Without ProdRescue
- 2+ hours log digging
- Slack back-and-forth
- Guess-based RCA
ProdRescueIncident workspace
Evidence-first incident RCA
Your last incident cost real money.Pinpoint root cause with proof in about two minutes.
Paste logs, drop a Slack thread, or run /incident where you already work. Each statement in the RCA points at a specific log line, not vague filler.
More than 30 teams already rely on ProdRescue when an incident lands. You're in good company.
Time to root cause
0s
When you stay on one incident
Evidence match
0%
Anchored to log lines
Where it runs
Slack-first
/incident in your channel
Add to Slack
Not on Slack yet? Open the workspace and paste logs. Your first full analysis is free.
No signup requiredEphemeral processing95% confidence scoring
incidents
war-room · 12 members · production
YOMLKPAI
12ML
Maya Lopez11:14
checkout-api alerts firing 🚨: anyone else seeing 5xx?
KP
Kai Park11:14
yep, PSQLException pool exhausted, started ~17:31. tied to the v2.15.0 deploy?
Message #incidents…
Live preview Same flow runs in your channel after /incident.
With ProdRescue
- 2-minute first draft
- Evidence-linked root cause
- Clear next fix actions
If an engineer hour costs ~$50, one 2-hour incident is ~$100+ in debugging time. A $5 run is usually cheaper than one incident hour.
After your free run: use $5 pay-per-run for the next deep dive, or Pro when you want unlimited analyses and /incident in Slack.
Integrations · models · clouds
Plays well with the stack you already ship
SlackGitHubOpenAIAnthropicGeminiDatadogSentryPagerDutyAWSGoogle CloudKubernetesVercelNext.jsPostgresRedisGrafanaPrometheusNotionJiraLinearSlackGitHubOpenAIAnthropicGeminiDatadogSentryPagerDutyAWSGoogle CloudKubernetesVercelNext.jsPostgresRedisGrafanaPrometheusNotionJiraLinear
AWSGoogle CloudKubernetesVercelNext.jsPostgresRedisGrafanaPrometheusNotionJiraLinearSlackGitHubOpenAIAnthropicGeminiDatadogSentryPagerDutyAWSGoogle CloudKubernetesVercelNext.jsPostgresRedisGrafanaPrometheusNotionJiraLinearSlackGitHubOpenAIAnthropicGeminiDatadogSentryPagerDuty
The problem
Incident debugging is messy
Logs are noisyErrors are unclearEveryone guesses
ProdRescue shows what broke and ties the narrative to real log lines.
Evidence mapping
Every claim maps to a log line
This is not “trust the model.” In ProdRescue, each point in the RCA carries a numbered reference to the line that supports it. Click through in the app, then take the same citations into the PDF.
Generated RCA · claims
Tap a claim to jump to its source. In your real report, every citation is a clickable link to the exact log line.
checkout-api · productionsource.log
- INFO[deploy-runner]release="v2.15.0" pool_size=10 prev_pool_size=30 changed=true
- ERROR[checkout-api]request_id=ab12c upstream_timeout_ms=30000 endpoint="/cart/checkout"
- FATAL[checkout-api]org.postgresql.util.PSQLException: connection pool exhausted (active=10/10)
- WARN[checkout-api]fallback=redis-cache cache_miss_rate=0.94 cart_ops_affected=true
- ERROR[edge-router]service="checkout-api" 5xx_rate=0.73 window=60s requests=4218
- INFO[deploy-runner]rollback initiated from="v2.15.0" to="v2.14.3" reason="db_pool_exhaustion"
- INFO[checkout-api]pool recovered active=12/30 5xx_rate=0.04 status="normalizing"
Every claim is grounded in raw evidence. No guesswork.
Zero uncited claimsClick any [N] to open the source lineCitations survive into the executive PDF
Same rule for Slack
Slack threads work the same way
Run /incident or paste a thread. Pager blurbs, deploy bots, and human messages all become first-class evidence, and each claim links to the message it came from.
RCA from Slack · claims
Tap a claim to jump to its Slack source. In a real RCA, every citation is a clickable link to the exact message in the war-room thread.
