading tone, facial expression, and hesitation to enable face-to-face AI interaction that feels human. Backed by Accel and Lightspeed Venture Partners, the company is pre-product: as of this enrichment pass it publishes only a marketing site (About, Press, Contact, Careers) with a "Get Demo" / early-access call to action, and exposes no public API, developer portal, documentation, SDKs, or other developer surface.

## Kin Score — 5.0 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 5.0).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| 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)

- **Nuance Labs Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, Foundation Model, Emotional Intelligence, Conversational AI, Multi-Modal, Real-Time, Seattle

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