AI-powered internal operations and asset intelligence platform built with ASP.NET Core, C#, SQL Server, Bootstrap, Chart.js, and an AI-ready insights layer.
OpsIntel AI helps managers understand operational data without digging through spreadsheets. It combines workforce analytics, asset tracking, checkout history, overdue alerts, low-stock signals, exports, and natural-language management summaries in one dashboard.
The core idea: internal teams already generate operational data every day. OpsIntel AI turns that data into decisions.
- Track office assets such as laptops, monitors, chairs, accessories, and software licenses
- Record employee checkouts and returns
- Flag overdue returns automatically
- Detect low-stock and high-demand asset risks
- Generate an AI-style asset health report
- Ask natural-language questions like "what stock is at risk?" or "which department has the highest asset load?"
- Export asset health to CSV
- Ask natural-language questions about operations and assets
- Generate a director-ready executive brief
- Show confidence score based on available data depth
- Surface high/medium/low risk signals with evidence
- Produce recommended actions with owner and priority
- Works offline through a deterministic analyst engine, while remaining API-ready for future LLM integration
- Upload operational CSV data
- View employee, department, task, and status metrics
- Track completion rate, pending work, active work, and average hours
- Generate management-style AI insights from operational records
- Export operational summaries to Excel
- ASP.NET Core MVC architecture
- Identity authentication
- Admin, Manager, and Employee roles
- SQL Server persistence for asset workflows
- Responsive enterprise-style UI
- Animated navigation shell with active states and accessibility-friendly reduced-motion support
- Unit tests for the asset intelligence decision logic
OpsIntel AI is a practical internal-ops platform for teams that still manage assets, task updates, and follow-ups through scattered spreadsheets or chat messages. It gives leaders a live view of what is available, what is delayed, what needs attention, and what action should happen next.
| Layer | Technology |
|---|---|
| Backend | ASP.NET Core MVC, C# |
| Database | SQL Server / LocalDB |
| Auth | ASP.NET Core Identity |
| Frontend | Bootstrap, Chart.js, custom CSS |
| Export | ClosedXML, CSV |
| AI Layer | Rule-based demo analyst, AI API-ready service layer |
- .NET 8 SDK
- SQL Server LocalDB or SQL Server
dotnet ef database update
dotnet runVisit http://localhost:5150.
Demo admin:
Email: admin@system.com
Password: Admin@123
This project demonstrates AI product thinking, backend engineering, role-based workflows, dashboard UX, data analysis, export pipelines, testable decision logic, and an AI-ready operations layer. It was designed as a recruiter-facing proof that a fresher can take an ambiguous business problem and turn it into a polished working product.
dotnet testThe test project currently covers the asset intelligence engine and AI analyst console: overdue detection, low-stock risk, department load analysis, natural-language analyst responses, combined workforce/asset risk signals, and department-attention queries.