Stop investigating. Start resolving.Start resolving.
When something breaks, Codehawk surfaces the root cause from your event history, keeps stakeholders informed automatically, and lets your engineers fix instead of investigate. No agents to install, no logs to ingest, no per-byte billing.
Correlation at read time, not write time
Most tools require you to define schemas, configure pipelines, and pre-categorise data before it's useful. Codehawk takes a different approach.
You send us lightweight events: a deployment finished, a threshold was crossed, a certificate is expiring. Four fields (title, source, severity, body) with sensible limits, no schema negotiation.
When an incident happens, Codehawk looks at everything across your sources and finds the connections. Nothing is pre-indexed or pre-categorised. The context is assembled when you need it, not when you guessed you might.
Example: errors are spiking
Codehawk correlates with 3 other signals:
A config change 35 minutes ago reduced the connection pool size. Latency started climbing 13 minutes later as connections became scarce. The pool saturated, then errors spiked. Codehawk flags the config change as the root cause.
You didn't configure this correlation in advance. Codehawk found it because it looks at everything at the moment you need it, including pipelines you never thought to configure before the incident.
What it looks like when something breaks
A Slack message: “Is something up with the app?” Here's what happens next.
You see it in the event stream. You start an investigation.
14:41 — error.rate.critical in checkout-service
Your dashboard shows live events as they arrive. Something's wrong with checkout. You click “Start investigation” and Codehawk gets to work.
Codehawk assembles the timeline. The picture is clear.
14:18 — Database team updated address lookup from v2.3 to v2.4
14:24 — Errors start in AU region
14:32 — Jason deployed to checkout-service
14:41 — Error rate crosses critical threshold
No digging. No switching between six dashboards. The timeline is assembled from events across all your sources, correlated at the moment you need it. The errors started before Jason's deploy. The DB update landed 6 minutes before that.
Codehawk tells you who's affected, and how.
Customer impact detected
Customers with an Australian shipping address are unable to complete purchases. Of 145 attempted purchases from 30 unique customers, 108 failed with no address found. 37 succeeded (non-AU addresses).
This is what your VP is going to ask about. Codehawk has the answer before they ask.
It remembers how your team fixed this before.
From your incident history
Last time errors spiked like this, Fatima fixed it by rolling the pods. Have you tried that?
Is it safe to roll back Jason's change? Last rollback of this service took < 1 minute.
Is it safe to roll back the DB change? Last time that took ~9 hours . Probably not your first move.
The kind of questions a great incident commander asks, informed by your team's actual history. Instead of three people chasing three theories, you're working the most likely path first.
After you fix it, the follow-up is already drafted.
Draft incident report
Between 14:18 and 15:03, all customers with an Australian address (51% of total customers) were unable to complete purchasing. Of 145 attempted purchases from 30 unique customers, 108 failed with no address found.
Recommended next steps
- Contact 30 affected customers and offer 5% off their next purchase
- Add integration test for AU address lookup before next DB migration
The hawk watches and learns. Next time this pattern appears, the resolution is faster. But it still requires a trained human in the loop. Think of it as your incident commander, always learning, never on autopilot.
What Codehawk does for you
An AI incident commander that works alongside your team, with deterministic automation for failures you've already solved.
Codehawk Incidents
AI that asks the right questions during an incident, surfaces patterns from your history, and writes the stakeholder updates so your engineers don't have to.
- One HTTP POST, four fields, no agents, no per-byte billing
- Correlation at read time, not write time
- Institutional knowledge that doesn't walk out the door
Codehawk Self-Healing
Coming soonFor failures you've already solved once: deterministic, in-process remediation rules that run automatically. No AI, no magic. Just vetted automation you control.
- Known failures fix themselves before anyone gets paged
- Deterministic rules you vet and test. No AI surprises
- Example: DB reads fail, replica takes over automatically
- Override or remove any rule in seconds
Connects to your stack
Send events from wherever things happen. A single HTTP POST with four fields, from your CI pipeline, your infrastructure, or a cron job on your laptop.
curl -X POST https://api.codehawk.org/events \
-H "Authorization: Bearer YOUR_API_TOKEN" \
-d '{"title":"deploy.completed","source":"github-actions","severity":"info","body":"checkout-service v2.4.1"}'Sign up and verify your email to get an API token. You'll be sending events in under a minute.
Kubernetes
AWS
GitHub Actions
Prometheus
HTTP API
GitOps
Built by engineers who've carried the pager
We ran detection and monitoring systems at Amazon, Microsoft, and Cloudflare. We've been woken up at 3am and held to the highest standards of reliability. So we built the tool we wished we had.
The Hawk watches the Hawk
We run Codehawk on Codehawk. Our architecture means an issue in one region never affects another.
See it for yourself
Free to sign up. Send your first event in under a minute. No credit card, no sales call, no 30-day eval that expires before you get to it.