For CTOs and engineering leads · Built for startup teams (5–50 people), Seed to Series B

Professional, Board-Ready Incident Reports in 2 Minutes.Even Without an SRE Team.

Stop spending 4 hours digging through logs at 3 AM. Turn chaotic Slack threads and Datadog logs into structured Executive Postmortems. Built for Startup CTOs and Lead Engineers.

14-day free trial · Try everything

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See real incident report examples

Used by engineering teams at early-stage startups from Seed to Series B.

Reads war-room threads and DMs. In-channel triggers are live: /rescue and @mention. Summaries + full report in channel.
Slack Live
Datadog shows you the signal. ProdRescue explains why the incident happened.
DatadogSoon
Pull CloudWatch Logs into reports. No manual copy-paste. Coming soon.
AWS CloudWatchSoon
Correlate incidents with PagerDuty events. Timeline from alerts. Coming soon.
PagerDutySoon
Denoise → RCA → evidence mapping → assembly. Each step tuned for one job.
ProdRescue AI
Exact root cause, timeline, evidence refs [1][6], and action items. Report in 1 click.
Executive Report
Suggest fix from report. Create branch & PR or issue. One click.
GitHub Live
Create Jira issue from report. Link to incident. Coming soon.
JiraSoon
Copy report to clipboard. Paste into Teams or email.
Teams Live
Post report link to any channel. No re-typing.
Post to Slack Live

Paste logs from Datadog, CloudWatch, Slack, JSON, or any export. Find RCA fast, then generate your report.

Slack · Live

Already using Slack for incidents?

You're already 90% done.

Most teams already run incidents in Slack. ProdRescue turns 200 chaotic messages into one clear RCA timeline with evidence. Datadog shows the problem; ProdRescue tells you why it happened.

"Slack integration's a game changer. We get the report link right in the channel. No more rebuilding the timeline from hundreds of messages."

Daniel, Senior SRE
Security-First Architecture

Your data stays yours

Logs are sanitized in-memory before any AI call. PII, IPs, tokens — redacted locally. Nothing sensitive leaves your session.

Anonymizer pipeline
Raw (never sent)
{
  "ts": "2024-02-15T23:47:12Z",
  "ip": "192.168.1.42",
  "user": "ops@acme.com",
  "auth": "Bearer sk-proj-abc123...",
  "msg": "checkout timeout"
}
Sanitized → AI
{
  "ts": "2024-02-15T23:47:12Z",
  "ip": "[IP_REDACTED]",
  "user": "[EMAIL_REDACTED]",
  "auth": "[TOKEN_REDACTED]",
  "msg": "checkout timeout"
}

Only sanitized text is sent to AI. Ephemeral processing — discarded after response.

What gets redacted

  • IP addresses (IPv4, IPv6) → [IP_REDACTED]
  • Emails → [EMAIL_REDACTED]
  • Passwords, API keys, secrets → [REDACTED]
  • Bearer tokens, JWTs → [TOKEN_REDACTED]
  • Slack @user, #channel (when applicable)

Runs before every API call. No log retention. No training on your data.

Anonymizer code is open source — view on GitHub

No training on your data
Evidence-backed claims
Secure processing

How it works

Two ways to get root cause fast. Logs or Slack — your choice.

Paste logs

Drop logs from Datadog, CloudWatch, JSON, or any export. No integration required.

Paste logs → Get report

Use Slack

Connect Slack. When an incident happens, the report is generated from your war-room thread.

Connect Slack → Incident happens → Report arrives

From Incident to Resolution

Detect → Diagnose → Fix. Minutes, not hours.

Step 1

Detect

Incident appears in Slack or logs. ProdRescue ingests context.

Step 2

Diagnose

AI reconstructs timeline and root cause. Every claim linked to logs.

Step 3

Fix

Suggested patch → PR in your repo. Review. Merge. Done.

Example: real flow in under 5 minutes

03:47Incident detected

"Nil pointer panic in checkout-api" posted to #incidents

03:49RCA generated

Root cause: missing nil check in payment processing

03:51Fix suggested

PR #847: "Add nil check to PaymentService.Process()"

See ProdRescue in Action

You know the drill

Production incidents rarely happen at a convenient time.

  • 3 AM page. Logs everywhere. Exec asking "what happened?" before you had coffee.
  • Slack chaos. 200 messages in #incident. Nobody has the full picture. Context scattered.
  • Exec pressure. "What happened?" "How much revenue did we lose?" "When's the postmortem?"
  • Revenue uncertainty. Bug fixed. Quantifying impact? Another 4-hour investigation.
  • Blame culture. No single source of truth. No evidence. No institutional memory.

Before vs After

Noise → narrative. Tribal knowledge → documented intelligence.

Chaos — #incident

10:23 john: can someone check checkout?

10:24 sarah: seeing 5xx here

10:25 mike: logs? where are logs

10:26 john: s3? cloudwatch?

10:28 ... 200 more messages ...

Nobody has the full picture.

Rescue — Executive Report

Timeline · Root cause · Evidence [1][6][8]

Understand what broke first. Generate the report in one click when you need it.

Evidence-backed. No finger-pointing.

View the report generated from these logs →

This chaos → Executive-ready PDF. One click.

The 4-Layer Intelligence Engine

Denoise → RCA → Evidence mapping → Assembly. One model, one job.

1
GPT-4o

Denoising

Cleanup

Clean noisy logs.

