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On-Call Engineer.

OnCall Owl is your team's first responder. It investigates, triages, and reports.

OnCall Owl — Investigation #417
Payment Service Incident
3:12 AM
Trigger
CRITICAL Payment service error rate spiked to 23%
Investigate
Owl
New Relic - 23% error rate on /checkout
PostgreSQL - "too many clients already"
GitHub - deploy at 2:15 AM added 3 DB connections
Root Cause
Owl
DB connection pool exhausted (153/100)
Caused by 2:15 AM deploy adding 3 new connections per request
Complete
Owl

Investigation complete. 2 min

Owl
Resolution Report sent to #on-call

Notified 4 members

Owl's brain is wired into your stack

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How Owl Works

Engineers

Write code

Coding Agent

Cursor, Claude Code

GitHub

Code & deploys

Production

Live services

Incident

OnCall Owl

Trigger → Investigate → Report

Brain: GitHub · Runbooks · Tools

Generates
Resolution Report
Critical
EvidenceRecommendationsFix Prompt
Action

On-Call Engineer

Acts only with your approval

Everything an On-Call Engineer Does, at 10x Speed

Detects and investigates autonomously
Reads logs, metrics, and code changes in parallel. No context-switching.

Investigating...3:12 AM
New Relic
23% error rate
PagerDuty
3 alerts today
GitHub
2 deploys found
PostgreSQL
querying logs...
4 tools queried in parallel3 of 4 complete

Reports with evidence, not assumptions
Every finding cites its source with structured summary and confidence scoring.

Owl Report#417
Critical
Root Cause

Config deploy removed connection pool limits, causing exhaustion

Affected

payment-service, checkout-api

Confidence
High
Evidence
New Relic: error rate 4% → 23% at 3:04 AM
GitHub: pool_config.yml changed in deploy #1847
PostgreSQL: "too many clients" at 3:06 AM
Recommendation

Rollback deploy #1847 to restore pool size limits

Connects to your stack once
Runbooks, wikis, and tools -Owl learns your full on-call system.

Fix Prompt for your coding agent
Every report includes a copy-paste prompt with root cause, files, and exact steps to resolve.

Fix Promptfor AI coding agents

# Fix: DB pool exhaustion

## Root cause

v2.4.2 opens 3 DB conn/req (was 1)

Pool: 100 max, 153 current

## Files to change

config/database.yml pool: 50

src/db/client.ts reuse conn

## Rollback

Revert to v2.4.1 if fix fails

Copy to Cursor / Claude Code

Every investigation searchable
Browse past incidents, compare patterns. Nothing lost in Slack threads.

Search past investigations...142
#421Medium
Today, 9:14 AM

Redis memory spike -cache eviction storm

#420Critical
Yesterday, 2:31 AM

Auth service 502s -expired TLS cert

#417Critical
Feb 6, 3:04 AM

DB pool exhaustion -config deploy regression

#415Low
Feb 4, 11:22 PM

Stripe webhook timeout -rate limit hit

142 investigationsMTTR down 64%

Notifies your team on resolution
Summary to Slack with root cause, evidence, and next steps.

#incidents3:18 AM
🦉
OnCall OwlAPP3:18 AM

Resolved: DB pool exhaustion

Severity

Critical

Duration

14 min

Confidence

High

Root Cause

Deploy #1847 removed connection pool limits

Action Taken

Rolled back to deploy #1846 ✓

View full report →
👍 3🎉 2🦉 1
Much like Cursor brought an AI copilot that reads and reasons about codebases in real time, OnCall Owl does the same for incidents. 90% fewer war room hours.
Sharath Keshava Narayana avatar
Sharath Keshava Narayana
CEO, Sanas

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<5 min
from incident to root cause
Cut your MTTR from hours to minutes.
Every
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Your team focuses on fixing, not finding.
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Tool name, timestamp, and exact data point. No hallucinations.

Frequently Asked Questions

Questions investors and teams ask us the most

Why not just hire another on-call engineer?
An engineer costs $150-200K/yr, takes months to ramp, and still can't investigate at 3 AM without burnout. Owl costs a fraction, works instantly, and scales with your incident volume. It's not a replacement, it's the first responder that makes your team 10x faster.
What happens when Owl gets it wrong?
Owl never acts without your approval. Every finding cites its source with confidence scores. If confidence is low, Owl says so. Worst case, you ignore the report and investigate yourself. Same as today, but you lost nothing.
How is this different from PagerDuty or Datadog AI?
Those tools alert you. Owl investigates for you. PagerDuty tells you something is broken. Owl tells you why it broke, what caused it, and what to do about it. It queries across all your tools in parallel and delivers a root cause report, not just a notification.
How long does it take to set up?
Connect your tools, Owl starts investigating on the next alert. No training data, no fine-tuning, no 6-month rollout. Most teams are live within a single call.
What if Owl can't find the root cause?
Owl gives you everything it found with confidence scores. Even a partial investigation saves your engineer 30 minutes of context-switching across dashboards. Not every case gets solved, but every case gets documented.

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