Every feature your team needs

Every OwlMQ message includes priority, slaDeadline, businessSignal, aiContext, correlationId, and causationId. Your routing layer, your ops team, and your ML model all see the same rich context — no guessing, no manual tagging.

// OwlMQ message envelope — business context built in
const message: OwlMQMessage = {
  payload: { orderId: 'ord_123', amount: 4999 },
  metadata: {
    priority: 'high',
    slaDeadline: Date.now() + 5000,
    businessSignal: { customerId: 'cust_vip_42', isVip: true },
    aiContext: { revenueSegment: 'enterprise' },
    causationId: 'checkout.session.started'
  }
};

40% lower average delivery latency compared to static routing, from day one. The model improves continuously — the more messages you send, the smarter your routing gets. You define the constraints; OwlMQ optimizes within them.

// AI routing decision — inspect what the engine decided
const result = await client.publish('orders.created', message);

console.log(result.routingDecision);
// → {
//     selectedRoute: 'consumer-group-vip',
//     confidence: 0.97,
//     reason: 'VIP customer + high priority + P99 < 5ms SLA',
//     alternatesConsidered: 3
// }

Self-tunes partition counts, consumer scaling, and flow control parameters without operator intervention. Competitors react after saturation crushes your P99 — OwlMQ acts before the first message is delayed. Average queue saturation events reduced by 99% in production.

Complete audit trail from HTTP request to SQL query, across every service boundary. When an order failed, you can trace exactly which message caused it, which retry was issued, and which consumer acknowledged it. Built-in compliance for HIPAA, SOX, and FedRAMP audit requirements.

// Trace any message back through its causal chain
const trace = await client.trace('msg_abc123');

trace.causalChain.forEach(msg => {
  console.log(`${msg.causationId} → ${msg.id}`);
});
// → http.request.checkout → order.created
// → order.created → payment.initiated
// → payment.initiated → fulfillment.queued

6 anomaly types detected out of the box: queue saturation, consumer lag, message poison, throughput cliff, latency spike, and replication lag. Dry-run mode lets you validate playbooks before enabling autonomous execution. Confidence-weighted decisions: above 90% confidence = autonomous; 60–90% = escalate to operator.

// Define a self-healing playbook
await client.playbooks.create({
  name: 'Auto-scale on queue depth spike',
  conditions: [{ metric: 'queue_depth', operator: '>', threshold: 10_000, windowMinutes: 5 }],
  actions: ['SCALE_CONSUMERS', 'SEND_ALERT'],
  confidence: 0.90  // autonomous above 90%
});

No competitor offers message-to-revenue correlation. OwlMQ connects infrastructure telemetry directly to your business metrics — so your engineering team can make decisions that move the revenue needle. Reduce churn prediction time from weeks to real time.

PII scrubbing before export. ClickHouse for 1-year analytics retention with sub-second query performance. Grafana and Datadog integrations built in. Every event, every routing decision, every anomaly — recorded immutably with full causal context.

Schema-per-tenant available on Enterprise. Kafka topics owned per domain — zero cross-tenant bleed. Single OwlMQ cluster can serve hundreds of tenants with complete isolation, at a fraction of the cost of running separate brokers per customer.