DeepHow

DeepHow is an enterprise Physical AI platform that helps industrial companies capture, verify, and optimize operational knowledge across manufacturing, pharmaceutical, and electronics assembly environments. It turns expert know-how into searchable digital workflows, uses vision AI for live standard-operating-procedure (SOP) verification and on-demand visual inspection, and applies time-and-motion AI for task analysis and operator training and enablement. DeepHow is used by 100+ enterprises including Stanley Black & Decker, Anheuser-Busch InBev, Foxconn, and GSK. It is backed by Sierra Ventures and Techstars.

DeepHow is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Manufacturing, Knowledge Management, and Video.

DeepHow’s developer surface includes support, engineering blog, and 11 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

20.1/100 emerging ▬ flat Agent 0/100 human only Front door AI 5/6 prominent Heartbeat 0/100 dormant saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs

Company Artificial Intelligence Manufacturing Knowledge Management Video Training Computer Vision IndustrialSOP Verification

Kin Score

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

Composite quality — 20.1/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 0.4 / 17

Access Clarity 6.0 / 17

Operational Transparency 3.2 / 11

Contract Governance 1.9 / 10

Discoverability 4.7 / 9

Regulatory Posture 3.3 / 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 2.4 — up to +19.5 composite points
Access Clarity scores 35.5 — up to +12.9 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/deephow/apis.yml: open an issue on github.com/api-evangelist/deephow 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.

Deephow Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

Login

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conformance

Conformance

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 3

Authentication, authorization, and security posture

Security

Security

Compliance

Compliance

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

Support

Support

StatusPage

StatusPage

Commercial 2

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

TermsOfService

TermsOfService

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: deephow
name: DeepHow
description: DeepHow is an enterprise Physical AI platform that helps industrial companies capture, verify, and optimize operational
  knowledge across manufacturing, pharmaceutical, and electronics assembly environments. It turns expert know-how into searchable
  digital workflows, uses vision AI for live standard-operating-procedure (SOP) verification and on-demand visual inspection,
  and applies time-and-motion AI for task analysis and operator training and enablement. DeepHow is used by 100+ enterprises
  including Stanley Black & Decker, Anheuser-Busch InBev, Foxconn, and GSK. It is backed by Sierra Ventures and Techstars.
url: https://raw.githubusercontent.com/api-evangelist/deephow/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- sierra-ventures
- techstars
x-tier: stub
x-tier-reason: portfolio-lead
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:
  - security
  generated: '2026-11-06'
  method: derived
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
image: https://storage.googleapis.com/deephow-public/images/deephow-logo.png
tags:
- Company
- Artificial Intelligence
- Manufacturing
- Knowledge Management
- Video
- Training
- Computer Vision
- Industrial
- SOP Verification
tags_raw:
- Company
- Ai
- Manufacturing
- Knowledge Management
- Video
- Training
- Computer Vision
- Industrial
- SOP Verification
- Knowledge-Management
- Computer-Vision
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://deephow.com/
- type: Login
  url: https://www.deephow.ai
- type: Support
  url: https://support.deephow.com
- type: Blog
  url: https://deephow.com/blog
- type: GitHubOrganization
  url: https://github.com/deephow
- type: StatusPage
  url: https://status.deephow.com
- type: Security
  url: https://deephow.com/security-policy
- type: Compliance
  url: https://deephow.com/security-policy
- type: PrivacyPolicy
  url: https://deephow.com/deephow-privacy-policy
- type: TermsOfService
  url: https://deephow.com/terms-conditions
- type: Conformance
  url: conformance/deephow-conformance.yml
- type: DomainSecurity
  url: security/deephow-domain-security.yml
- type: LLMsTxt
  url: llms/deephow-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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