Create realistic training scenarios with AI-generated synthetic data. Simulate entities, incidents, and operational events across 20+ scenario types — perfect for exercises, system testing, and AI model training.
The Synthetic Data Generator creates lifelike training data that mirrors real-world operations. Generate entities with realistic movement patterns, simulate incident lifecycles, and create comprehensive scenarios for training and testing.
A SAR team prepares for a missing person exercise. Instead of manually creating markers and routes, they use the Synthetic Data Generator to create 50 search team members with realistic GPS tracks, 10 mock witnesses providing false leads, and a simulated missing person moving through difficult terrain. The entire scenario is generated in seconds.
Choose from over 20 pre-configured scenario templates covering military, emergency response, law enforcement, and civilian operations. Each scenario includes appropriate entity types, behaviors, and incident patterns.
| Category | Scenario Types | Key Entities |
|---|---|---|
| Law Enforcement | Police Patrol, Active Shooter, SWAT Operation, Traffic Stop, Pursuit | Officers, Suspects, Vehicles, Civilians |
| Fire Services | Structure Fire, Wildfire, HAZMAT, Rescue, Multi-Casualty | Fire Units, Incident Command, Victims, Hazards |
| Search & Rescue | Missing Person, Wilderness SAR, Urban SAR, Water Rescue, Avalanche | Search Teams, Subjects, Witnesses, Helicopters |
| Emergency Management | FEMA Disaster, Mass Evacuation, Shelter Operations, Distribution | Responders, Evacuees, Resources, Facilities |
| Border Security | CBP Patrol, Interdiction, Surveillance, Tracking | Agents, Subjects, Vehicles, Sensors |
| Intelligence | FBI Surveillance, Counter-Terror, Cyber Response | Agents, Targets, Assets, Events |
| Conservation | Wildlife Tracking, Anti-Poaching, Habitat Survey | Rangers, Animals, Poachers, Drones |
Generate synthetic entities with realistic attributes and behaviors. Each entity type has appropriate properties, movement patterns, and interaction capabilities.
Entities follow configurable movement patterns that simulate realistic behavior. From foot patrols to vehicle convoys to aircraft orbits, each pattern creates believable GPS tracks.
| Pattern Type | Description | Use Case |
|---|---|---|
| Waypoint Route | Follow defined path through waypoints | Patrols, convoys, search patterns |
| Random Walk | Semi-random movement in area | Crowds, wildlife, wandering subjects |
| Area Patrol | Systematic coverage of polygon | Security patrols, grid searches |
| Orbit | Circular pattern around point | Aircraft surveillance, drones |
| Pursuit/Evasion | One entity chases or avoids another | Pursuits, evasion training |
| Stationary | Fixed position with minor drift | Checkpoints, observation posts |
Simulate the complete lifecycle of incidents from detection through resolution. Create time-based scenarios that unfold realistically over the exercise duration.
Control the pace of scenario execution for different training needs. Run at real-time for realistic exercises, or accelerate for rapid testing and iteration.
A developer needs to test the CoT messaging system under load. They configure a scenario with 200 entities moving simultaneously, then run it at 10x speed. The system processes thousands of position updates in minutes, revealing a bottleneck in the message queue handling.
Generated scenarios can be exported for reuse, sharing between teams, or integration with other systems. Support for multiple formats ensures compatibility with external tools.
Beyond exercises, synthetic data supports machine learning model development. Generate labeled training data for object detection, trajectory prediction, and anomaly detection algorithms.