Data Capture
Collect inspection data from ROV, UAV, or sensor platforms
Pushing the boundaries of artificial intelligence through cutting-edge research, industry partnerships, and continuous innovation.
Our research initiatives drive the future of AI in Norwegian energy and maritime sectors, focusing on practical applications that solve real-world challenges in oil & gas, subsea operations, and aquaculture.
We collaborate with leading universities, industry partners, and research institutions to ensure our solutions incorporate the latest scientific breakthroughs tailored for Norway's strategic industries.
in Survey & Inspection
Collect inspection data from ROV, UAV, or sensor platforms
The foundation of AI-powered inspection starts with high-quality data collection. This phase captures real-time video, sensor data, and metadata from the inspection platform during field operations.
Validate data integrity and completeness
Quality assurance ensures that captured data meets inspection standards before analysis. This critical step validates data completeness, accuracy, and usability for AI processing.
AI + Human verification for accurate detection
Combines artificial intelligence with human expertise to identify anomalies, defects, and critical findings. AI models perform rapid initial detection, while expert review ensures accuracy and actionable insights.
Model training & refinement from validated data
Leverages validated inspection data to continuously improve AI model accuracy. Expert-verified findings become training data, creating a feedback loop that enhances detection capabilities over time.
Real-time deployment on edge devices
Deploys optimized AI models to production environments for real-time inspection and monitoring. Enables live detection during field operations, providing immediate insights and alerts to operators.
Real-time AI-powered streaming analysis for offshore platform inspections via fiber optic transmission. Our advanced transfer learning models detect surface installation defects including corrosion, structural cracks, coating degradation, and mechanical damage with 90%+ accuracy. The system processes 15-30 frames per second from platform-mounted cameras, delivering instant alerts for critical issues through temporal tracking that reduces false positives by 60%. Operators receive real-time defect overlays, confidence scores, and maintenance recommendations during live inspections, dramatically reducing vessel time and enabling immediate operational decisions. Applications include topside equipment monitoring, flare tower inspections, helideck structural assessment, and safety barrier verification across Norwegian Continental Shelf installations.
Pioneering subsea inspection through real-time ROV/AUV video streaming with AI-powered defect detection. Our transfer learning framework analyzes live underwater video transmitted via fiber optic cables, identifying pipeline defects (corrosion, cracks, weld anomalies, biofouling), subsea equipment degradation, and structural integrity issues in real-time. The system integrates YOLOv8 models fine-tuned on subsea datasets with multi-sensor fusion (visual, LiDAR, sonar) for comprehensive underwater asset monitoring. Frame-by-frame inference delivers <500ms latency, enabling operators to make immediate decisions during inspection operations. Advanced temporal tracking validates defects across multiple frames, dramatically improving accuracy and reducing post-inspection analysis time from 24-48 hours to real-time. Our offline training pipeline supports continuous model improvement using customer-specific data, adapting detection capabilities to unique field conditions and asset types across deepwater installations.
Advanced computer vision and passive sonar analysis for comprehensive aquaculture monitoring. Our multi-sensory platform integrates underwater 8K camera arrays with hydroacoustic networks (10Hz-100kHz) for real-time feeding behavior recognition (92% accuracy), stress detection through acoustic signatures, and species identification. Advanced sonar systems distinguish farmed from wild salmon using swim bladder resonance patterns and behavioral fingerprinting with 95% accuracy. The system processes 10TB daily per facility through edge computing (NVIDIA Jetson Orin) and central AI clusters, providing behavioral analysis, escape risk prediction, and automated visual documentation. Applications include fish health monitoring through thermal imaging, feeding optimization via acoustic analysis, and escape prevention using behavioral conditioning. Our image and sonar-based AquaGuard platform achieved 99.5% escape prevention rates in pilot deployments across Norwegian salmon farms.
Computer vision and machine learning for autonomous underwater vehicles, enabling intelligent subsea infrastructure inspection and environmental monitoring at depth.
Active partnerships with Norwegian universities including UiO, NTNU, and UiB for joint research projects and knowledge exchange.