# Adaption

**Canonical:** https://apis.io/providers/adaption/  
**Website:** https://docs.adaptionlabs.ai/  
**APIs profiled:** 2

Adaption (Adaption Labs) is a San Francisco AI research company building adaptive, continuously-learning AI systems rather than relying on ever-larger static models. Founded in 2026 by former Cohere leaders Sara Hooker and Sudip Roy, it exited stealth with a $50M seed round led by Emergence Capital. Its first product, Adaptive Data, exposes a REST API and official Python SDK to ingest, adapt, evaluate, and export model-ready training datasets — folding data-optimization techniques usually reserved for frontier labs into a self-serve workflow for everyday teams.

## Kin Score — 36.6 / 100 (thin)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 36.6).

| Facet | Score |
|---|---|
| Discoverability | 68.5 |
| Contract Quality | 55.6 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 45.2 |
| Commercial Clarity | 34.2 |
| Access Clarity | 34.2 |

## Agent readiness — 15.4 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | yes |
| 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).

## APIs (2)

- **Adaption Datasets API** — Create, list, run, evaluate, download, and publish adaptive datasets.
- **Adaption Upload API** — Pre-signed direct-to-S3 upload lifecycle for file-sourced datasets.

## Security (1)

- **Adaption Domain Security** — TLSv1.2 · DMARC

## Tags

Company, Artificial Intelligence, Machine-Learning, Training Data, Datasets, Data Augmentation, LLM, Model Training, Developer Tools

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