How ProdRescue works
Deploy → incident → fix: four-layer report pipeline, Suggest Fix → GitHub PR, Honest Score, and a live timeline demo — technical detail, not marketing fluff.
Incident logs produce evidence-backed RCA. Deploy analysis runs in parallel (Enterprise: manual + automatic) and never replaces log citations.
Automatic deploy analysis (Enterprise): add a GitHub webhook from Account— each qualifying event runs "what changed" + risk (separate from log-backed RCA). Manual context: on New incident, open Incident Parameters to paste git log / PRs, or use Import last 20 commits / AI deploy risk scan. Evidence refs [1][6] map to incident logs only.
Jump to: 4-layer engine · Fix · PR · Honest Score · timeline demo.
The 4-Layer Incident Analysis Engine
For incident logs: Denoise → RCA → Evidence mapping → Assembly. Enterprise includes deploy analysis and webhook automation — parallel to logs, never mixed with log evidence. Model versions may vary over time.
Denoising
Cleanup
Clean noisy logs.
RCA
Analysis
Find the root cause. Timeline. Impact. Logs only.
Evidence Mapping
Evidence
Every claim linked to real logs. [1], [6], [8] → source.
Assembly
Assembly
Executive-ready report. Timeline. RCA. Action items.
What you get (incident path)
- Executive report (optional PDF, one click)
- Timeline with evidence links [1], [6], [8]
- Root Cause + Contributing Factors
- Action Items (owner / priority / deadline)
- Enterprise: parallel deploy analysis on webhook (what changed + risk) — see Account
Fix · PR Flow
After RCA: evidence → code search → AI-suggested fix → branch · PR. (Deploy webhooks feed change context upstream — not the same as log citations.)
Evidence extraction
From report
[1], [6], [8] and code blocks from the report.
Code search
Repo
Search repo by evidence. Fetch top file contents.
Suggest fix
AI
Report + evidence + code → explanation, risk, diff · file.
Branch · PR
On your approval
You review. We create branch, commit, open PR. No auto-merge.
What you get
- Explainable fix (why + risk)
- Unified diff + full file content for commit
- Optional: create GitHub Issue instead of PR
- Branch protection · approvals unchanged
Engineers stay in control.
Transparency & evidence
Honest Score reflects evidence coverage: how many claims are matched to real log lines, not AI self-rating.
Bottom line
Honest Score reflects how well we could map claims to your logs — not model bravado. Thin logs mean a lower score and an explicit nudge to add more signal; we never fake certainty.
Generic AI
95%
Self-reported confidence
Model says confident. No way to verify. No log backing.
ProdRescue — Honest Score
60–100%
High coverage trends toward 95% · low coverage is capped
Most claims matched → higher score. Coverage drops → score is capped and "Manual review recommended" is shown.
Every claim → real log line. No hallucinations. Can't match enough? We tell you.
Real incident timeline
Click a citation in the timeline to open the matching source log — same idea as Datadog or Sentry.