This multinational oil and gas company is among the world’s largest companies out of any industry. This $381 billion company has over 86,000 employees worldwide and a presence in more than 70 countries.

CHALLENGE

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Pipeline inspection processes were heavily reliant on manual review of drone imagery, creating significant delays in identifying critical asset risks.

Large volumes of visual data needed to be analyzed by human inspectors, making the process slow, inconsistent, and difficult to scale across vast infrastructure networks.

As a result, maintenance teams struggled to respond in time to emerging issues, increasing the risk of undetected corrosion and asset degradation.

• Time-intensive image review cycles delaying decision-making
• Limited responsiveness to emerging asset risks
• High potential for human error and missed defects
• Inability to scale inspection efforts across growing pipeline networks

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SOLUTON

Aligned Automation developed an AI-powered application that leverages computer vision to analyze drone imagery at scale and in near real time.

The solution automatically processes and interprets visual data to detect and classify signs of corrosion, structural anomalies, and overall asset health conditions.

By transforming raw drone footage into actionable intelligence, the system enables early warning triggers that support proactive, preventive maintenance strategies.

Key capabilities include:

• Automated analysis of drone-captured imagery using computer vision
• Detection and classification of corrosion and asset anomalies
• Real-time alerts and early warning triggers for potential failures
• Scalable inspection coverage across large and complex pipeline networks
• Data-driven insights to support predictive and preventive maintenance

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What Changed

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Inspection processes shifted from reactive, manual review to proactive, intelligence-driven monitoring.

Maintenance teams can now identify risks earlier, prioritize interventions more effectively, and reduce the likelihood of costly failures or downtime.