# Uare.ai

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

Uare.ai is a consumer Individual AI platform founded by LivePerson founder Rob LoCascio that lets people build and own a personal AI trained on their own voice, knowledge, and experiences, organized by its proprietary Human Life Model across seven dimensions (Identity, World, Story, Mindset, Drive, Pattern, Growth). The company raised a $10.3M seed led by Mayfield and Boldstart Ventures in November 2025. Memberships are metered in Credits and include MCP tool integrations that connect an Individual AI to 1000+ applications, with iOS and Android apps alongside the web app at app.uare.ai. Uare.ai publishes no public developer API, API documentation, or SDKs as of July 2026.

## Kin Score — 16.7 / 100 (emerging)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 16.7).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 2.4 |
| Commercial Clarity | 52.6 |
| Access Clarity | 52.6 |

## 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 (2)

- **Uare Ai Domain Security** — TLSv1.3 · HSTS · DNSSEC · DMARC
- **Uare Ai Trust Center** — trust center published

## Tags

Company, Artificial Intelligence, Personal AI, Digital Twin, Consumer, Voice, MCP

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