#incident-checkout8 msgs
- PagerDutyApp17:31
Triggered:
checkout-api 5xx spike· severityP1· routing to #incident-checkout - Jenny Liu17:31
Seeing
PSQLException: pool exhausted (active=10/10): that 17:25 deploy from alex ? - deploy-runnerApp17:32
Release
v2.15.0shipped 17:25 ·pool_size: 30 → 10(config drift) - Alex TanakaOn-call17:33
Redis cache_miss_rate climbed to
0.94: cart traffic is fanning straight back to the saturated DB. - edge-routerApp17:33
Window 60s ·
5xx_rate=0.73·requests=4218· service=checkout-api - Jenny Liu17:34↳ replying to msg 3
Rolling back to
v2.14.3now. oncall-platform standing by. - deploy-runnerApp17:35
Rollback complete ·
v2.14.3live · reason=db_pool_exhaustion - Jenny Liu17:36
Pool back to
active=12/30·5xx_rate=0.04· I think we're recovering. ✅
Bots, humans, and pager events are cited the same way.
PagerDuty + Opsgenie events cited inlineHuman chatter mapped just like log linesBot deploys, rollbacks & mentions preserved
Sample
Sample output
Root cause: DB connection pool exhausted
Fix: Increase pool size (10 → 30)
Preview. Full confidence scoring and exports unlock in the workspace. Run your free analysis →
Product
Everything in one workspace
Structured output for production, not another noisy chat thread.
Find the root cause
Pinpoint what broke from raw logs in seconds.
Understand logs instantly
Turn noisy traces into a clear failure story.
Get fix suggestions
Receive next actions you can ship right away.
How we're different
Built for production reliability
Skip improvised war rooms. Use a structured first pass you can hand off.
ProdRescue is purpose-built for incidents, not broad chat. It checks claims against evidence, filters noise, and stitches logs, Slack, and deploy context into one readable path from symptom to root cause.
01Pillar
Evidence over guesses
Every conclusion maps back to real log lines.
Proof
Claims and source lines live in the same report.
02Pillar
Workflow, not chat noise
Output is structured for incident response and handoff.
Proof
Timeline, RCA, and action items you can paste into a ticket.
03Pillar
Context filter for noisy logs
ProdRescue pre-filters signal before deep analysis.
Proof
Errors and correlation context instead of a 5k-line paste.
04Pillar
Multi-stack incident context
Logs, Slack context, and deploy signals are reconciled.
Proof
One narrative from symptom to root cause across teams.
Board-ready exports
Copy your RCA and paste directly into your incident ticket workflow.
JiraConfluenceNotion
Context filter · multi-stack
Input stream
5,000 raw lines + Slack thread + deploy metadata
ProdRescue output
Relevant error chain, correlated IDs, cross-team narrative with evidence links.
Example: Frontend reports latency, but correlated backend logs show DB connection timeout after a recent deploy. ProdRescue links the timeline, evidence lines, and fault boundary in one RCA.
Pricing
Simple, transparent
Default path. Start free in the workspace, add $5 runs when incidents are rare, or move to Pro when the team lives in Slack and runs RCAs every week.
Transparent usage · No surprise seat fees · Upgrade or downgrade anytime
At a glance
- Exploring or one incident a quarter? Stay on Free, then buy a $5 run when you need another full-depth RCA.
- On-call most weeks and want
/incidentin-channel? That's Pro ($29/mo after trial), with unlimited analyses and no per-run meter.
Starter
Free
$0
1 analysis included
- Fast RCA preview
- Root cause + fix summary
- No migration required
After your free run
Pay-per-use
$5/ run
Occasional incidents, pay only when you need depth
Flat price · no seat math
- Full incident debugging output
- Best when RCAs are rare or unpredictable
- Buy credits inside the workspace when you're ready
Teams in Slack
14-day trial · then billingPro
$29/ mo
Unlimited analyses + in-channel /incident
- Unlimited analyses (no per-run meter)
- Slack in-channel RCA (/incident, mentions)
- Built for weekly on-call and recurring incidents
- 14-day trial before your first invoice
Contact
Talk to us about your incident workflow
Share your use case, integration needs, or rollout questions. We'll reply with a practical setup path.
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