# DatologyAI

**Canonical:** https://apis.io/providers/datologyai/  
**Website:** https://datologyai.com  
**APIs profiled:** 0

DatologyAI is a data curation and optimization platform that automatically identifies, prioritizes, and curates training data at petabyte scale so teams can train machine learning and foundation models that are better, faster, and smaller. Founded on the principle that "models are what they eat," its research-driven pipeline improves model quality while cutting compute cost and training time, and is used by AI labs and enterprises building frontier open models. The company is venture-backed by Amplify Partners and Felicis. DatologyAI operates as an enterprise, contact-sales service and does not currently publish a public developer API, SDKs, or documentation portal.

## Kin Score — 10.6 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 10.6).

| 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 |

## 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)

- **Datologyai Domain Security** — TLSv1.3 · HSTS · DNSSEC · DMARC

## Tags

Company, Ai Ml, Data Curation, Machine-Learning, Training Data, Foundation Models, Data Optimization, Artificial Intelligence

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