HazNetix
HazNetix is developing a pre-production hazard intelligence workflow for fire service and wildland-urban interface (WUI) response, beginning with an operator-reviewed Fire 360 incident size-up. The platform aims to provide simulated, structured size-up data to support situational awareness and decision-making during fire incidents. The system is designed to be reviewed and validated by operational fire personnel to ensure practical relevance.
- Artificial Intelligence
- Software Only
Funding
Founders
Product
Problem
Fire service and wildland-urban interface (WUI) responders often lack a structured, pre-incident hazard intelligence workflow that integrates a full 360-degree size-up of the fire environment. This can lead to incomplete situational awareness, delayed operational decisions, and increased risk to personnel and communities during rapidly evolving incidents.
Solution
HazNetix is building a pre-production, operator-reviewed hazard intelligence workflow that begins with a simulated Fire 360 size-up. The platform provides a structured approach to capturing, organizing, and reviewing incident size-up data so responders can assess hazards and conditions more systematically. The workflow is designed to be iteratively improved with direct input from operational fire personnel, ensuring the output aligns with real-world needs and mental models. By focusing on the size-up phase, HazNetix supports earlier and more informed operational decision-making during WUI and structural fire responses.
Target Audience
Primary customers are fire service agencies and wildland-urban interface response teams seeking a structured, operator-reviewed workflow for incident size-up and hazard intelligence.
Features
- Structured Fire 360 size-up workflow tailored for fire service and WUI response
- Pre-production system designed for iterative operator review and feedback integration
- Simulated incident data environment for testing and refining hazard intelligence processes
- Focus on early-phase situational awareness and hazard identification in complex fire environments