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Your service is down.Find the root cause in 2 minutes.Fix it before anyone asks.
From logs or Slack -> find what broke -> fix in 2 minutes. Stop digging through logs.
Try your first incident with no signup. Upgrade when you need ongoing runs and saved history.
No signup required.
Try with your own logs.
See real RCA examples→What takes hours → now takes minutes. Save 4-8 hours every incident.
Teams handling weekly incidents trust ProdRescue.
Used during real incidents. Not for testing.
INCIDENT MODE ACTIVEStatus: DegradedTime since start: 14mConfidence: 95%
You've already been debugging for 15 minutes. You still don't know what broke.
Live incident demo
You're in the middle of an incident.
Slack is exploding. Logs everywhere. Clear next action in 2 minutes.
Raw incident data
Noisy, unstructured03:14:02 ALERT checkout-api 5xx > 22% 03:14:18 ERROR panic: nil pointer at PaymentService.Process() 03:14:26 WARN redis timeout (pool exhausted) 03:14:41 WARN p99 latency 28.4s 03:15:03 ERROR failed transactions: 1,247 03:16:20 INFO rollback started v2.15.0 -> v2.14.3
Live incident view
Action-readyWhat is breaking RIGHT NOW
Error rate spike 34.7% · checkout-api failing · 1,247 failed transactions.
Most likely root cause
Missing nil safety in PaymentService.Process() after v2.15.0 deploy.
What to fix first
Rollback v2.15.0 · patch nil check · restart checkout workers.
Suggested fix + PR
Patch prepared with retry guard + test case, PR draft ready.
Timeline
alert -> error spike -> pool exhaustion -> rollback -> recovery.
Executive summary
Customer impact, revenue risk, and owners ready to share.
Next best action: Roll back checkout-api to v2.14.3 immediately.
Generated from 200+ logs in 2 minutes.
Live analysisIncident viewFix firstPR ready
Chaos RCA Fix PR
Chaos -> RCA -> Fix -> PR in one focused flow.
Built for teams handling real incidents
SREs on-call
Backend engineers debugging production
Teams using Slack war rooms
Companies with weekly incidents
Don't just understand incidents. Fix them.
Logs -> Root cause -> Fix generated automatically -> PR ready in your repo
Find what broke. Fix it in 2 minutes. Stop digging through logs.
Slack · Live
Already using Slack for incidents?
Your incident timeline is already in Slack.
If your incidents are in Slack, you're already using ProdRescue — just not automated yet.
"This saved us 3 hours during a live incident. We stopped digging through 200 Slack messages."
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. Find what broke fast.
Paste logs in appUse Slack
Connect Slack. During an incident, get root cause from your war-room thread.
Open app with SlackFrom 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
03:47 — Incident detected
"Nil pointer panic in checkout-api" posted to #incidents
03:49 — RCA generated
Root cause: missing nil check in payment processing
03:51 — Fix 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
Before: 200 Slack messages. No clarity. After: Clear root cause. Timeline. Fix ready.
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 — RCA + Brief
Timeline · Root cause · Evidence [1][6][8]
Find what broke first. Share executive summary when leadership asks.
Evidence-backed. No finger-pointing.
View the RCA generated from these logs ->
Chaos -> clarity in 2 minutes. Optional PDF when needed.
How it works
Multiple models. Each optimized for one job. From incident chaos to clear RCA and action.
Analysis engine
See root cause. Understand the timeline. Know what to fix.
Fix flow
Evidence to code search to suggested fix and PR — with engineer approval.
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
After your team understands the root cause, leadership gets the executive view.
P1Payment Processing Degradation
ResolvedRevenue 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.
Why generic AI fails incidents
Generic AI misses incident context.ProdRescue is built for RCA, evidence, and action.
Generic AI
- No evidence linking
- No timeline reconstruction
- No impact clarity
ProdRescue AI
- Task-specific models for evidence, timeline, and RCA
- Every claim linked to log line [1], [6], [8]
- Unified timeline with impact and confidence
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. If you handle even 1 incident per month, this pays for itself.One incident pays for this.Cheaper than one hour of debugging.
Try your first incident — no login required.
Starter
Free
Perfect for trying out
- Try your first incident with no signup. Upgrade when you need ongoing runs and saved history.
- Full experience for each: PDF, RCA, action items
- Post to Slack, copy to Teams — same as paid
- No report storage (upgrade for history)
Early Adopter
Incident Intelligence
$29/month
Less than 1 hour of senior engineer time. ROI in the first incident.
- Unlimited incident analysis
- PDF export
- Structured RCA
- Action items (owner + deadline)
- Slack bot included
Cancel anytime. No contracts. No lock-in.
Start Incident IntelligenceNEW
Auto-Fix
$99/month
Auto-fix powered by AI
- Everything in Incident Intelligence
- GitHub integration
- Auto-fix suggestions
- PR creation
- Code search & analysis
Most Scalable
Enterprise
Contact Sales for Custom Pricing
For teams and organizations
- Unlimited reports
- Team workspace
- Custom deployment (VPC / On-Prem)
- SOC2 & GDPR alignment
- Dedicated support
- Custom branding
- Custom terminology
- PDF whitelabeling
Compare Plans
| Feature | Starter | Incident Intelligence | Auto-Fix | Enterprise |
|---|---|---|---|---|
| Incident Analysis | 1 free | Unlimited | Unlimited | Unlimited |
| RCA Analysis | ||||
| Slack Integration | Included within Starter limit (first incident) | |||
| GitHub Integration | — | — | ||
| Auto-Fix Suggestions | — | — | ||
| PR Creation | — | — | ||
| Custom Deployment | — | — | — |
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.
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
Understand your next incident in 2 minutes
War-room or logs → root cause clarity. Share a report if needed.
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 action-ready incident output in 2 minutes.
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
I built ProdRescue after too many 3 AM incidents. Logs in one tab, Slack in another, leadership asking "What happened?" while the team is still piecing it together.
My rule is simple: every claim should trace to evidence. No guesswork. Engineers should spend time fixing, not rewriting incident timelines.
If you're tired of manual incident write-ups, let's talk. I'm a real person, and I read every message.
I also publish production engineering playbooks used by backend teams.