Funding
Funding not disclosed
Founders
Product
Problem
Wildfire detection and real-time spread prediction rely on delayed satellite passes and manual observation, leaving first responders with incomplete intelligence that hampers timely decision‑making and increases damage.
Solution
Fireflyt delivers a two‑tier intelligence platform that combines AI‑driven predictive modeling with autonomous drone surveillance to provide continuous situational awareness from ignition onward. The software ingests live NASA FIRMS satellite alerts and environmental data via Google Earth Engine to generate proximity risk assessments, ignition probability scores, and cellular‑automata fire‑spread simulations. When risk thresholds are crossed, the system autonomously dispatches drones equipped with HD, LiDAR, and thermal sensors to the hotspot, navigating and relaying multi‑spectral video without human piloting. The resulting data stream and tactical analytics—such as closest point of contact and optimal containment windows—are presented to fire departments before ground crews arrive, enabling faster, data‑informed response actions.
Target Audience
Primary customers are municipal fire departments, regional wildfire response agencies, and emergency management teams that require early detection and real‑time intelligence for wildfire incidents.
Features
- Live ingestion of NASA FIRMS alerts combined with terrain and environmental inputs for real‑time ignition risk scoring
- AI/ML models that predict fire spread using configurable wind, dryness, and fuel parameters via cellular‑automata simulation
- Autonomous drone fleet with obstacle avoidance, HD video, LiDAR, and thermal imaging for continuous multi‑spectrum monitoring
- End‑to‑end data relay from drone to command center, delivering live video and analytics without manual piloting
- Tactical analytics dashboard showing closest point of contact, least‑spread windows, and active vs. burned zone mapping
- Integration with fire department workflows to provide alerts and actionable insights prior to any ground deployment