Clarifeye
Clarifeye is an AI-native knowledge infrastructure platform that captures the undocumented knowledge locked in people's heads. Its AI interviewer, Clara, runs document-aware interviews with subject-matter experts, surfaces contradictions, and structures the results into versioned, reusable knowledge artifacts — briefs, playbooks, mental maps, and ontologies. That captured knowledge is exposed to AI clients such as Claude, ChatGPT, and Microsoft Copilot through a hosted Model Context Protocol (MCP) server and a REST API, so agents answer grounded in an organization's own logic with references back to source. Clarifeye targets regulated industries where undocumented processes create key-person risk, offers EU and US data localization with PII redaction at ingestion, and is SOC 2 Type II certified. Backed by EQT Ventures.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Clarifeye the way a machine reads it — 18 machine-readable artifacts across 13 APIs, 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 — Clarifeye scores 49.5/100 (developing), with a separate agent-readiness read of 69/100 (agent native). 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 Clarifeye
Each block below is one kind of artifact we hold for Clarifeye. 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.
APIs 13
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Clarifeye Agent Settings API
Manage AI agent configurations
Clarifeye Conversations API
Create and interact with AI-powered conversations
Clarifeye Documents API
Manage documents within a project
Clarifeye Extraction Flows API
Manage extraction flows (auto-sync DAGs) — list, run, inspect statistics, update, and publish
Clarifeye Feedback API
Submit feedback on conversation messages
Clarifeye Interviews API
Assign and review structured interview conversations
Clarifeye Invitations API
Manage project invitations
Clarifeye Notifications API
Manage project-scoped notifications for users
Clarifeye Pipeline Runs API
Inspect pipeline runs queued by extraction flows or other pipeline triggers — list runs and fetch the details/status of a single run
Clarifeye Signals API
Submit signals about the project's content for domain experts to review
Clarifeye Tables API
Perform CRUD operations on warehouse tables
Clarifeye Tools API
Execute configured AI tools with custom parameters
Clarifeye Users API
Manage users within a project
Scroll within the panel for all 13 ·
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.
clarifeye-mcp.yml
MCP SERVERSecurity Posture 2
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.
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.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Clarifeye — 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 5
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 5
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Access & Security 4
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
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