MAGNI offers an on‑demand drone surveillance platform that autonomously plans, executes, and processes multi‑sensor aerial missions to capture high‑resolution RGB, multispectral, thermal, and LiDAR data. Its cloud‑native AI analytics convert the imagery into change‑detection alerts and GIS‑compatible layers, delivered through dashboards and APIs for continuous monitoring of environmental, infrastructure, and security risks while reducing carbon emissions.
Funding
Funding not disclosed
Founders
Product
Problem
Monitoring large territories for environmental degradation, infrastructure risk, or illegal activity requires frequent, high‑resolution aerial data. Conventional manned aircraft or satellite imagery are either too costly, have limited revisit times, or generate a sizable carbon footprint, leaving authorities with delayed or incomplete situational awareness.
Solution
MAGNI delivers a turnkey drone surveillance platform that plans, executes, and processes aerial missions on demand. An integrated fleet management system autonomously schedules flights, deploys multi‑sensor payloads (RGB, multispectral, thermal, LiDAR), and streams raw geospatial data to a secure cloud. Proprietary analytics pipelines apply AI‑based change detection and risk modeling to transform imagery into actionable alerts and GIS‑ready layers. Clients receive web‑based dashboards and API endpoints for real‑time monitoring, reporting, and integration with existing asset‑management tools. The service is offered at scale with a reduced carbon footprint compared to traditional aerial surveys.
Target Audience
Primary customers are public agencies (environmental, urban planning, emergency management), infrastructure operators (energy, transportation, utilities), and private land managers in agriculture, forestry, and mining who require frequent, high‑precision aerial intelligence.
Features
- Automated flight‑planning engine that optimizes routes for coverage, battery life, and regulatory compliance
- Modular sensor suite supporting high‑resolution RGB, multispectral, thermal, and LiDAR data capture
- Edge‑computing on the UAV for preliminary orthomosaic stitching and quality checks before upload
- Cloud‑native analytics stack with deep‑learning models for anomaly detection, vegetation health indexing, and structural deformation monitoring
- GIS‑compatible output (GeoTIFF, shapefiles, WMS/WFS services) and RESTful API for seamless integration with enterprise systems
- Role‑based access control and end‑to‑end encryption to meet data‑privacy and security standards
- Subscription‑based monitoring dashboards with customizable alert thresholds and historical trend visualizations
- Carbon‑offset reporting that quantifies emissions saved versus conventional aerial methods