Gangkhar
Gangkhar is an AI-native embedded protection infrastructure platform that lets digital platforms, insurers, MGAs, and brokers configure, launch, and continuously optimize embedded insurance products globally through a single infrastructure layer and API. Its Sherpa+ engine provides full-stack embedded protection rails covering onboarding, pricing, underwriting, and claims across multiple carriers and markets, while Sherpa+Lens applies real-time AI optimization to pricing, messaging, and product versions. The platform enables sectors like mobility, delivery, fintech, e-commerce, travel, health, and vertical SaaS to embed compliant protection directly into their customer experiences without becoming insurers themselves. Founded in 2025 and led by former Chubb executive Federico Spagnoli, Gangkhar closed a $4.25M seed round in March 2026 led by Anthemis. Surfaced as an Anthemis portfolio company and enriched into the API Evangelist network.
Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.
API Evangelist profiles Gangkhar the way a machine reads it — 2 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 — Gangkhar scores 13.4/100 (minimal), with a separate agent-readiness read of 12/100 (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
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Gangkhar
Each block below is one kind of artifact we hold for Gangkhar. 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.
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
gangkhar-mcp.yml
MCP SERVERSecurity Posture 1
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.
Resources
Every other property we hold for Gangkhar — 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.
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Access & Security 1
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 2
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 4
The organization behind the API
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