RAVAM provides an autonomous AI‑driven drone platform for continuous monitoring of large‑scale infrastructure such as pipelines, solar farms, and construction sites. The system combines multi‑sensor payloads, edge processing, and a cloud‑based command center to fuse data, detect anomalies with over 95% accuracy, and deliver predictive maintenance insights that reduce downtime and operational costs.
Funding
Funding not disclosed
Founders
Product
Problem
Many critical infrastructure assets such as pipelines, solar farms, and construction sites require frequent inspections, but traditional manual surveys are labor‑intensive, costly, and often miss early signs of failure, leading to unplanned downtime and safety incidents.
Solution
RAVAM delivers an autonomous, AI‑driven drone platform that continuously monitors assets without human operators on site. Integrated multi‑sensor payloads (thermal, RGB, night‑vision, LiDAR, magnetometer, GPR, etc.) capture surface and subsurface data, which is streamed to a cloud‑based Command Center. There, a multi‑sensor fusion engine and machine‑learning models detect anomalies, predict degradation, and generate actionable reports in real time. The system operates 24/7 from an autonomous docking “Nest” that handles charging, communication, and weather monitoring, enabling turnkey deployment and ongoing surveillance with zero on‑site staff.
Target Audience
Primary customers are operators of large‑scale infrastructure—energy pipelines, solar and wind farms, utilities, construction and mining sites—who need continuous, high‑precision monitoring and predictive maintenance.
Features
- Multi‑rotor and VTOL drone variants with interchangeable sensor modules for surface, subsurface, and rapid‑response missions
- Autonomous Nest stations providing self‑charging, RTK‑base positioning, and direct drone communication without internet
- Cloud Command Center for fleet management, waypoint scheduling, live video (up to 32 streams), and remote control via browser
- AI analytics engine that fuses 12+ sensor streams, achieving >95 % detection accuracy and sub‑5‑second alerting
- Predictive maintenance models that forecast failures weeks in advance and produce automated GIS‑compatible reports
- Photogrammetry pipeline (RAVAM Mapper) delivering orthomosaics, 3D point clouds, DEMs, and survey‑grade outputs in standard formats
- Edge‑on‑drone processing for immediate safety actions and cloud‑scale analysis for trend detection and cross‑site learning