# Peak

**Canonical:** https://apis.io/providers/peak/  
**Website:** https://www.peak.net/  
**APIs profiled:** 0

Peak is a consumer brain-training mobile application operated by Synaptic Labs (formerly Peak Labs) that offers cognitive exercise games and personalized workouts spanning memory, attention, problem-solving, mental agility, language, and emotion. Its games and Advanced Training Programmes are developed with researchers from institutions including Cambridge, Yale, UCL, and King's College London. Peak Pro is a subscription that unlocks 40+ games, personalized workouts, performance analytics, and an AI Coach. Peak is a mobile-only consumer product distributed via the App Store and Google Play and does not currently publish a public API or developer platform.

## Kin Score — 10.4 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 10.4).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 7.1 |
| Commercial Clarity | 21.1 |
| Access Clarity | 21.1 |

Regulatory layer — **Education & Research**: 25.9 (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)

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

## Tags

Company, Consumer, Brain Training, Cognitive Training, Mobile App, Games, Health and Fitness, Education

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