A metro area emergency management agency coordinating police, fire, and EMS across 8 jurisdictions had average response times of 11.2 minutes, above the national benchmark of 7 minutes. Dispatchers managed 2,500 daily calls with manual unit assignment based on radio communication.

Multi-agency incidents required phone coordination between separate dispatch centers, adding 4-8 minutes to coordinated responses. Resource tracking was updated manually on whiteboards. Situation reports were compiled manually, reaching commanders 30-45 minutes after incident onset.

The agency needed automated dispatch optimization, unified multi-agency coordination, and real-time situational awareness.

The Solution

Agents that detect incidents, dispatch units, coordinate agencies, track resources, and brief commanders

Vijan.AI deployed 5 agents. The Detection agent ingests and correlates 911 calls, sensor data, and social media signals to identify and classify incidents. The Dispatcher agent assigns nearest appropriate units using CAD integration and real-time GIS positioning. The Coordination agent manages multi-agency response, ensuring all jurisdictions are notified and resources don't duplicate. The Resource agent tracks equipment, personnel, and vehicle availability in real-time. The Situation Reporter generates live command briefings with incident status, resource deployment, and recommended actions.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Emergency

Emergency Response Agent

Rapid Emergency Dispatch

Coordinates emergency response by receiving 911 calls, dispatching appropriate units, and managing incident command, autonomously prioritizing calls and optimizing resource deployment in prevention phase before transitioning to response.

Input

Emergency calls with location, incident type, and severity classification

Output

Dispatched units with optimized routing and incident status updates

Calls computer-aided dispatch system to log incidents, determine unit availability, and assign based on proximity and capability
Invokes AVL tracker to locate nearest police, fire, or EMS units and calculate optimal response routes with traffic data
Autonomous decision: dispatch single unit, call mutual aid, or escalate to multi-agency response for major incidents
Transitions from prevention monitoring to active response phase, coordinating with Infrastructure Monitor for situational awareness

Step 2 · Monitoring

Infrastructure Monitoring Agent

Predictive Infrastructure Monitoring

Monitors critical infrastructure including traffic signals, water systems, and public facilities using IoT sensors, autonomously detecting failures and routing maintenance requests while supporting emergency response with situational intelligence.

Input

Sensor data from traffic, utilities, and public facilities

Output

Predictive maintenance alerts and infrastructure status reports

Queries IoT sensor network for anomalies in traffic flow, water pressure, power outages, and facility access systems
Calls asset database to retrieve maintenance history, warranty status, and criticality ratings for prioritization
Autonomous decision: dispatch maintenance crews, issue public advisories, or escalate critical failures to emergency response
Provides infrastructure status data to Emergency Response and Process Automation agents in both prevention and response phases

Step 3 · Automation

Process Automation Agent

Safety Workflow Automation

Automates routine public safety workflows such as permit approvals, inspection scheduling, and report generation, autonomously freeing personnel for critical response activities and coordinating with notification systems.

Input

Routine requests for permits, inspections, and administrative processes

Output

Automated approvals and scheduled workflows with exception handling

Invokes workflow engine to process fire safety permits, event security plans, and inspection requests without manual intervention
Executes RPA tool to auto-generate reports, update case management systems, and distribute documents across departments
Autonomous decision: auto-approve compliant requests, route exceptions for review, or schedule follow-up actions
Coordinates with Multi-Channel Notifier to communicate approvals and transitions to response phase for time-sensitive alerts

Step 4 · Notification

Multi-Channel Notifier

Mass Emergency Notification

Broadcasts emergency alerts and public safety information across multiple channels including SMS, email, social media, and reverse 911, autonomously segmenting audiences and timing messages for maximum reach in response phase.

Input

Emergency alerts with severity levels and geographic targeting criteria

Output

Delivered notifications with confirmation rates and reach metrics

Calls emergency alert system to trigger geo-targeted warnings for severe weather, public safety threats, or evacuation orders
Invokes SMS gateway to send wireless emergency alerts compliant with IPAWS standards for mobile device penetration
Autonomous decision: activate all channels for imminent threats, use selective channels for advisories, or cancel false alarms
Triggers citizen feedback collection immediately post-incident to assess notification effectiveness and refine targeting

Step 5 · Feedback

Feedback Collection Agent

Community Safety Feedback

Collects citizen feedback on emergency response quality, public safety concerns, and infrastructure needs, autonomously analyzing sentiment and feeding insights back to improve prevention and response capabilities.

Input

Citizen feedback from surveys, social media, and direct communications

Output

Analyzed feedback with actionable improvement recommendations

Invokes survey platform to deploy post-incident questionnaires measuring response time satisfaction and communication effectiveness
Calls sentiment analysis tool to monitor social media for public safety concerns and trending community issues
Autonomous decision: escalate urgent safety concerns, aggregate for policy review, or acknowledge positive feedback publicly
Feeds response analytics back to Emergency Dispatcher and Infrastructure Monitor to refine prevention strategies and response protocols

Results

Measurable impact within 90 days of deployment

50%

Faster Response

Average response time reduced from 11.2 to 5.6 minutes. Critical incident response under 4 minutes.

Zero

Coordination Delays

Multi-agency coordination is automatic. Cross-jurisdiction response time reduced from 15 to 6 minutes.

< 2min

Situation Reports

Commanders receive live situation reports within 2 minutes of incident detection, down from 30-45 minutes.

18%

Better Outcomes

Emergency outcome severity reduced 18% through faster response and better resource allocation.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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