OPSMIND

OpsMind monitors applications 24/7, explains incidents with AI, finds root causes in code, and automatically creates fixes through GitLab merge requests.

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Inspiration

Modern development teams spend too much time reacting to incidents instead of preventing them. When a production error happens, engineers often need to search through logs, trace stack traces, identify the root cause, and manually create fixes. We wanted to build an AI-powered SRE teammate that can perform these tasks automatically and reduce incident response time from hours to minutes.

What it does

OPSMIND is an AI-powered incident response platform that continuously monitors applications and infrastructure.

When an error occurs, OPSMIND:

  • Detects incidents in real time from logs.
  • Groups related errors into incidents.
  • Explains the issue for different audiences (Junior Engineer, Senior Engineer, CEO).
  • Uses Orbit code intelligence to identify the root cause, affected files, and repository impact.
  • Generates AI-powered code fixes.
  • Creates GitLab merge requests automatically.
  • Sends alerts through Telegram and Email.

The platform transforms raw production failures into actionable fixes.

How we built it

We built OPSMIND using:

  • FastAPI for the backend API.
  • PostgreSQL for incident and project storage.
  • Redis for real-time event streaming.
  • React for the dashboard UI.
  • GitLab API integration for repository access and automated merge requests.
  • Google Gemini for AI explanations and code generation.
  • Orbit for repository indexing, dependency analysis, and impact assessment.

The system collects logs, analyzes incidents, enriches them with repository intelligence, and generates AI-assisted remediation workflows.

Challenges we ran into

One of the biggest challenges was connecting runtime incidents with repository-level code intelligence.

We had to:

  • Parse stack traces accurately.
  • Identify the real application file instead of framework files.
  • Build repository graphs dynamically for different projects.
  • Correlate production errors with source code locations.
  • Generate safe code fixes without modifying unrelated files.

Another challenge was making the AI explanations useful for both technical and non-technical users.

Accomplishments that we're proud of

We successfully built an end-to-end autonomous incident response workflow.

Key achievements include:

  • Real-time incident detection.
  • AI-generated root cause analysis.
  • Orbit-powered repository impact analysis.
  • Automatic GitLab merge request generation.
  • Multi-level explanations for different audiences.
  • Telegram and Email alerting.

The platform can identify a production failure, locate the responsible file, explain the issue, and propose a fix with minimal human intervention.

What we learned

During development we learned:

  • Advanced incident management workflows.
  • Repository graph analysis using Orbit.
  • Large-scale log processing techniques.
  • AI-assisted software maintenance.
  • Automated remediation pipelines.
  • Building reliable integrations with GitLab and monitoring systems.

What's next for OPSMIND

Future plans include:

  • Kubernetes-native monitoring.
  • Multi-repository dependency tracking.
  • Predictive incident detection.
  • Automatic deployment validation.
  • Support for GitHub and Bitbucket.
  • AI-powered rollback recommendations.
  • Deeper repository graph analytics using Orbit.

Our vision is to evolve OPSMIND into a fully autonomous AI Site Reliability Engineer capable of detecting, diagnosing, and resolving incidents before users are affected.

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