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The Client required a specialised team to identify and assess damage of civil structures with a high accuracy of at least 90%.
SUPA’s engineering experts collaborated closely with the Client by co-creating the annotation workflow and assembling a team of annotators experienced with engineering-related projects.
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SUPA’s team of 25 annotators successfully delivered the annotations with a consistent >90% accuracy.
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The Problem
The Client is an integrated Oil & gas company looking to build an automated damage classification model that could:
The Client does not have an internal labeling team. Additionally, their previous 3rd party vendor struggled with generating annotations with a minimum accuracy of 90%.
The Solution
SUPA recognized the importance of understanding the task’s engineering context and the open-endedness associated with assessing structural damage. The team:
The Result
SUPA successfully delivered all the data required by the Client, completing over 22,500 annotations at a consistent >90% accuracy.
This enabled the Client to enhance their classification model, enabling a faster, safer, and more accurate inspection process.
Why SUPA?
SUPA’s engineering experts go beyond generic annotation work. They understand the underlying context of the task, working closely with the Client from Day 1 to build a tailored work process to successfully deliver the annotation project.
SUPA's labeling infrastructure helped Greyparrot.ai, a global leader in AI waste intelligence, expand to 89 categories
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SUPA scales high-quality annotation output during seasonal data surges by 170% for a global agritech company that manages over 200 million trees
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