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 📡 Source on GitHub

19.3/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-08-20 · rubric v0.12.0

0 APIs

CompanyArtificial IntelligenceManufacturingKnowledge-ManagementVideoTrainingComputer-VisionIndustrialSOP Verification

Kin Score

Kin Score How this is scored →
scored 2026-08-20 · rubric v0.12.0

Composite quality — 19.3/100 · emerging

Contract Quality 0.0 / 25

Developer Ergonomics 0.5 / 20

Access Clarity 7.1 / 20

Operational Transparency 3.8 / 13

Contract Governance 2.2 / 12

Discoverability 5.7 / 10

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

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/deephow: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

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
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  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
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