A P0 fires at 2am and the first forty minutes are archaeology: which deploy, which commit, which PR, which ticket. ArcRelease arrives with the alert already correlated to the deployments in its blast radius, ranked, and resolved to the pull request.
It correlates every PagerDuty and Datadog alert to the deployments inside a 60-minute blast-radius window and resolves the chain to commit, pull request and Jira story in roughly 10 seconds — so the bridge call opens with a named candidate instead of an open question.
A P0 fires at 2am and my first forty minutes are archaeology: which deploy, which commit, which PR, which ticket.
Four teams deployed in that window and none of them think it was theirs.
By the time we find the commit, the author has gone to bed and the context is gone.
A named deployment, its pull request and its author — ranked by blast-radius overlap — are on screen before the call fills up. Roughly 10 seconds to a candidate root cause instead of a 40-minute walk across four tools.
If nothing shipped inside the blast-radius window, ArcRelease says so in seconds: stop looking at releases and go look at infrastructure or traffic.
No. Ten seconds is time to a candidate root cause — how fast you learn what changed. MTTR is time to restore, including the fix and verification. Correlation shortens diagnosis, not the repair.
Either one is enough to start. Datadog catches the monitor that fires before anyone declares an incident; PagerDuty catches the declared page. Both together usually means catching it earlier.
Tell us about your release process. Connect your stack in ten minutes, run a Gaja preflight on a real release, and see the reasons behind the verdict.
Free plan · ten minute setup · we reply in one to two business days