Incandor

Incandor is a San Francisco based Y Combinator (Spring 2026) startup building behavioral intelligence infrastructure for fraud and abuse detection. Rather than relying on identity verification at account creation, Incandor constructs a behavioral map of every user on a platform from how they physically interact with an app or website — mouse dynamics, keystroke timing, scroll patterns, and on mobile how a device is held. It requires no fraud labels and no enrollment period, working from the first session. At the individual level it identifies account takeovers with greater than 99 percent accuracy; at the population level it separates out coordinated fraud rings, mule operators, shared operators, bots and agents, and coerced or duress sessions. Incandor is delivered as a programmable API and a client-side telemetry SDK rather than a black-box risk score, letting fraud teams combine behavioral signals with their own transaction history, customer records, and business rules. It collects only behavioral signals — not what users type, read, or view — keeping user data private by design. The company was founded by Matthew Yekell (CEO) and Luc Rosenzweig (CTO), both Stanford CS graduates. This profile is maintained by the API Evangelist network; the developer platform is currently early access.

Incandor is profiled on the APIs.io network. Tagged areas include Company, Fraud Prevention, Behavioral Biometrics, Account Takeover, and Security.

Incandor’s developer surface includes support and 3 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

4.6/100 minimal ▬ flat Agent 11/100 agent aware Front door AI 3/6 light Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs 1 MCP Servers

Company Fraud Prevention Behavioral Biometrics Account Takeover Security Fintech Machine Learning

Kin Score

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

Composite quality — 4.6/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.8 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.4 / 9

Regulatory Posture 0.8 / 15

Agent readiness — 11/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 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
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
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0

12 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
Access Clarity scores 0.0 — up to +20.0 composite points
Developer Ergonomics scores 4.8 — up to +19.0 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/incandor/apis.yml: open an issue on github.com/api-evangelist/incandor 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
Biometrics Testing (biometric recognition vendors, identity verification depth)	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.

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (verified)

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Incandor MCP Server

Remote MCP server at mcp.incandor.com over HTTP; 3 tools listed.

MCP SERVER

Security Posture 1

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

Incandor Domain Security

TLSv1.3 · DMARC

SECURITY

Resources

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: incandor
name: Incandor
description: Incandor is a San Francisco based Y Combinator (Spring 2026) startup building behavioral intelligence infrastructure
  for fraud and abuse detection. Rather than relying on identity verification at account creation, Incandor constructs a behavioral
  map of every user on a platform from how they physically interact with an app or website — mouse dynamics, keystroke timing,
  scroll patterns, and on mobile how a device is held. It requires no fraud labels and no enrollment period, working from
  the first session. At the individual level it identifies account takeovers with greater than 99 percent accuracy; at the
  population level it separates out coordinated fraud rings, mule operators, shared operators, bots and agents, and coerced
  or duress sessions. Incandor is delivered as a programmable API and a client-side telemetry SDK rather than a black-box
  risk score, letting fraud teams combine behavioral signals with their own transaction history, customer records, and business
  rules. It collects only behavioral signals — not what users type, read, or view — keeping user data private by design. The
  company was founded by Matthew Yekell (CEO) and Luc Rosenzweig (CTO), both Stanford CS graduates. This profile is maintained
  by the API Evangelist network; the developer platform is currently early access.
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  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
image: https://www.incandor.com/logo.png
url: https://raw.githubusercontent.com/api-evangelist/incandor/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
x-founders:
- FN: Matthew Yekell
  role: CEO
- FN: Luc Rosenzweig
  role: CTO
x-yc-batch: Spring 2026
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Fraud Prevention
- Behavioral Biometrics
- Account Takeover
- Security
- Fintech
- Machine Learning
tags_raw:
- Company
- Fraud Prevention
- Anti-Fraud
- Behavioral Biometrics
- Account Takeover
- Security
- Fintech
- Machine Learning
- Machine-Learning
apis: []
common:
- type: DomainSecurity
  url: security/incandor-domain-security.yml
- type: Website
  url: https://www.incandor.com
- type: GitHubOrganization
  url: https://github.com/incandor
- type: Support
  url: mailto:info@incandor.com
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

Incandor API — frequently asked questions

Does Incandor have an MCP server?

Yes. APIs.io lists 1 MCP server for Incandor: Remote MCP server at mcp.incandor.com over HTTP; 3 tools listed.

Is the Incandor API ready for AI agents?

Incandor has a Kin Score of 4.6 out of 100 (minimal) and an Agent Readiness score of 11.4 (agent-aware), scored by API Evangelist on 2026-11-06.

Work with this as data

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MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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