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Artificial Labs website screenshot

Artificial Labs

Artificial Labs is a London-based insurance technology company building algorithmic and digital underwriting software for the specialty and reinsurance market, with the Lloyd's of London subscription market as its home ground. Founded and headquartered in the City of London at 1-3 Frederick's Place, and led by co-founders and co-CEOs David King and Johnny Bridges under chairman Martin Reith, the company sells three products to carriers, syndicates, MGAs and wholesale brokers: Smart Underwriting (a configurable lead-and-follow digital underwriting platform with appetite modelling and accept/refer/decline submission triage), Smart Placement (a broker-side placement and distribution platform) and Contract Builder (MRCv3-compliant structured digital contract creation, which powers PPL's integrated Digital Contract Capability). Named customers and partners include Apollo, PPL, BMS Group, Lockton and McGill and Partners, and the firm is an alumnus of the Lloyd's Lab accelerator. Its API posture is partner-gated and matches the London Market pattern: Artificial talks publicly and often about APIs as the connective tissue between broker PAS, carrier systems and its own platform, and describes real quote-and-bind flows in which risk data is pushed to the Artificial platform via API and a written line and rate are returned via API, but it publishes no public self-serve developer portal, no downloadable OpenAPI or Swagger definition, no public Postman collection and no public API host. api.artificial.io and developer.artificial.io do not resolve in DNS. The product documentation site at docs.artificial.io returns HTTP 200 on its landing page only, which states plainly "To view the documentation, you must sign in"; every product and reference path 302-redirects to an Auth0 authorization-code login at auth.artificialos.com, and docs robots.txt is Disallow all. Its GitHub organization, github.com/artificial-labs, exists but has zero public repositories. Where Artificial is genuinely and demonstrably standards-forward is ACORD: it joined the ACORD Solutions Group Licensed Integrator Partner program in October 2023, builds Contract Builder and Smart Placement on a structured data model incorporating MRCv3, ACORD GRLC and the Lloyd's Core Data Record, validates contracts pre-submission against MRCv3 and ACORD standards, and consumes the ACORD Transcriber API for automated data extraction. That is the honest shape of this record: real, working, standards-aligned insurance APIs that are invisible from outside the contractual relationship.

human only

Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.

Kin Score

API Evangelist profiles Artificial Labs the way a machine reads it — 5 machine-readable artifacts, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.

Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Artificial Labs scores 35.4/100 (thin), with a separate agent-readiness read of 14/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.

Kin Score

This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 35.4/100 · thin
Contract Quality 0.0 / 25
Developer Ergonomics 5.2 / 20
Commercial Clarity 10.0 / 20
Operational Transparency 2.1 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Regulatory · Securities & Market Data 15.0 / 15
Agent readiness — 14/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 10 / 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 4 / 4
Consent & Bot Identity 0 / 3

How we profile Artificial Labs

Each block below is one kind of artifact we hold for Artificial Labs. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.

Security Posture 4

Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.

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

Artificial Labs Authentication

oauth2/openIdConnect · 1 scheme

SECURITY

Artificial Labs Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Artificial Labs Vulnerability Disclosure

Hackerone · contact published

SECURITY

Artificial Labs Trust Center

ISO 27001, Cyber Essentials Plus

SECURITY

Scopes 1

OAuth scopes are the vocabulary of least-privilege access. Profiling them shows exactly what an integration — or an agent acting on a user's behalf — is allowed to do.

OAuth scopes governing access to this provider's APIs.

Artificial Labs Scopes

14 scopes · authorizationCode/clientCredentials

14 scopes

SCOPES

Resources

Every other property we hold for Artificial Labs — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.

Get Started 1

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

Build 2

SDKs, sample code, and the tooling you integrate with

Operate 2

Status, limits, changes, and where to get help

Commercial 2

Pricing, plans, and the legal terms of use

Other 2

Properties that don't map to a standard resource type

← All providers · Data indexed from github.com/api-evangelist/artificial-labs · machine-readable index on apis.io