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, Ai, Manufacturing, Knowledge Management, and Video.

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

🌐 Visit website 📡 Source on GitHub
20.6/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-23 · rubric v0.5
0 APIs
CompanyAiManufacturingKnowledge ManagementVideoTrainingComputer VisionIndustrialSOP Verification

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-23 · rubric v0.5
Composite quality — 20.6/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 1.3 / 20
Commercial Clarity 8.4 / 20
Operational Transparency 4.1 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
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
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 →

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
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)

apis.yml Raw ↑ 
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: '0.20'
created: '2026-07-17'
modified: '2026-07-18'
image: https://storage.googleapis.com/deephow-public/images/deephow-logo.png
tags:
- 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