Known

Known is a voice-first, swipeless dating application founded in 2025 by Celeste Amadon and Asher Allen and headquartered in San Francisco, California. Instead of profiles, feeds, queues, and swiping, Known onboards users through a voice-AI interview and then sends one curated introduction at a time, with a 24-hour acceptance window on both sides. When both people accept, Known helps pick a restaurant that fits both sets of preferences and checks calendar availability, then collects a short post-date check-in to refine future matches. The company optimizes for in-person dates rather than engagement, and has raised $9.7M from Forerunner Ventures, NFX, and other investors. The product is iOS-only in the United States today, with waitlists for Android and for Apple users outside the US. Known publishes no public developer program, API, or SDKs; it does publish a first-party llms.txt describing the product for AI agents.

Known is profiled on the APIs.io network. Tagged areas include Company, Consumer, Dating, Mobile, and Voice AI.

Known’s developer surface includes support, signup flow, legal docs, and 13 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

12.6/100 emerging ▬ flat Agent 0/100 human only Front door AI 1/6 silent saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessApproval

0 APIs

Company Consumer Dating Mobile Voice AI Artificial Intelligence Social iOS

Kin Score

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

Composite quality — 12.6/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 0.8 / 17

Access Clarity 5.8 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 1.9 / 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 AI & Biometrics 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 4.8 — up to +19.0 composite points
Access Clarity scores 34.2 — up to +13.2 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/known/apis.yml: open an issue on github.com/api-evangelist/known 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
Apple Accessory & Device Ecosystem (MFi, Matter, Apple partners)	undeclared	-	-	-
Mobile SDK Publishers (Play SDK Index, CocoaPods, ADA certification)	undeclared	-	-	-

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.

Known Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

SignUp

SignUp

Documentation 1

Reference material describing how the API behaves

Guidelines

Guidelines

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

Support

Support

HelpCenter

HelpCenter

Commercial 3

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Legal

Legal

Company 4

The organization behind the API

Website

Careers

Instagram

Twitter

Other 3

Properties that don't map to a standard resource type

Branding

Branding

Overview

Overview

TikTok

TikTok

Source (apis.yml)

aid: known
name: Known
description: Known is a voice-first, swipeless dating application founded in 2025 by Celeste Amadon and Asher Allen and headquartered
  in San Francisco, California. Instead of profiles, feeds, queues, and swiping, Known onboards users through a voice-AI interview
  and then sends one curated introduction at a time, with a 24-hour acceptance window on both sides. When both people accept,
  Known helps pick a restaurant that fits both sets of preferences and checks calendar availability, then collects a short
  post-date check-in to refine future matches. The company optimizes for in-person dates rather than engagement, and has raised
  $9.7M from Forerunner Ventures, NFX, and other investors. The product is iOS-only in the United States today, with waitlists
  for Android and for Apple users outside the US. Known publishes no public developer program, API, or SDKs; it does publish
  a first-party llms.txt describing the product for AI agents.
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: approval
  trial: false
  try_now: false
  public: false
  label: Requires approval
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://mstspgqzsxe5kj3q.public.blob.vercel-storage.com/images/og.jpg
url: https://raw.githubusercontent.com/api-evangelist/known/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- forerunner-ventures
- nfx
x-tier: profiled
x-tier-reason: consumer-app-no-public-api
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Consumer
- Dating
- Mobile
- Voice AI
- Artificial Intelligence
- Social
- iOS
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://known.com
- type: LLMsTxt
  url: llms/known-llms.txt
- type: DomainSecurity
  url: security/known-domain-security.yml
- type: Support
  url: https://known.com/support
- type: HelpCenter
  url: https://known.com/faq
- type: SignUp
  url: https://known.com/join
- type: TermsOfService
  url: https://known.com/terms
- type: PrivacyPolicy
  url: https://known.com/privacy
- type: Guidelines
  url: https://known.com/guidelines
- type: Legal
  url: https://known.com/legal
- type: Careers
  url: https://known.com/careers
- type: Branding
  url: https://known.com/brand
- type: Overview
  url: https://known.com/readme
- type: Instagram
  url: https://instagram.com/known
- type: Twitter
  url: https://x.com/dateknown
- type: TikTok
  url: https://tiktok.com/@known
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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