Your AI on-call engineer
Get ahead of incidents. ewake handles production issues, from the first page to the fix.
Trusted every day by
AI on-call for your software
ewake takes the whole on-call shift: it triages, investigates and fixes. You only get the pages that need you.
DatadogApp11:02 AM
[Triggered] [PROD] payment-api 5xx rate above 2%
payment-api (threshold 2%). @payments-oncallDatadog · Monitor 7730415
ewakeApp11:06 AM
Taking the page.
Root cause found · investigated by ewake
payment-api 5xx rate above 2%
payment-api rose from 0.3% to 6.8%, and every failure comes from acquireConnection(). Most likely cause: connection pool exhaustion after release #4805 lowered max_size from 32 to 8. The release landed 4 minutes before the first error.Built for production at scale
- 5×
- faster time to root cause
- 5 min
- from alert to root cause
“ewake proved extremely useful during production investigations, consistently pointing us to the right root cause and saving precious time, especially during on-call.”
Piyush S.
Lead Software Engineer, Brevo
Connects to your stack, maps your production
ewake plugs into your observability, alerting, code and chat, and builds a live map of your services, releases and dependencies from them, so every investigation starts from what changed.
- Datadog
- Grafana
- PagerDuty
- Notion
- Jira
- Confluence
- Linear
- Kubernetes
- GitHub
- GitLab
- Custom MCP
Slack
incident.io
Rootly
FireHydrant- Prometheus
- Opsgenie
- Elastic
- ClickHouse
Frequently asked questions
It handles the whole shift. On alerts, it clears the noise and surfaces the real issues, with the root cause posted in the alert's thread. On incidents, it works toward a scoped fix or hands off to the on-call engineer with the evidence attached. On deployments, it watches each release for a window you set and flags drift before any monitor fires.
Only when it needs you: when human judgment is required, when customers are affected or when the fix is outside what you allowed it to do. The page arrives with the investigation, the evidence, what it ruled out and the recommended action.
No. It handles first response and the legwork on every alert, so your engineers only step in for the decisions that need them. Your team keeps ownership of production and sets the limits ewake works within.
It shows its work. Every conclusion arrives with a confidence score, the evidence behind it and what it ruled out along the way. Your engineers can challenge it in the thread and get the reasoning back.
It acts inside limits you set. You decide what it can do on its own, what needs an approving human and what it should only ever suggest. It is read-only until you grant write access, per service.
Never. Your telemetry, code and incidents are never used to train or fine-tune any model, ours or a provider's. No exceptions.
Your existing stack: observability (Datadog, Grafana, Prometheus and others), alerting (PagerDuty, Opsgenie), code (GitHub, GitLab), tickets (Jira, Linear), docs (Confluence, Notion) and chat (Slack). No new tooling, no custom instrumentation and no code changes. The integrations are the same however you run ewake.
Run ewake as SaaS on EU infrastructure, or as BYOC (bring your own cloud) in your own cloud account. On SaaS, you connect your tools and pick a Slack channel. BYOC adds one infrastructure step, which we run with your platform team. After that, both setups work the same way.