Drone Inspection and Monitoring System
Implemented a sophisticated microservices architecture with real-time drone and robot monitoring, advanced analytics for inspection data, and a dual-interface design optimized for both mobile and desktop environments.
Capabilities
- Microservices architecture (Docker, Kubernetes) for horizontal scalability
- Low-latency, real-time streaming of 4K/thermal/LiDAR feeds
- AI-driven anomaly detection & automated measurements
- Unified React-based web dashboard & field-optimized mobile app
- Configurable, multi-channel alert engine (SMS, email, in-app)
Technologies
- Kubernetes & Docker
- Python (TensorFlow, OpenCV)
- Kafka for streaming
- React & React Native
- PostgreSQL & S3 storage
Team
- 5 Software Engineers
- 3 Testers
- 1 Product Owner
Outcome Achieved
- 35% reduction in inspection cycle time
- 50% faster defect detection
- Zero critical defects missed in pilot
- 95% system uptime
- ~95% ROI in year 1
Value Delivered
Smarter, Safer, Cheaper: AI-Driven Inspections at Scale
Delivered a turnkey solution that eliminated dangerous manual inspections; reduced inspection times by ~35% while AI analytics uncovered ~48% more issues; centralized data lake enabled predictive maintenance; automated audit trails ensured compliance; clients saw 20–25% cost savings.
Customer
SkyTech Inspections
SkyTech Inspections is a Vietnam-based aerospace & infrastructure tech provider operating 200+ autonomous drones across sectors (airports, bridges, energy, agri-tech). They process ~10 TB of inspection data monthly and needed a real-time, data-rich platform to replace slow, hazardous manual inspections.
Key Highlights
Decoupled ingestion, processing & analytics into independent, scalable services.
<1s latency video streams for rapid decision-making.
ML models flag defects and measure anomalies automatically.
Dual UIs: streamlined mobile for field & full web for HQ.
Dynamic threshold alerts via SMS, email & in-app notifications.
Customer Testimonial
“Our inspection accuracy and safety have never been better—we spotted issues we’d previously miss.”
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