3D Look

3DLOOK is an AI-powered body measurement and mobile body-scanning company whose computer-vision technology extracts 80+ accurate body measurements and 3D body models from just two smartphone photos. It offers two developer products: SAIA Perfect Fit / Mobile Tailor (a made-to-measure and size-recommendation API for apparel, uniforms, and on-demand manufacturing) and FitXpress (a body-composition and health/fitness insights API delivering weight prediction, BMI, body-fat, BMR, and 2D/3D body-progress visualization). Both expose REST APIs with API-key authentication, a JavaScript SDK, native iOS and Android capture SDKs, and embeddable photo-capture and measurement widgets.

3D Look publishes 2 APIs on the APIs.io network. Tagged areas include Company, Body Measurement, Computer-Vision, Artificial Intelligence, and Apparel.

3D Look’s developer surface includes documentation, API reference, pricing, support, authentication, and 14 more developer resources.

🌐 Visit website 📡 Source on GitHub

24.7/100 emerging ▬ flat Agent 5/100 agent aware saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

2 APIs

Company Body Measurement Computer-Vision Artificial Intelligence Apparel Fashion Technology Health and Fitness Sizing3D Body ScanningSDK

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 24.7/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 8.1 / 17

Access Clarity 5.4 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 6.5 / 9

Regulatory Posture 3.8 / 15

Agent readiness — 5/100 · 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 8 / 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 Health 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/3d-look: 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 →

APIs 2

Individual APIs this provider publishes, each with its own machine-readable definition.

SAIA Perfect Fit / Mobile Tailor (MTM) API

REST API to create a person, upload front and side photos, and asynchronously retrieve 80+ calculated body measurements for made-to-measure apparel, uniforms, and size recommend...

FitXpress API

REST API for body composition and health/fitness insights - body measurements, weight prediction, BMI/body-fat/BMR, 3D model prediction, and 2D/3D body-progress comparison, with...

Security Posture 2

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

3D Look Authentication

apiKey · 2 schemes

SECURITY

3D Look Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

X-MCPServerCandidate

X-MCPServerCandidate

LLMsTxt

LLMsTxt

Design & Contract 4

Pagination, idempotency, versioning, errors, and events

Components

ErrorCatalog

Conventions

Lifecycle

Build 3

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Packages

Packages

SDKs

SDKs

Access & Security 2

Authentication, authorization, and security posture

Authentication

Authentication

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 3

Pricing, plans, and the legal terms of use

Pricing

Pricing

PrivacyPolicy

PrivacyPolicy

TermsOfService

TermsOfService

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: 3d-look
name: 3D Look
description: '3DLOOK is an AI-powered body measurement and mobile body-scanning company whose computer-vision technology extracts
  80+ accurate body measurements and 3D body models from just two smartphone photos. It offers two developer products: SAIA
  Perfect Fit / Mobile Tailor (a made-to-measure and size-recommendation API for apparel, uniforms, and on-demand manufacturing)
  and FitXpress (a body-composition and health/fitness insights API delivering weight prediction, BMI, body-fat, BMR, and
  2D/3D body-progress visualization). Both expose REST APIs with API-key authentication, a JavaScript SDK, native iOS and
  Android capture SDKs, and embeddable photo-capture and measurement widgets.'
url: https://raw.githubusercontent.com/api-evangelist/3d-look/refs/heads/main/apis.yml
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - security
  generated: '2026-09-03'
  method: derived
image: https://3dlook.ai/wp-content/uploads/2024/12/fitxpress.jpg
x-type: company
x-source: vc-portfolio
x-backed-by:
- 500-global
x-tier: enriched
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-17'
tags:
- Company
- Body Measurement
- Computer-Vision
- Artificial Intelligence
- Apparel
- Fashion Technology
- Health and Fitness
- Sizing
- 3D Body Scanning
- SDK
tags_raw:
- Company
- Body Measurement
- Computer Vision
- Artificial Intelligence
- Apparel
- Fashion Technology
- Health and Fitness
- Sizing
- 3D Body Scanning
- SDK
apis:
- name: SAIA Perfect Fit / Mobile Tailor (MTM) API
  description: REST API to create a person, upload front and side photos, and asynchronously retrieve 80+ calculated body
    measurements for made-to-measure apparel, uniforms, and size recommendation.
  humanURL: https://saia.3dlook.me/docs/
  baseURL: https://saia.3dlook.me/api/v2/
  tags:
  - Body Measurement
  - Made-to-Measure
  - Sizing
  tags_raw:
  - Body Measurement
  - Made to Measure
  - Sizing
  properties:
  - type: Documentation
    url: https://saia.3dlook.me/docs/
  - type: APIReference
    url: https://saia.3dlook.me/docs/perfectfit.html
- name: FitXpress API
  description: REST API for body composition and health/fitness insights - body measurements, weight prediction, BMI/body-fat/BMR,
    3D model prediction, and 2D/3D body-progress comparison, with subscription usage tracking.
  humanURL: https://docs.fitxpress.3dlook.me/
  baseURL: https://backend.fitxpress.3dlook.me/api/1.0/
  tags:
  - Health and Fitness
  - Body Composition
  - Body Measurement
  properties:
  - type: Documentation
    url: https://docs.fitxpress.3dlook.me/
  - type: APIReference
    url: https://docs.fitxpress.3dlook.me/
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://3dlook.ai/
- type: DeveloperPortal
  url: https://saia.3dlook.me/docs/
- type: Documentation
  url: https://saia.3dlook.me/docs/
- type: APIReference
  url: https://saia.3dlook.me/docs/perfectfit.html
- type: Pricing
  url: https://3dlook.ai/pricing/
- type: PrivacyPolicy
  url: https://3dlook.ai/privacy-policy/
- type: TermsOfService
  url: https://3dlook.ai/terms-and-policies/
- type: Support
  url: https://3dlook.ai/contact-us/
- type: GitHubOrganization
  url: https://github.com/3dlook-me
- type: Authentication
  url: authentication/3d-look-authentication.yml
- type: Packages
  url: packages/3d-look-packages.yml
- type: SDKs
  url: packages/3d-look-packages.yml
- type: Components
  url: components/3d-look-components.yml
- type: ErrorCatalog
  url: errors/3d-look-error-codes.yml
- type: Conventions
  url: conventions/3d-look-conventions.yml
- type: X-MCPServerCandidate
  url: mcp/3d-look-mcp.yml
  note: 'Renamed from MCPServer 2026-09-03 (roadmap#247): the manifest self-describes as status: candidate — a tool list derived
    from the published API contracts, not an existing server. The scorer already read the manifest and reported mcp_server
    correctly; the MCPServer type was crediting the artifact-type surfaces with a server that does not exist.'
- type: Lifecycle
  url: lifecycle/3d-look-lifecycle.yml
- type: DomainSecurity
  url: security/3d-look-domain-security.yml
- type: LLMsTxt
  url: llms/3d-look-llms.txt
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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