Platform
Stable model aliases
Integrate once with `kentwynn/reasoning` and `kentwynn/embedding` instead of binding clients to raw backend model IDs.
Kent Wynn AI Platform
Build with Kent Wynn AI—model access, token governance, and integration-ready AI endpoints.
Ship chat, completions, and embedding workflows behind a stable API surface. Kent Wynn AI gives teams branded model aliases, scoped keys, token-aware usage controls, and a cleaner path from prototype to internal production rollout.
Model surface
Keep clients pinned to `kentwynn/reasoning` and `kentwynn/embedding` while the platform owns backend routing.
Token controls
Issue scoped keys, enforce quotas, and audit usage from one Kent Wynn control plane instead of distributing raw provider credentials.
Quick reference
curl -X POST \
https://api.kentwynn.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "X-AI-Token: YOUR_TOKEN" \
-d '{
"model": "kentwynn/reasoning",
"messages": [
{ "role": "system", "content": "You are a precise platform assistant." },
{ "role": "user", "content": "Summarise the Kent Wynn AI platform for an engineering manager." }
],
"stream": false
}'Quickstart
Integrate in four controlled steps.
Kent Wynn AI should read like a product platform, not a demo catalog. This flow keeps onboarding clear for engineers, internal platforms, and automation teams.
Step 1
Create an API key
Sign in to the Kent Wynn AI Console, issue a scoped key, and store it once. Keys are only revealed at creation time.
Step 2
Choose the model alias
Use `kentwynn/reasoning` for generation workflows and `kentwynn/embedding` for retrieval, clustering, and semantic search.
Step 3
Call the compatible endpoint
Send requests to the `/v1/*` surface with `X-AI-Token`. Chat, completions, and embeddings follow an OpenAI-compatible contract.
Step 4
Govern usage centrally
Track token consumption, rotate keys, and enforce account-level controls from the console or admin endpoints.
Platform
OpenAI-compatible surface
Use standard chat, completions, and embeddings flows with minimal client changes across Python, TypeScript, and automation tools.
Platform
Token governance
Issue keys, apply quotas, and monitor token consumption from a single control plane instead of scattering secrets across systems.
Platform
Integration-ready platform
Drop Kent Wynn AI into SDKs, workflow tools, and internal services without rebuilding your application architecture.
Live Demo
Verify the public contract before you integrate.
These examples call the /demo namespace on api.kentwynn.com and show the same branded aliases and response shapes developers can expect from the platform.
GETGET /demo/v1/models
List the public demo model aliases exposed by Kent Wynn AI.
curl -X 'GET' \
'https://api.kentwynn.com/demo/v1/models' \
-H 'accept: application/json'API Surface
View full reference →Core API surface
Kent Wynn AI keeps the public contract narrow and stable. Model aliases stay branded, authentication stays token-based, and the endpoint surface remains easy to integrate from existing OpenAI-style clients.
GET/v1/models
List stable public model aliases and supported capabilities.
POST/v1/chat/completions
Run chat-style generation with the Kent Wynn reasoning model.
POST/v1/completions
Generate prompt-based completions for legacy and lightweight text flows.
POST/v1/embeddings
Produce 2560-dimension embeddings for retrieval, clustering, and semantic search.
Control Plane
Token governance, account control, and usage visibility.
Kent Wynn AI is not just a model endpoint. It is the control layer for who can call the platform, how token consumption is governed, and how integrations stay observable over time.
- • API access is gated by issued keys and enforced token quotas.
- • Key lifecycle, account state, and request limits are managed from the Kent Wynn control plane.
- • Every request path is designed to support auditability and centralized governance.
Response shape
Standard JSON responses keep integration surfaces predictable across chat, completions, and internal telemetry.
{
"choices": [
{
"message": {
"role": "assistant",
"content": "Kent Wynn AI exposes stable model aliases with token-aware access controls."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 18,
"completion_tokens": 24,
"total_tokens": 42
}
}Integrations
Drop Kent Wynn AI into production workflows.
The platform is designed to sit behind the tools your team already uses. Keep a stable API contract for engineering, route requests through governed model aliases, and preserve control over tokens and usage across every integration.
OpenAI SDKTypeScriptPythonn8nLangChainREST API
OpenAI SDK compatibility
Use the official OpenAI Node or Python SDKs with a base URL swap. Kent Wynn preserves the surface developers already know.
View docs →Workflow orchestration
Connect Kent Wynn endpoints into n8n, scheduled jobs, or internal automations without introducing a second AI integration layer.
View docs →Agent frameworks
Plug into LangChain or similar toolchains while keeping model aliases, token controls, and account-level governance centralized.
View docs →Direct REST integration
Use raw HTTP when you need full transport control for backend services, webhooks, and platform-to-platform integrations.
View docs →Integration model
Kent Wynn AI sits between your applications and the underlying model runtime. That lets you standardize authentication, preserve stable product aliases, and manage token usage without rewriting every client each time the backend changes.
- • One branded API surface for chat, completions, and embeddings
- • Centralized token lifecycle and request governance
- • Minimal client churn across SDKs, automations, and services
OpenAI SDK drop-in example
Use the official client, swap the base URL, and keep the rest of the integration straightforward.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.KENTWYNN_TOKEN!,
baseURL: "https://api.kentwynn.com/v1",
});
const completion = await client.chat.completions.create({
model: "kentwynn/reasoning",
messages: [
{
role: "system",
content: "You are a precise platform assistant."
},
{
role: "user",
content: "Draft 3 onboarding steps for a new enterprise customer."
}
],
});
console.log(completion.choices[0].message?.content);Product Ecosystem
Products built on the Kent Wynn AI platform.
Kent Wynn AI is the model and governance layer. Products on top of it turn that platform into focused workflows for knowledge, automation, and future multimodal operations.
OpenQueryLive product
Document intelligence built on Kent Wynn AI for high-trust internal knowledge workflows.
OpenQuery turns contracts, SOPs, support logs, and operational documents into a queryable system with retrieval, citations, and controlled reasoning on top of Kent Wynn model aliases.
RetrievalEmbeddingsCitationsInternal knowledge
Powered by Kent Wynn AI
Uses Kent Wynn model aliases, governed token access, and the same stable public API surface as the platform.
Retrieval-first workflow
Designed for document-heavy teams that need structured ingestion, embedded search, and defensible answers.
Why it matters
OpenQuery demonstrates the intended Kent Wynn product pattern: one controlled AI platform underneath, multiple focused products above it.
Coming soon
Future platform productsVision Workbench
A multimodal workspace for image, scan, and visual document analysis once the vision runtime is production-ready.
Agent Console
A controlled environment for multi-step task execution, tool routing, and auditable operator workflows.
Usage Hub
A unified operational dashboard for token reporting, key lifecycle, quota policy, and model adoption across products.
Build on a controlled AI platform.
Start with stable Kent Wynn model aliases, issue governed API keys, and integrate chat, completions, or embeddings without exposing your product to backend churn.