Public safety & emergency response

The problem

Wildfires, floods, earthquakes, and search and rescue operations all share the same challenge: too much data from too many sources, and not enough time to make sense of it. Drones, ground sensors, thermal cameras, and satellite feeds generate massive volumes of imagery and telemetry. First responders and coordinators need processed intelligence, not raw data.

What we bring

• Multi-endpoint data processing — Backend systems that ingest data from many sensors and endpoints simultaneously, process it with ML (fire detection, person detection, terrain mapping), and deliver actionable results.
• Real-time map building — Image stitching and sensor fusion from multiple aerial and ground platforms into coherent, up-to-date maps of the operational area.
• HPC compute — Slurm-backed processing for heavy workloads: large-scale image analysis, model inference, and historical data analytics that can't run on laptops in the field.
• AI-assisted decision support — RAG systems and ML-driven analytics that surface relevant information from operational documents, past incidents, and live data streams.
• Resilient operation — Endpoints that continue operating and collecting data during comms loss, syncing with the backend when connectivity returns.

Applications

• Wildfire detection and monitoring — thermal and visual data from aerial endpoints, processed for fire front mapping and spread prediction
• Search and rescue — coordinated aerial search patterns with real-time image analysis for person detection
• Disaster response — rapid situational awareness from multiple sensor platforms during floods, earthquakes, and severe weather
• Law enforcement — aerial surveillance coordination and data processing for large-area operations
• Infrastructure damage assessment — post-disaster inspection using coordinated endpoint surveys with ML-based damage classification