Shaip

Shaip provides a data platform for AI and machine learning, offering services such as data collection, annotation, labeling, and model evaluation. Their solutions help enterprises build high‑quality training datasets, accelerate AI development, and ensure data security and compliance across industries. Shaip’s platform supports custom workflows, expert review, and scalable pipelines for both image and text data, catering to sectors like healthcare, automotive, and finance.

Shaip is profiled on the APIs.io network. Tagged areas include Company, AI, Data-Annotation, Machine-Learning, and Platform.

Shaip’s developer surface includes engineering blog and 7 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

10.0/100 minimal Agent 17/100 agent aware Full breakdown ↓
scored 2026-10-31 · rubric v0.25.0 · Show this score on your site →

0 APIs

CompanyAIData-AnnotationMachine-LearningPlatform

Kin Score

Kin Score How this is scored →
scored 2026-10-31 · rubric v0.25.0

Composite quality — 10.0/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 3.6 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 2.7 / 15

Agent readiness — 17/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 10 / 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 4 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 6 / 6

Protected Resource Metadata 5 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

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
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)
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

9 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
Developer Ergonomics scores 2.4 — up to +19.5 composite points
Access Clarity scores 21.1 — up to +15.8 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/shaip/apis.yml: open an issue on github.com/api-evangelist/shaip 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.

Shaip Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

WellKnown

WellKnown

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

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

Other 1

Properties that don't map to a standard resource type

Hosts

Hosts

Source (apis.yml)

aid: shaip
name: Shaip
image: https://c0a6be58.delivery.rocketcdn.me/wp-content/uploads/2024/12/Homepage_SM.jpg
description: Shaip provides a data platform for AI and machine learning, offering services such as data collection, annotation,
  labeling, and model evaluation. Their solutions help enterprises build high‑quality training datasets, accelerate AI development,
  and ensure data security and compliance across industries. Shaip’s platform supports custom workflows, expert review, and
  scalable pipelines for both image and text data, catering to sectors like healthcare, automotive, and finance.
url: https://raw.githubusercontent.com/api-evangelist/shaip/refs/heads/main/apis.yml
x-type: company
x-source: harvest:g2-catalog
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-21 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-10-21'
modified: '2026-10-21'
tags:
- Company
- AI
- Data-Annotation
- Machine-Learning
- Platform
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: LLMsTxt
  url: llms/shaip-llms.txt
- type: WellKnown
  url: well-known/shaip-well-known.yml
- type: Hosts
  url: hosts/shaip-hosts.yml
- type: TermsOfService
  url: https://www.shaip.com/terms-of-service/
- type: PrivacyPolicy
  url: https://www.shaip.com/privacy-policy/
- type: DomainSecurity
  url: security/shaip-domain-security.yml
- type: Website
  url: https://www.shaip.com/
- type: Blog
  url: https://www.shaip.com/blog/
apis: []
x-coverage:
  state: none
  reason: no-developer-program
  detail: Shaip does not provide a public developer program or API documentation.
  evidence:
  - url: https://www.shaip.com/api-docs/
    status: 301
  checked: '2026-10-21'
x-enrichment:
  date: '2026-10-21'
  status: enriched
  artifacts_added: 10
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

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get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
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