Traverse

Traverse is a San Francisco data research lab, founded in 2025 by Lance Yan and backed by Y Combinator (W26) along with angel investors from OpenAI, Google DeepMind, Anthropic, and Meta. Traverse partners with frontier AI labs to solve "non-verifiable" work: the ambiguous, taste-dependent, long-horizon tasks in domains like law, healthcare, sales, and writing where reinforcement learning lacks the clean deterministic feedback loops it has in math and coding. The lab captures real human expert behavior in highly controlled settings and turns judgment, taste, and reasoning into high-fidelity training environments for frontier models. As of mid-2026 Traverse is an early-stage research lab with no public API, developer platform, SDKs, or product documentation — its public surface is a manifesto-style website, a blog, careers pages, and a contact CTA for prospective lab partners.

Traverse is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, Training Data, and Data Labeling.

Traverse’s developer surface includes engineering blog and 3 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

4.2/100 minimal ▬ flat Agent 0/100 human only Front door AI 4/6 moderate Heartbeat 17/100 dormant Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Company Artificial Intelligence Machine Learning Training Data Data Labeling Research Lab Frontier Models Reinforcement Learning Y Combinator

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 4.2/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 0.8 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

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

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Access Clarity scores 0.0 — up to +20.0 composite points
Developer Ergonomics scores 2.4 — up to +19.5 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/traverse/apis.yml: open an issue on github.com/api-evangelist/traverse to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Traverse Domain Security

TLSv1.3 · HSTS

SECURITY

Resources

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: traverse
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/traverse.png
name: Traverse
description: 'Traverse is a San Francisco data research lab, founded in 2025 by Lance Yan and backed by Y Combinator (W26)
  along with angel investors from OpenAI, Google DeepMind, Anthropic, and Meta. Traverse partners with frontier AI labs to
  solve "non-verifiable" work: the ambiguous, taste-dependent, long-horizon tasks in domains like law, healthcare, sales,
  and writing where reinforcement learning lacks the clean deterministic feedback loops it has in math and coding. The lab
  captures real human expert behavior in highly controlled settings and turns judgment, taste, and reasoning into high-fidelity
  training environments for frontier models. As of mid-2026 Traverse is an early-stage research lab with no public API, developer
  platform, SDKs, or product documentation — its public surface is a manifesto-style website, a blog, careers pages, and a
  contact CTA for prospective lab partners.'
url: https://raw.githubusercontent.com/api-evangelist/traverse/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-no-api-surface
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- Training Data
- Data Labeling
- Research Lab
- Frontier Models
- Reinforcement Learning
- Y Combinator
tags_raw:
- Company
- Artificial Intelligence
- Machine Learning
- Training Data
- Data Labeling
- Research Lab
- Frontier Models
- Reinforcement Learning
- Y Combinator
- Machine-Learning
apis: []
common:
- type: DomainSecurity
  url: security/traverse-domain-security.yml
- type: Website
  url: https://traverse.so/
- type: Blog
  url: https://traverse.so/blog
- type: GitHubOrganization
  url: https://github.com/traverse-so
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-21'
  status: minimal
  artifacts_added: 1
  pass: local-v1

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