SUPA

SUPA is an AI data infrastructure company that helps machine learning teams build high-quality training datasets through a blend of technology and expert human annotation. It delivers data labeling and curation services spanning RLHF, LLM training, semantic segmentation, image classification, and medical imaging annotation, drawing on domain-expert networks across healthcare, manufacturing, robotics, agriculture, design, and industrial sectors. SUPA claims annotation accuracy above 95% and delivery of over one million data points per week. It publishes datasets on Hugging Face and is backed by 500 Global. As of enrichment, SUPA exposes no public developer API, SDK, MCP server, or /.well-known/ discovery surface — engagement is through its sales/contact channels.

SUPA is profiled on the APIs.io network. Tagged areas include Company, Data Labeling, Data Annotation, Training Data, and Machine Learning.

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

🌐 Visit website 📡 Profile data on GitHub

4.6/100 minimal ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Heartbeat 0/100 dormant Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Company Data Labeling Data Annotation Training Data Machine Learning Artificial Intelligence Data Curation RLHF Computer Vision

Kin Score

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

Composite quality — 4.6/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 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/supa/apis.yml: open an issue on github.com/api-evangelist/supa 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 →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
ONVIF Video Analytics (video surveillance, edge cameras, computer vision)	undeclared	-	-	1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Security Posture 1

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

Supa Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

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: supa
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/supa.png
name: SUPA
description: SUPA is an AI data infrastructure company that helps machine learning teams build high-quality training datasets
  through a blend of technology and expert human annotation. It delivers data labeling and curation services spanning RLHF,
  LLM training, semantic segmentation, image classification, and medical imaging annotation, drawing on domain-expert networks
  across healthcare, manufacturing, robotics, agriculture, design, and industrial sectors. SUPA claims annotation accuracy
  above 95% and delivery of over one million data points per week. It publishes datasets on Hugging Face and is backed by
  500 Global. As of enrichment, SUPA exposes no public developer API, SDK, MCP server, or /.well-known/ discovery surface
  — engagement is through its sales/contact channels.
url: https://raw.githubusercontent.com/api-evangelist/supa/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- 500-global
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Data Labeling
- Data Annotation
- Training Data
- Machine Learning
- Artificial Intelligence
- Data Curation
- RLHF
- Computer Vision
tags_raw:
- Company
- Data Labeling
- Data Annotation
- Training Data
- Machine Learning
- Artificial Intelligence
- Data Curation
- RLHF
- Computer Vision
- Machine-Learning
- Computer-Vision
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://supa.so
- type: Blog
  url: https://blog.supa.so
- type: DomainSecurity
  url: security/supa-domain-security.yml
- type: LLMsTxt
  url: llms/supa-llms.txt
x-enrichment:
  date: '2026-07-21'
  status: minimal
  artifacts_added: 2
  pass: local-v1

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get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
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apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
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