DataCenterAIOps

Open-source AIOps control center for logs, metrics, traces, incidents, and early product feedback.

DataCenterAIOps is a lightweight MVP for turning noisy telemetry into a cleaner incident workflow. It gives teams a live dashboard for service health, correlated incidents, root-cause hints, and an in-app feedback flow to validate the product before full rollout.

Live Demo

• Demo: https://data-center-ai-ops.vercel.app/
• Repository: https://github.com/SamoTech/DataCenterAIOps

Preview

The current live demo includes:

• A dark AIOps dashboard landing page
• Incident feed with severity and root-cause hints
• Service health table with latency, error rate, and uptime
• Early-access feedback form for beta validation
• API routes for telemetry, incidents, services, and feedback

Why this project

Modern teams often have logs in one tool, metrics in another, alerts in a third place, and feedback somewhere else. DataCenterAIOps starts with a simpler idea: one place to understand what is happening, what is impacted, and what should happen next.

This repository is currently focused on the MVP stage:

• Modern dashboard UI for incidents and service health
• Mock AIOps-style incident correlation and risk scoring
• Telemetry ingestion endpoints for experimentation
• Early-access feedback collection inside the product
• Strong starter base for a production-ready AIOps platform

Current Features

Dashboard

• Hero overview with risk score and active incident summary
• Incident feed with severity, status, impact, and likely cause
• Service health table with latency, error rate, and uptime metrics
• MVP proof section that explains the direction of the product

API

• GET /api/incidents → Returns incident feed data
• GET /api/services → Returns service health data
• GET /api/telemetry → Returns telemetry overview and supported signals
• POST /api/telemetry → Accepts telemetry-like payloads and returns AIOps guidance
• POST /api/feedback → Accepts beta feedback from demo users

Feedback Flow

• Name, email, role, and team-size capture
• 1–5 rating input for fast product validation
• Free-text product feedback submission
• Frontend success and error handling for form submissions

Tech Stack

• Next.js 14
• React 18
• TypeScript
• App Router
• Vercel deployment workflow

Quick Start

1) Install dependencies

npm install

2) Run locally

npm run dev

3) Open the app

http://localhost:3000

API Examples

Send telemetry

curl -X POST http://localhost:3000/api/telemetry \
  -H "Content-Type: application/json" \
  -d '{
    "service": "payments-api",
    "signal": "latency",
    "value": 920,
    "severity": "high",
    "message": "Latency spike detected"
  }'

Submit feedback

curl -X POST http://localhost:3000/api/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Ossama",
    "email": "beta@example.com",
    "rating": 5,
    "role": "DevOps Engineer",
    "teamSize": "1-10",
    "message": "Strong concept, next I want Telegram alerts and persistent storage"
  }'

Architecture

flowchart TD
    A[User / Operator] --> B[Next.js Dashboard]
    B --> C[API Routes]
    C --> D[Telemetry Endpoint]
    C --> E[Incidents Endpoint]
    C --> F[Services Endpoint]
    C --> G[Feedback Endpoint]

    D --> H[Mock Telemetry Processing]
    E --> I[Incident Feed Data]
    F --> J[Service Health Data]
    G --> K[Beta Feedback Intake]

    H --> L[Risk Score + AIOps Hints]
    I --> B
    J --> B
    K --> B

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Project Structure

DataCenterAIOps/
├── app/
│   ├── api/
│   │   ├── feedback/
│   │   │   └── route.ts
│   │   ├── incidents/
│   │   │   └── route.ts
│   │   ├── services/
│   │   │   └── route.ts
│   │   └── telemetry/
│   │       └── route.ts
│   ├── globals.css
│   ├── layout.tsx
│   └── page.tsx
├── components/
│   ├── feedback-form.tsx
│   ├── metric-card.tsx
│   └── section-card.tsx
├── lib/
│   └── mock-data.ts
├── .gitignore
├── next-env.d.ts
├── next.config.js
├── package.json
├── README.md
└── tsconfig.json

Product Direction

This MVP is the first step toward a fuller AIOps platform with features such as:

• OpenTelemetry ingestion
• Real alert correlation
• Persistent incident storage
• Slack and Telegram notifications
• Auth and team workspaces
• Postmortem generation
• Admin review for beta submissions
• AI-powered root-cause explanation

Deployment

This project is ready to deploy on Vercel.

Typical workflow:

1. Connect the GitHub repository to Vercel
2. Use main as the production branch
3. Push changes to GitHub
4. Let Vercel build and publish automatically

Contributing

Contributions, ideas, and product feedback are welcome.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Open a pull request

If you want to suggest a major feature, open an issue first so the direction stays aligned.

Support

• Issues: https://github.com/SamoTech/DataCenterAIOps/issues
• Repository: https://github.com/SamoTech/DataCenterAIOps
• GitHub Profile: https://github.com/SamoTech

Sponsor

If this project helps you or your team, support development through GitHub Sponsors:

• GitHub Sponsors: https://github.com/sponsors/SamoTech

Roadmap

Near term

• Persist feedback submissions
• Add admin view for responses
• Improve API validation
• Add loading and empty states

Mid term

• Real telemetry ingestion
• Incident timelines
• Notification routing
• Team access control

Long term

• Multi-project workspaces
• AI incident summaries
• Suggested remediation steps
• Full observability integrations

License

MIT