Ooak Data

Ooak Data is an applied AI research lab building the data infrastructure for frontier AI. It turns real company data into reinforcement-learning environments where AI agents learn to work in the real world — sourcing multimodal enterprise data, anonymizing it into digital twins of tools like Slack, Gmail, Notion and Jira, and generating expert-calibrated, multi-step, multi-tool workflow tasks calibrated on the latest frontier models. It serves frontier AI labs, enterprise AI teams, and AI startups that need real-world evaluation infrastructure rather than synthetic benchmarks. Backed by Y Combinator (Summer 2026) and based in Paris, France. Ooak Data publishes research essays and an AI-friendly content surface (llms.txt + Markdown content negotiation) but does not currently expose a public API or developer platform.

Ooak Data is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, Reinforcement Learning, and AI Agents.

Ooak Data’s developer surface includes engineering blog, support, and 5 more developer resources.

🌐 Visit website 📡 Source on GitHub

12.3/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5

0 APIs

CompanyArtificial IntelligenceMachine LearningReinforcement LearningAI AgentsDatasetsEvaluationData Infrastructure

Kin Score

Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5

Composite quality — 12.3/100 · minimal

Contract Quality 0.0 / 25

Developer Ergonomics 1.3 / 20

Commercial Clarity 4.2 / 20

Operational Transparency 0.0 / 13

Governance 0.0 / 12

Discoverability 6.8 / 10

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 15

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/ooak-data: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: ooak-data
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/ooak-data.png
name: Ooak Data
description: Ooak Data is an applied AI research lab building the data infrastructure for frontier AI. It turns real company
  data into reinforcement-learning environments where AI agents learn to work in the real world — sourcing multimodal enterprise
  data, anonymizing it into digital twins of tools like Slack, Gmail, Notion and Jira, and generating expert-calibrated, multi-step,
  multi-tool workflow tasks calibrated on the latest frontier models. It serves frontier AI labs, enterprise AI teams, and
  AI startups that need real-world evaluation infrastructure rather than synthetic benchmarks. Backed by Y Combinator (Summer
  2026) and based in Paris, France. Ooak Data publishes research essays and an AI-friendly content surface (llms.txt + Markdown
  content negotiation) but does not currently expose a public API or developer platform.
url: https://raw.githubusercontent.com/api-evangelist/ooak-data/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- Reinforcement Learning
- AI Agents
- Datasets
- Evaluation
- Data Infrastructure
apis: []
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 2
  pass: local-v1
common:
- type: Website
  url: https://ooakdata.com
- type: Blog
  url: https://ooakdata.com/research
- type: Support
  url: https://ooakdata.com/contact
- type: TermsOfService
  url: https://ooakdata.com/terms
- type: PrivacyPolicy
  url: https://ooakdata.com/privacy
- type: LLMsTxt
  url: llms/ooak-data-llms.txt
- type: DomainSecurity
  url: security/ooak-data-domain-security.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io