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Welcome to SerOps

SerOps is an AI-powered incident detection and resolution platform. It connects to your existing observability, SCM, and CI/CD tools — correlating logs, metrics, code changes, and deployments to automatically detect issues and trace them to their root cause.

  • Getting Started

    Set up SerOps and create your first project in minutes.

    Quick Start

  • Connect Your Stack

    Bring your own observability, SCM, and CI/CD tools. SerOps integrates with Elasticsearch, Prometheus, Datadog, GitLab, GitHub, and more.

    Integrations

  • AI-Powered Correlation

    Pluggable AI models analyze your logs alongside metrics and code changes to pinpoint root causes and recommend fixes.

    AI Chat

  • Security First

    All data is scanned for PII and credentials before AI analysis. SSO, RBAC, and full audit logging included.

    Data Protection

How SerOps Works

SerOps sits on top of the tools you already use. It pulls data from four sources and uses AI to connect the dots:

graph LR
    A[Logs<br/>Elasticsearch, Loki, Splunk,<br/>Datadog, CloudWatch, GCP, Azure...] -->|pull| E[SerOps]
    B[Metrics<br/>Prometheus, VictoriaMetrics, Mimir,<br/>Datadog, CloudWatch, GCP, Azure...] -->|pull| E
    C[Code Changes<br/>GitLab, GitHub,<br/>Bitbucket, Azure DevOps] -->|webhook| E
    D[CI/CD Pipelines<br/>GitLab CI, GitHub Actions,<br/>Jenkins, Bitbucket...] -->|webhook| E
    E --> F[AI Analysis &<br/>Issue Detection]
    F --> G[Root Cause +<br/>Commit Correlation]
    G --> H[Alerts &<br/>Escalation<br/>Email / Slack / Teams]

Example: SerOps detects a spike in database connection errors in your production logs, correlates it with a CPU spike from Prometheus, and links it to a commit pushed 30 minutes ago — then alerts your team via Slack with a root cause analysis and fix recommendation.

Key Features

Feature Description
Issue Detection AI classifies 26+ issue types with smart fingerprinting and deduplication
Commit Correlation Links production issues to the exact code changes and deployments that caused them
Metric Correlation Detects infrastructure anomalies (CPU, memory) that coincide with application errors
Multi-Environment Separate analysis for development, staging, and production with branch pattern matching
AI Chat Conversational AI for investigating issues with full context from logs, metrics, and code
Model Comparison Re-analyse batches with different AI models and compare results side-by-side
PII Redaction Automatic scanning and masking of sensitive data before AI processing
Notifications Slack, Teams, and email alerts with quiet hours, digests, and 2-level escalation
Issue Lifecycle Muting, auto-resolution, acknowledgement, and MTTR tracking
20 Integrations 4 SCM, 5 CI/CD, and 11 observability providers (~90% of the enterprise market) with encrypted credential storage
Team Management RBAC (admin/user/viewer), team invitations, SSO (OIDC/SAML)
Audit & Compliance Full audit trail with export, data retention policies, and GDPR support