Implemented an Azure-native AI platform to automate aerial inspections using video analytics, reducing manual effort, accelerating fault detection, and improving operational efficiency.
Video story · 1:04
The challenge
The organisation relied on complex aerial inspections using helicopters, generating terabytes of video data per cycle. Manual analysis of hundreds of hours of footage was time-consuming, resource-intensive, and prone to delays in identifying faults. The growing volume of inspection data made it increasingly difficult to scale operations and maintain timely and accurate analysis.
The approach
Designed and implemented a cloud-based solution leveraging Microsoft Azure, incorporating a Data Lake for large-scale data management and computer vision for automated video analysis. The platform enabled automated ingestion and processing of video data, combined with audio-to-text capabilities and machine learning models for enhanced interpretation. Interactive dashboards provided real-time insights and visualisation, enabling faster decision-making and improved operational oversight.
The outcome
Delivered a highly efficient inspection platform that reduced manual analysis effort by hundreds of hours per inspector and decreased total inspection time by over 95%. The solution enabled rapid fault identification through advanced analytics, improving responsiveness and operational reliability. A scalable architecture was established to support future expansion, ensuring long-term sustainability and continuous improvement in inspection processes.
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