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Solera AI Sets The Global Standard Now With 40% Faster Image Damage Detection And Unrivalled Accuracy

New Qapter Release Advances Machine Learning and Boosts User Experience

Solera, a global leader in data, applications and services for insurance and automotive, has released a ground-breaking update to its Artificial Intelligence (AI)-powered, end-to-end automotive claims platform, Qapter. The release is driven by a new approach to Machine Learning, that delivers 40% faster damage detection (in under 2 seconds) and a more intuitive and easy-to-use system, for a faster, accurate car repair estimate.

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“Our continuous investment into core Machine Learning capabilities is accelerating the pace of evolution of Qapter,” said Evan Davies, Chief Technology Officer, Solera. “With the continued innovations our team has developed, we can provide a full, detailed repair cost estimate in less time.”

Announced in June of 2020, Qapter is the industry’s only globally available end-to-end solution for full digitalization of the modern claims workflow. The platform’s integrated approach which brings together data science and extensive Repair Science provides superior accuracy and performance delivering maximum benefits in digitizing the claims process.

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Smart AI powering faster estimates

In addition to an unbeatable speed in vehicle damage detection, the updated Qapter solution now provides the user with real-time image quality feedback. Dark, blurred, or over exposed images will be highlighted so better images may be taken. This intuitive user-friendly experience combined with the powerful Machine Learning in Qapter’s AI means that fewer images are required to deliver a better, faster estimate.

Improved accuracy

The solution also brings higher accuracy for parts and damage detection, with an improved damage localiser. The AI can identify and itemize damages of the same type in the same area, for example, multiple scratches on a front bumper. It can also better detect damage when reflections are present, which can disguise damage and compromise the estimate.

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