Elementary

Elementary is an AI-powered machine-vision inspection company for manufacturing, founded in 2017 in South Pasadena, California (originally Elementary Robotics). Its VisionStream self-training edge AI detects production defects in seconds without rules, labeling, or tuning, while the QualityOS cloud platform delivers traceability, analytics, and MES/ERP integration across production lines. Elementary inspects over one billion parts annually for Fortune 500 manufacturers across food & beverage, automotive, consumer packaged goods, electronics, and life sciences, and is backed by Rockwell Automation, Tiger Global, Toyota Ventures, Threshold Ventures, Fika Ventures, and Idealab. The company exposes no public developer API; this profile captures its identity, security posture, and public web properties.

Elementary is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Vision, Computer Vision, and Manufacturing.

Elementary’s developer surface includes engineering blog, support, and 8 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

6.5/100 minimal ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Full breakdown ↓
scored 2026-10-17 · rubric v0.24.0

0 APIs

Company Artificial Intelligence Machine Vision Computer Vision Manufacturing Quality Inspection Industrial Automation Defect Detection

Kin Score

Kin Score How this is scored →
scored 2026-10-17 · rubric v0.24.0

Composite quality — 6.5/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 1.8 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 1.4 / 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

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/.

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 github.com/api-evangelist/elementary: open an issue to ask a question, or submit a pull request to add artifacts. 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
Industrial Automation and Robotics	standard	19.4 (thin)	-	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.

Elementary Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

Support

Support

HelpCenter

HelpCenter

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

Company 6

The organization behind the API

Website

About

Blog

Partners

Careers

LinkedIn

Source (apis.yml)

aid: elementary
name: Elementary
description: Elementary is an AI-powered machine-vision inspection company for manufacturing, founded in 2017 in South Pasadena,
  California (originally Elementary Robotics). Its VisionStream self-training edge AI detects production defects in seconds
  without rules, labeling, or tuning, while the QualityOS cloud platform delivers traceability, analytics, and MES/ERP integration
  across production lines. Elementary inspects over one billion parts annually for Fortune 500 manufacturers across food &
  beverage, automotive, consumer packaged goods, electronics, and life sciences, and is backed by Rockwell Automation, Tiger
  Global, Toyota Ventures, Threshold Ventures, Fika Ventures, and Idealab. The company exposes no public developer API; this
  profile captures its identity, security posture, and public web properties.
url: https://raw.githubusercontent.com/api-evangelist/elementary/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- threshold-ventures
x-tier: stub
x-tier-reason: portfolio-lead
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: []
  generated: '2026-07-22'
  method: derived
image: https://assets.elementaryml.com/og-image-001.webp
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Artificial Intelligence
- Machine Vision
- Computer Vision
- Manufacturing
- Quality Inspection
- Industrial Automation
- Defect Detection
tags_raw:
- Company
- Ai
- Machine Vision
- Computer Vision
- Manufacturing
- Quality Inspection
- Industrial Automation
- Defect Detection
- Computer-Vision
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/elementary-domain-security.yml
- type: Website
  url: https://www.elementaryml.com/
- type: About
  url: https://www.elementaryml.com/about
- type: Blog
  url: https://www.elementaryml.com/blog
- type: Support
  url: https://elementarymlsupport.zendesk.com/
- type: HelpCenter
  url: https://elementarymlsupport.zendesk.com/
- type: PrivacyPolicy
  url: https://www.elementaryml.com/privacy-policy
- type: Partners
  url: https://www.elementaryml.com/partners
- type: Careers
  url: https://elementary.bamboohr.com/careers
- type: LinkedIn
  url: https://www.linkedin.com/company/elementary-ml
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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