SemanticNoManageProj

The system directly tackles the issue of incomplete or confusing timesheet data affecting the project plan by making timesheet data central to the analysis: • Timesheet Data Input is mandatory, requiring employee work logs detailing employee names, dates worked, tasks completed, hours logged, and status updates. • The AI Analysis uses this input to provide detailed visibility into Resource utilization per employee. This helps project management figure out exactly where the team’s time is going. • Tool includes Custom risk detection and Anomaly detection in timesheet data. feature identifies issues like burnout, overwork, unusual hours, or context switching. By catching these anomalies early, project managers can address human resource risks before they lead to major delays. Taking Action on Risks and Delays For project management finding it hard to take effective action against risks and delays, the system uses historical parallels to inform the response: 1. Similarity Search: When project data (including the Project Plan and timesheets) is entered, the system converts it into semantic vector embeddings that capture project complexity and dependencies. It then uses vector similarity search (simulating Qdrant) to find the top 3 most similar historical projects from a persistent knowledge base. 2. Predictive Risk Identification: The Historical Insights Dashboard shows Predicted Risks. These predictions are based specifically on what went wrong in those similar past projects. This allows management to see potential risks before they materialize in the current project, such as recognizing common challenges that similar projects faced. 3. Context-Aware Recommendations: The analysis provides Actionable recommendations that are Context-Aware. By automatically storing every analyzed project, the system continuously builds a knowledge base that improves over time, making its predictive accuracy and actionable recommendations increasingly powerful for future risk mitigation.