Mecka AI

Mecka AI is the data and deployment layer for physical AI — it captures, structures, and evaluates real-world human and robot activity into datasets that robotics and embodied-AI teams can train on. Rather than building robots, Mecka positions itself as the integrator of the robotics ecosystem, plugging into hardware makers, frontier model labs, and commercial partners and owning the last-mile data, annotation, QA, and evaluation layer between them. Its Egoverse dataset captures large-scale first-person (egocentric) human activity across tasks and environments, and a companion iOS app lets teams collect task-specific data at scale. Mecka is backed by Forerunner Ventures and raised a $60M Series A in 2026. As of this pass Mecka publishes a marketing site, an iOS data-collection app, and the Egoverse data platform, but no public developer API, SDK, or documentation surface.

Mecka AI is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Physical AI, Robotics, and Embodied AI.

🌐 Visit website 📡 Profile data on GitHub

6.0/100 minimal ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Heartbeat 100/100 alive Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Company Artificial Intelligence Physical AI Robotics Embodied AI Training Data Datasets Machine Learning Computer Vision

Kin Score

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

Composite quality — 6.0/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 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

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
Developer Ergonomics scores 0.0 — up to +20.0 composite points
Access Clarity scores 10.5 — up to +17.9 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/mecka-ai/apis.yml: open an issue on github.com/api-evangelist/mecka-ai 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)	standard	17.2 (thin)	-	-

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.

Mecka Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

Company 4

The organization behind the API

Website

Website

Careers

Careers

LinkedIn

Twitter

Twitter

Other 2

Properties that don't map to a standard resource type

Platform

Platform

MobileApp

MobileApp

Source (apis.yml)

aid: mecka-ai
name: Mecka AI
description: Mecka AI is the data and deployment layer for physical AI — it captures, structures, and evaluates real-world
  human and robot activity into datasets that robotics and embodied-AI teams can train on. Rather than building robots, Mecka
  positions itself as the integrator of the robotics ecosystem, plugging into hardware makers, frontier model labs, and commercial
  partners and owning the last-mile data, annotation, QA, and evaluation layer between them. Its Egoverse dataset captures
  large-scale first-person (egocentric) human activity across tasks and environments, and a companion iOS app lets teams collect
  task-specific data at scale. Mecka is backed by Forerunner Ventures and raised a $60M Series A in 2026. As of this pass
  Mecka publishes a marketing site, an iOS data-collection app, and the Egoverse data platform, but no public developer API,
  SDK, or documentation surface.
url: https://raw.githubusercontent.com/api-evangelist/mecka-ai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- forerunner-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:
  - security
  generated: '2026-11-06'
  method: derived
image: https://www.mecka.ai/og.jpg
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Artificial Intelligence
- Physical AI
- Robotics
- Embodied AI
- Training Data
- Datasets
- Machine Learning
- Computer Vision
tags_raw:
- Company
- Ai
- Physical AI
- Robotics
- Embodied AI
- Training Data
- Datasets
- Machine Learning
- Computer Vision
- Machine-Learning
- Computer-Vision
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/mecka-ai-domain-security.yml
- type: Website
  url: https://mecka.ai
- type: PrivacyPolicy
  url: https://mecka.ai/privacy
- type: Platform
  name: Egoverse
  url: https://egoverse.ai/
- type: MobileApp
  name: Mecka for iOS
  url: https://apps.apple.com/us/app/mecka/id6758835773
- type: Careers
  url: https://mecka.ai/careers
- type: LinkedIn
  url: https://www.linkedin.com/company/mecka-ai
- type: Twitter
  url: https://x.com/MeckaAI
x-enrichment:
  date: '2026-07-20'
  status: minimal
  artifacts_added: 1
  pass: local-v1

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