Ambient.ai website screenshot

Ambient.ai

Ambient.ai (legal entity Ambient AI, Inc.) is an AI-powered enterprise physical security platform founded in 2016 by Stanford AI researchers Shikhar Shrestha and Vikesh Khanna, and headquartered in Redwood City, California. The company created the category of "Agentic Physical Security," where purpose-built computer vision and a Vision-Language Model (Ambient Pulsar) autonomously observe, detect, assess, and respond to real-world threats in real time. Ambient.ai transforms an organization's existing cameras, sensors, and access control systems into a unified intelligence layer through products including Ambient Foundation (real-time situational awareness), Ambient Advanced Forensics (investigation acceleration), Ambient Access Intelligence (PACS correlation to eliminate false alarms), and Ambient Threat Detection (150+ validated threat signatures). It integrates with 200+ ONVIF cameras and VMS/PACS platforms such as Axis, Hanwha, Avigilon, Genetec, Milestone, LenelS2, and Honeywell. The platform is SOC 2 Type II certified and GDPR/CCPA aligned, uses no facial recognition, and stores no PII. Ambient.ai is backed by a16z and Allegion.

Ambient.ai is profiled on the APIs.io network. Tagged areas include Company, Physical Security, Computer-Vision, Artificial Intelligence, and Video Analytics.

Ambient.ai’s developer surface includes documentation, engineering blog, support, signup flow, and 9 more developer resources.

🌐 Visit website 📡 Source on GitHub
19.4/100 emerging ▬ flat Agent 0/100 human only saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0
0 APIs
CompanyPhysical SecurityComputer-VisionArtificial IntelligenceVideo AnalyticsAccess ControlThreat DetectionEnterprise SecurityAgentic AI

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0
Composite quality — 19.4/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 3.3 / 20
Access Clarity 10.0 / 20
Operational Transparency 0.3 / 13
Contract Governance 0.0 / 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
Delegated User Identity 0 / 6
Protected Resource Metadata 0 / 5
Registration Without a Human 0 / 6
Agentic Commerce Well-Known Document 0 / 5
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.
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/ambientai: 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 →

Security Posture 2

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

Ambientai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Ambientai Trust Center

SOC 2 Type II

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

SignUp
SignUp

Documentation 1

Reference material describing how the API behaves

Documentation
Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt
LLMsTxt

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization
GitHubOrganization

Access & Security 3

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity
 Compliance
Compliance
 TrustCenter
TrustCenter

Operate 1

Status, limits, changes, and where to get help

Support
Support

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService
TermsOfService
 PrivacyPolicy
PrivacyPolicy

Company 2

The organization behind the API

Website
Website
 Blog
Blog

Other 1

Properties that don't map to a standard resource type

PlatformOverview
PlatformOverview

Source (apis.yml)

apis.yml Raw ↑ 
aid: ambientai
name: Ambient.ai
description: Ambient.ai (legal entity Ambient AI, Inc.) is an AI-powered enterprise physical security platform founded in
  2016 by Stanford AI researchers Shikhar Shrestha and Vikesh Khanna, and headquartered in Redwood City, California. The company
  created the category of "Agentic Physical Security," where purpose-built computer vision and a Vision-Language Model (Ambient
  Pulsar) autonomously observe, detect, assess, and respond to real-world threats in real time. Ambient.ai transforms an organization's
  existing cameras, sensors, and access control systems into a unified intelligence layer through products including Ambient
  Foundation (real-time situational awareness), Ambient Advanced Forensics (investigation acceleration), Ambient Access Intelligence
  (PACS correlation to eliminate false alarms), and Ambient Threat Detection (150+ validated threat signatures). It integrates
  with 200+ ONVIF cameras and VMS/PACS platforms such as Axis, Hanwha, Avigilon, Genetec, Milestone, LenelS2, and Honeywell.
  The platform is SOC 2 Type II certified and GDPR/CCPA aligned, uses no facial recognition, and stores no PII. Ambient.ai
  is backed by a16z and Allegion.
url: https://raw.githubusercontent.com/api-evangelist/ambientai/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: []
  generated: '2026-07-22'
  method: derived
image: https://cdn.prod.website-files.com/696e308b948dd4131d5959ff/699320646f38a418d7e3313c_ambient-OG-image.png
x-type: company
x-source: vc-portfolio
x-backed-by:
- a16z
- Allegion
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-17'
tags:
- Company
- Physical Security
- Computer-Vision
- Artificial Intelligence
- Video Analytics
- Access Control
- Threat Detection
- Enterprise Security
- Agentic AI
tags_raw:
- Company
- Physical Security
- Computer Vision
- Artificial Intelligence
- Video Analytics
- Access Control
- Threat Detection
- Enterprise Security
- Agentic AI
apis: []
common:
- type: DomainSecurity
  url: security/ambientai-domain-security.yml
- type: Website
  url: https://www.ambient.ai
- type: PlatformOverview
  url: https://www.ambient.ai/platform-overview
- type: Documentation
  url: https://www.ambient.ai/ai-info
- type: Integrations
  url: https://www.ambient.ai/integration
- type: Blog
  url: https://www.ambient.ai/blog
- type: GitHubOrganization
  url: https://github.com/ambientai
- type: Support
  url: https://www.ambient.ai/request-demo
- type: SignUp
  url: https://www.ambient.ai/request-demo
- type: TermsOfService
  url: https://www.ambient.ai/terms-of-service
- type: PrivacyPolicy
  url: https://www.ambient.ai/privacy-policy
- type: Compliance
  url: https://www.ambient.ai/press/soc2-type-ii-certification
- type: TrustCenter
  url: security/ambientai-trust-center.yml
- type: LLMsTxt
  url: llms/ambientai-llms.txt
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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