Your AI needs your data. Not your database.

// 1. Your backend starts the workflow, then sends the channel to the browser
const headers = { "Content-Type": "application/json",
  Authorization: `Bearer ${process.env.MODELRIVER_API_KEY}` };
const job = await fetch("https://api.modelriver.com/v1/ai/async", {
  method: "POST", headers,
  body: JSON.stringify({ workflow: "support_reply", messages }),
}).then((r) => r.json()); // channel_id, ws_token, websocket_url, ...

// 2. ModelRiver calls your webhook. Verify x-modelriver-signature first.
app.post("/modelriver/webhook", async (req, res) => {
  res.sendStatus(200); // acknowledge first, then do the work
  const { ai_response, callback_url } = req.body.data;
  const order = await db.orders.find(ai_response.data.order_id);
  await fetch(callback_url, { method: "POST", headers,
    body: JSON.stringify({ data: { status: order.status, eta: order.eta } }) });
});

// 3. Your frontend gets live updates (@modelriver/client/react)
const { connect, steps, response } = useModelRiver();
connect({ channelId: job.channel_id, wsToken: job.ws_token,
  websocketUrl: job.websocket_url, websocketChannel: job.websocket_channel });