Overview:
A platform designed to help companies detect hidden system issues—such as glitches, data breaches, or account break-in methods—that are often difficult to catch. It enables real-time monitoring, anomaly detection, error tracing, and secure user access management.
- Connect to company tools like databases, APIs, and log systems.
- Real-time or scheduled data collection.
- Support for both structured (tables) and unstructured (logs, messages) data.
- Continuous tracking of system events, errors, user flows, and performance.
- Support for frontend, backend, and infrastructure monitoring.
- Log collection from multiple services or devices.
- Rule-based detection for obvious issues (e.g., missing data, timeouts).
- Machine Learning-based detection for unusual patterns (e.g., sudden user drop-offs).
- Identification of both technical errors and silent process failures.
- Interactive dashboards showing issues, trends, and KPIs.
- Real-time visual feedback: charts, timelines, heatmaps.
- User journey mapping to trace drop-offs or failures.
- Trace issues back to the source (service logs, failed transactions).
- Dependency mapping to understand system relationships.
- Probable cause suggestions for faster resolution.
- Role-based access control (Admin, Analyst, Developer, etc.).
- Audit trails for changes and access logs.
- Secure authentication (SSO, 2FA).
- Pre-configured startup script for quickly running the platform in development or production mode.
- One-command execution for setup and launch.
- Easy customization via the
package.jsonor a dedicatedstart.shfile. - Supports environment variables for flexible configuration.
- React – Ideal for scalable dashboards.
- Vue.js – Lightweight and beginner-friendly.
- Angular – Suitable for enterprise-level platforms.
- Chart.js – Clean, simple charts.
- Recharts – Optimized for React.
- D3.js – Advanced and customizable visuals.
- Databases: PostgreSQL, MongoDB, BigQuery.
- Time-series Databases: InfluxDB, TimescaleDB.
- ML Models (optional): Scikit-learn, TensorFlow, PyTorch.
- Encryption (in-transit & at-rest).
- Audit logging.
- Role-based user management.
- Real-Time Monitoring: Capture and analyze live system events.
- Glitch & Black Hole Detection: Identify and respond to hidden failures.
- Advanced Analytics: Use ML to uncover patterns.
- Secure Access: Ensure compliance and restrict sensitive areas.
- Data Encryption: All sensitive data encrypted during transmission and storage.
- Audit Logging: Track every change and access event.