CivicFlow AI

CivicFlow AI is an AI-powered predictive queue and bottleneck management system designed for government service centres. Traditional queue systems mainly show officials what is happening at the present moment, but they often provide limited support for anticipating future congestion or understanding the impact of an intervention. CivicFlow AI addresses this challenge through a complete operational decision-support pipeline: Observe → Predict → Detect → Explain → Simulate → Recommend → Approve → Intervene → Monitor. The platform collects live operational information such as queue length, arrival behaviour, processing time and staff availability. A Random Forest Regression model predicts future queue pressure for the next 30 and 60 minutes. The system then identifies the current operational bottleneck and performs root-cause analysis using factors such as demand, processing time and staffing. Before taking an operational action, officials can use the What-If Simulation to compare scenarios such as adding an officer, moving an officer, routing simple cases online, or taking no action. Based on the analysis, CivicFlow generates an AI-supported recommendation. The final decision remains with the authorised official, keeping a human in the loop. After an intervention, the system continues monitoring actual queue and waiting-time changes rather than assuming that the intervention was successful. Historical queue snapshots and intervention data are stored to support continuous monitoring and analytics. For our demonstration, CivicFlow AI is applied to a Regional Passport Seva Kendra scenario. The system is designed to help officials move from reactive queue management toward predictive, explainable and measurable operational decision-making.