An enterprise-grade, high-performance incident tracking and mitigation platform powered by AI-driven analytics, Natural Language Processing (NLP), and vector embeddings.
- AI-Driven Analytics: Machine Learning classifiers for automatic incident severity and category assessment.
- Lessons Learned Library: Aggregated repository of closed incidents highlighting root causes, actions taken, and key takeaways.
- Live AI Recommendations: Real-time suggestion engine offering historical resolutions and recommendations using vector similarity embeddings as you draft new incidents.
- Predictive Risk Scoring: Proactive cluster and category forecasting to prevent upcoming incidents.
We have migrated from a Vagrant-based VM setup to running the project directly on Windows using Docker Desktop with WSL 2 integration. This setup is faster, more performant, and runs cleanly using Linux containers on Windows.
- Install Docker Desktop for Windows.
- Enable the WSL 2 backend in Docker Desktop settings.
- Ensure WSL integration is enabled for your default distro (e.g., Ubuntu) under
Settings > Resources > WSL integration.
- Clone this repository to your local machine.
- Open your terminal in the project root directory.
- Start all services in detached mode:
docker compose up -d
- Verify the container status:
docker compose ps
To stop and remove the active containers:
docker compose downTo stop containers while retaining their data volumes:
docker compose stopA backup utility script is provided at the project root for capturing PostgreSQL snapshots.
To back up the database:
bash ims_backup.shThe snapshot will be saved to the ./backups/ directory.
To restore the database from a backup file:
cat backups/ims_backup_YYYYMMDD_HHMMSS.sql | docker exec -i ims_postgres psql -U ims_user -d incident_management- Frontend Application: http://localhost:3000
- Core Backend Service: http://localhost:8000
- AI Analytics Service: http://localhost:8001