A secure backend you don't have to build. REST, webhooks, queues, search, signed file URLs, AI, and a built-in MCP server — already there.
All the boring (and risky) parts of running a real app — already in. Use them; don't rebuild them.
AWS-backed runtime. Tier-based scaling. Zero-config deploys.
Durable job queues. Backoff. Dead-letter visibility.
Native files with transforms and streaming.
Time-bounded, tamper-proof access to private files.
Fast, indexed search across your application data.
Configurable activity log for compliance.
Mobile push and SSE for terminal subscriptions.
Cron-like and one-shot event scheduling.
Per-key, per-route limits. Out of the box.
Outbound events with signing, retries, and delivery logs.
Talk to your data from Claude, Cursor, or any MCP environment.
AI agents wired to platform tools.
Query records and trigger actions from Claude, Cursor, or any AI agent — through a built-in MCP server, governed by the same permissions as everything else.
Where engineering judgment matters — model design, workflows, integrations — you stay in control.
Object types, properties, constraints, calculations. Yours to design.
Workflows, triggers, custom actions. Code or no-code.
Page composition with blocks or custom HTML.
External service glue, webhooks, third-party APIs.
Most teams use both. Start where your work lives.
Platform foundations: model design, views, canvases, automations, public pages, custom forms.
A built-in MCP server for talking to your data from any AI environment. REST API, webhook patterns, service-to-service connectivity, and event-driven integrations.
Predictable, rate-limited, and reflective of your model — talk to your data from the AI environment you already use. The shapes below are illustrative.
const res = await fetch(`${BASE_URL}/collection/customers`, {
method: "POST",
headers: {
"x-api-key": process.env.TOKEN,
"content-type": "application/json",
},
body: JSON.stringify({ name: "John", surname: "Doe" }),
});
await fetch(`${BASE_URL}/webhook/push-event`, {
method: "POST",
headers: {
"x-api-key": process.env.TOKEN,
"content-type": "application/json",
},
body: JSON.stringify({
event: "customer.updated",
occurred_at: new Date().toISOString(),
data: { id: "cus_8h2", active: true },
}),
});
const events = new EventSource(
"https://sse.jetstack.dev/you-app/subscribe/event",
{ headers: { "x-api-key": process.env.TOKEN } }
);
events.onmessage = (e) => {
console.log(JSON.parse(e.data));
};
Build a pilot module — objects, permissions, key views — to internalize the patterns.
Introduce automations, button actions, and event hooks around real workflow states.
Wire external services via REST and webhooks. Validate UAT. Deploy.
When clients ask "why not just AI app generation tools or self-hosted?" — these are the answers that hold up.
Prompt-first tools accelerate concept work, but production apps need deterministic behavior. On Jetstack, changes are intentional and reviewable.
Your client does not own deployment, scaling, monitoring, or audit infrastructure. That category of cost and risk simply moves off the table.
Model, workflow, and UI changes are explicit and reproducible. Scope and acceptance criteria stay grounded.
Ship quickly while preserving the consistency, governance, and ownership stability that serious business systems require.
Share your team profile and target use cases. We'll plan onboarding and cooperation.
Tell us about your project. We will reply shortly.