What changed, the moment you're paged.

Mean Time To Resolve - the average time from when an incident is detected to when it's fully resolved. It's the headline reliability metric most on-call teams track, because it maps directly to how long your users feel the pain. Resolution breaks into three stretches: reconstruct what changed, decide what to do, ship the fix. Pagescout attacks the first one - usually the slowest and most variable - so the whole number comes down.

That's the whole point. For most teams the biggest, most variable chunk of resolution time isn't diagnosing or fixing - it's reconstructing what changed, by hand, across six tools, under pressure. Pagescout does that reconstruction the instant the alert fires, so you start from a complete timeline instead of a blank tab. The gathering phase stops dominating your clock.

No - deliberately. Pagescout gathers and orders the changes that touched the affected service before the alert. It never tells you which one is the cause. The judgment is the part you're good at; the gathering is the part it's good at.

Those tools produce conclusions you then have to verify - which, during an incident, is just more work you don't trust. A confident wrong answer at 3am is worse than no answer: it can hallucinate a cause or miss the context that never lives in the code - the customer escalation, the vendor outage, the "oh, we knew that box was flaky" - and point you down the wrong path. Pagescout produces evidence, not opinions: the timeline is assembled deterministically from your tools' own change data, scoped to the affected service and ordered by rule, with every line linked to the raw commit, run, diff or flag log. You hold the full, ordered picture and your own judgement - still the fastest, safest way to the real cause.

Different job entirely. Coding agents live in your editor and help you write and change code - they don't sit on your incident tooling, don't fire when PagerDuty does, and can't tell you what deployed, which flag flipped or what config changed across GitHub, Grafana, AWS and the rest in the seconds after a page. Pagescout is on-call infrastructure: no prompting, no gathering by you - the moment you're paged it has the cross-tool timeline waiting in Slack. They're complementary: use Pagescout to find the change that caused the incident, then use your coding agent to ship the fix.

GitHub, Grafana, AWS, LaunchDarkly, PagerDuty, Slack and Kubernetes today, with webhooks for anything custom. All read-only - Pagescout pulls your change feed and never writes back to your systems.

The timeline is assembled and posted to Slack within seconds of the alert firing - typically before you've unlocked your laptop. The page fires, Pagescout reaches into your tools, pulls the recent changes, then scopes and orders them - all in one fast pass.

By default Pagescout works from change metadata and diffs. For teams who opt in, it indexes your codebase read-only so it can search for related evidence the moment you're paged - prior changes to the same files, the commits around them - and surface it alongside the timeline. Nothing is ever written back, and that search runs only on that scoped context. Self-hosted and private-deployment options are on the roadmap for teams who need them - flag it in the waitlist form.

Especially you. Pagescout is built for the team where on-call rotates through whoever shipped last. It's the institutional memory you don't have the headcount for.

Yes - the stakes are just higher. At scale, a single incident spans dozens of services and many teams' changes, and every extra minute spent reconstructing what shipped is more customer impact and a wider blast radius. Pagescout collapses that gathering step across all your tools at once, so the responder starts from a complete cross-team timeline instead of pinging six channels. Lower MTTR, faster mitigation, less time your customers feel the pain.