Contextual AI
Contextual AI is an enterprise RAG platform built around a Grounded Language Model (GLM) engineered to minimize hallucinations. Its REST API exposes end-to-end RAG agents (create, configure, query) plus standalone component APIs - Generate, Rerank, Parse, and LMUnit - over datastores of ingested documents, all authenticated with a Bearer API key.
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 Contextual AI the way a machine reads it — 20 machine-readable artifacts across 9 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 — Contextual AI scores 49.3/100 (developing), with a separate agent-readiness read of 54/100 (agent ready). 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 Contextual AI
Each block below is one kind of artifact we hold for Contextual AI. 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 9
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.
Contextual AI Agents API
Create, configure, and manage RAG agents.
Contextual AI Agents Query API
Query agents for grounded responses, retrievals, metrics, and feedback.
Contextual AI Datastores API
Create and manage datastores that hold ingested knowledge.
Contextual AI Documents API
Ingest and manage documents within a datastore.
Contextual AI Generate API
Grounded generation with the Grounded Language Model (GLM).
Contextual AI LMUnit API
Evaluate model responses with natural-language unit tests.
Contextual AI Parse API
Parse documents into structured, AI-ready markdown.
Contextual AI Rerank API
Instruction-following reranking of retrieved passages.
Contextual AI Users API
Manage workspace users.
Scroll within the panel for all 9 ·
Open Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Contextual AI Platform API
OPEN COLLECTIONPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Contextual Ai Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Contextual Ai Finops
FINOPSEvent Specifications 1
Not every API is request/response. AsyncAPI describes the event-driven and streaming side — the webhooks and channels — so the asynchronous half of the interface is documented the same way the synchronous half is.
AsyncAPI definitions for this provider's event-driven and streaming APIs.
Contextual AI Agent Query Streaming (HTTP + SSE)
AsyncAPI 2.6 description of Contextual AI's **agent query streaming** surface. Contextual AI does not publish a WebSocket API. The only asynchronous / event-style transport docu...
ASYNCAPISpectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Contextual AI API Rules
SPECTRALSecurity 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.
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 Contextual AI — 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.
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 4
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/contextual-ai · machine-readable index on apis.io