ScavAI redefines scavenge inspection reporting by combining computer vision with automated defect intelligence. By transforming raw inspection imagery into structured, actionable reports, ScavAI enables faster diagnostics, more consistent defect documentation, and proactive engine condition monitoring across the fleet. With 7,800+ automated inspection reports generated, ScavAI has demonstrated practical value in supporting routine scavenge inspections and engine condition monitoring.
ScavAI enables engineers to convert scavenge inspection images into structured technical reports through automated visual analysis. After inspection photographs are uploaded, the system evaluates internal engine conditions and classifies visible anomalies across predefined defect categories. By providing immediate visual diagnostics, ScavAI reduces reliance on manual interpretation and helps inspection teams quickly identify early indicators such as abnormal deposits, oil residue buildup, or corrosion patterns that may signal developing engine issues leading to a consistent defect reporting cycle.
Automatically analyzes uploaded scavenge inspection images to detect and classify up to nine common defect indicators, including corrosion, rust formation, carbon deposits, oil residue, and abnormal combustion patterns.
Instantly converts analyzed inspection images into structured technical reports, significantly reducing manual documentation and accelerating the inspection reporting process.
Identifies visual anomalies such as excessive carbon buildup, oil carryover, and corrosion patterns that may indicate incomplete combustion, liner wear, or lubrication imbalance.
Provides immediate visual diagnostics, allowing engineers to escalate only confirmed anomalies for further laboratory analysis when necessary.
Ensures consistent defect classification and reporting across vessels, enabling easier comparison of engine condition and inspection outcomes fleet-wide.