AI-Powered Incident Response

Your autonomous on-call engineer.

When production breaks at 2 AM, Incident Copilot investigates logs and traces, identifies the root cause, validates a fix, and prepares a response before the team wakes up.

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Before & After

See how incident-copilot transforms your on-call experience

Without incident-copilot

2:14 AM
PagerDuty wakes you up
2:30 AM
Coffee, login to 5 different dashboards
2:45 AM
Digging through internal runbooks...
3:15 AM
Searching Slack threads for similar issues
3:45 AM
Reading past incident reports
4:30 AM
Still grepping through logs manually
5:30 AM
Root cause finally identified
7:00 AM
Exhausted, drafting the postmortem
Time to RCA~5 hours

With incident-copilot

2:14 AM
PagerDuty alert triggers incident-copilot
2:15 AM
RAG pipeline searches runbooks & past incidents
2:16 AM
Relevant context retrieved and reranked
2:17 AM
Hypotheses generated with evidence
7:00 AM
Wake up refreshed, review the triage
Time to RCA~4 minutes

Interactive Investigation Demo

Watch the AI investigate a production incident in real-time. See how it correlates logs, traces, and deployments to identify the root cause.

Click to launch the interactive demo

How Does It Work?

Incident-copilot automates investigation and provides actionable insights for your review.

1

Investigate

When an incident triggers, incident-copilot automatically analyzes logs, traces, metrics, and deployment history to understand what went wrong.

  • Correlates recent deployments with error patterns
  • Searches indexed runbooks and past incidents
  • Uses hybrid retrieval (dense + BM25) for optimal recall
  • Reranks results for relevance to current incident
2

Identify

Using multi-step reasoning and evidence grounding, incident-copilot generates a root cause hypothesis with confidence scoring.

  • Chain-of-thought reasoning through evidence
  • Cross-references similar past incidents
  • Validates hypothesis against deployment timeline
  • Provides confidence scores and reasoning chain
3

Suggest

Based on the identified root cause, incident-copilot suggests relevant runbooks, remediation steps, and a potential fix based on your codebase patterns.

  • Retrieves relevant runbooks and documentation
  • Suggests remediation steps based on past resolutions
  • Generates fix suggestion following your coding standards
  • Includes proper error handling and context

Built for Production

Enterprise-grade architecture designed for reliability, scalability, and security.

Incident Ingestion

Real-time integration with PagerDuty, Datadog, and custom webhooks

WebhooksKafkaRedis

Query Understanding

LLM-powered query rewriting and semantic understanding

Claude 3.5GPT-4Embeddings

Hybrid Retrieval

Dense vectors + BM25 with RRF fusion for optimal recall

PineconeElasticsearchCohere Rerank

Knowledge Base

Indexed runbooks, past incidents, deployment history, and metrics

PostgreSQLVector DBS3

Reasoning Engine

Multi-step reasoning with evidence grounding and confidence scoring

LangGraphChain-of-ThoughtSelf-Critique

Deploy Correlation

Automatic correlation with recent deployments and config changes

GitHubArgoCDKubernetes

Triage Generation

Structured incident response with hypotheses, next steps, and evidence

ClaudeLangGraphGrounding

Investigation Pipeline

Incident Received
Query Rewrite
Hybrid Retrieval
Reranking
Deploy Correlation
Triage Generation

Simple Pricing

Start with the open source version, upgrade when you need enterprise features.

Open Source

Free

Self-hosted solution for teams getting started

  • Full investigation pipeline
  • Root cause analysis
  • Incident-copilot recommends actionable suggestions
  • Community support
  • Deploy on your infrastructure
Get Started

Enterprise

Custom

For teams requiring advanced features and support

  • Everything in Open Source
  • SSO & SAML integration
  • Advanced analytics dashboard
  • Custom integrations
  • Priority support & SLAs
  • Dedicated success manager
  • On-premise deployment options