2
Claude Opus 4.5

RCA

Analysis

Find the root cause. Timeline. Impact. Logs only.

3
Gemini 2.5 Pro

Evidence Mapping

Evidence

Every claim linked to real logs. [1], [6], [8] → source.

4
GPT-4o

Assembly

Assembly

Executive-ready report. Timeline. RCA. Action items.

What you get

  • Executive report (optional PDF, one click)
  • Timeline with evidence links [1], [6], [8]
  • Root Cause + Contributing Factors
  • Action Items (owner / priority / deadline)

Fix & PR Flow

Report → evidence → code search → AI fix → branch & PR.

1
Report

Evidence extraction

From report

[1], [6], [8] and code blocks from the report.

2
GitHub

Code search

Repo

Search repo by evidence. Fetch top file contents.

3
Claude Sonnet 4

Suggest fix

AI

Report + evidence + code → explanation, risk, diff & file.

4
GitHub

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

Confidence = how many claims we matched to real log lines. Not AI self-rating.

Generic AI

95%

Self-reported confidence

Model says confident. No way to verify. No log backing.

ProdRescue — Honest Score

60–100%

95% when mapping is solid · 60% when coverage is low

Most claims match logs → 95%. Verified. Low coverage → we cap the score. "Manual review recommended."

Evidence 30% → Score capped. Some claims not matched to logs.

Every claim → real log line. No hallucinations. Can't match enough? We tell you.

What Executives See

Real metrics from real incidents. No guesswork. No theater.

P1Payment Processing Degradation

Resolved

Revenue Impact

$67K

net loss

Peak Error Rate

34.7%

Failed Transactions

2,847

MTTR

18 min

vs 24 min avg

Confidence Score

95%

Customer impact, severity, and action owners — all in one report. Every claim linked to logs.

See more examples: Kubernetes · Redis · Stripe · Database →

Why teams don't rely on generic AI

Generic AI was never built for incident intelligence.No timeline. No impact. No evidence mapping.ProdRescue was.

Generic AI

  • No evidence linking
  • No timeline reconstruction
  • No incident impact calculation
  • No Slack war-room parsing

ProdRescue AI

  • 4 models, task-specific — evidence, timeline, impact
  • Every claim linked to log line [1], [6], [8]
  • Unified timeline + revenue impact & confidence score
  • Logs & Slack threads → structured report

Every claim links to evidence. Click [1], [6], [8] — Source Log highlights the line.

No hallucinations. Not in the logs? We don't say it.

Simple, Transparent Pricing

Plans for teams. Evidence-first. No training on your data.

One incident = hours of manual work. ProdRescue pays for itself in the first one.

Try your first incident — no login required.

Starter

Free

Try the full flow

  • 1 free report when you sign up
  • Basic features
  • Watermarked PDF
  • No saved history (upgrade to keep reports)
Get started
Founding Team

Founding Team

$29/month

Unlimited incident intelligence

  • Unlimited reports
  • PDF export & Slack integration
  • Evidence-backed RCA, timeline & action items
  • No watermark on PDFs
Start 14-day trial
Auto-fix

Autonomous

$99/month

GitHub suggest-fix & PR from your report

  • Everything in Founding Team
  • GitHub integration & repo connection
  • Suggest fix → branch & PR (or issue)
  • Code search from evidence in repo
Get Autonomous
Enterprise

Enterprise

Custom pricing

Regulated environments & large orgs

  • Everything in Autonomous
  • Unlimited seats
  • Custom deployment (VPC / on-prem)
  • SLA guarantee
  • Dedicated success manager
  • Custom integrations
  • Unlimited report history

Compare Plans

FeatureStarterFounding TeamAutonomousEnterprise
Reports1 freeUnlimitedUnlimitedUnlimited
Watermark on PDFYesNoNoNo
Price$29/mo$99/moCustom
Slack integrationLimited
GitHub auto-fix & PR
Custom deployment

Our Mission

ProdRescue exists for one reason:

Turn 3 AM incident chaos into institutional engineering memory.

Undisciplined docs end. Data-driven transparency begins.

Strong Privacy & Security

  • No log retention — we don't store your data
  • No training on customer data — ever
  • Ephemeral processing — in-memory only, then discarded

Top Incident Struggles

Based on survey of 50+ engineering teams

68%

Struggle writing clear, structured reports

54%

Can't find proper error messages quickly

42%

Spend hours on root cause analysis

Finish your next incident report in minutes

War-room or logs → executive report. Every claim linked.

Start 14-day trial

Get in touch

Turn your next incident into institutional memory.

We'll respond within 24 hours.

Why ProdRescue?

See it in action

Watch your own logs turn into executive-ready reports in seconds.

Security first

No log retention. No training. Ephemeral processing only.

Custom pricing

Team discounts, unlimited credits, and enterprise plans.

Or email us at info@prodrescueai.com

A Note from the Founder

Devrim Ozcay

I built ProdRescue because I've lived the 3 AM incident report nightmare. Logs scattered across Datadog and Slack. Exec asking "What happened?" before you've had coffee. Hours spent correlating events into a narrative that holds up to scrutiny.

The vision: every claim in an incident report should trace back to a log line. No hallucinations. No guesswork. A 4-layer pipeline — denoising, RCA, evidence mapping, assembly — so engineers spend time fixing, not writing.

If you're tired of manual postmortems, let's talk. I'm a real person, and I read every message.

I've also published several production engineering playbooks used by hundreds of backend engineers.

View engineering playbooks