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A confidence score is a number that represents how certain an AI system is about a particular prediction or classification. It is not a guarantee that the result is correct.
Damage detection is the use of image analysis or other sensors to locate and classify visible vehicle damage such as dents, scratches, cracks, and broken components.
Deep learning is a type of machine learning that uses neural networks with many processing layers to learn complex patterns from large amounts of data.
Explainable AI refers to methods that help people understand why an artificial intelligence system produced a particular result or recommendation.
Human-in-the-loop describes a process in which a person reviews, corrects, approves, or otherwise participates in decisions made or supported by an automated system.
Image segmentation is a computer-vision technique that assigns image pixels to specific objects or regions so their precise shapes and boundaries can be analyzed.
Inference is the stage when a trained AI model processes new information and produces a prediction, classification, recommendation, or generated response.
Machine learning is a type of artificial intelligence in which a computer model learns patterns from examples or data instead of relying only on rules written explicitly by a programmer.
Inspektlabs provides AI-assisted vehicle inspections using photographs and 360-degree video. Its technology evaluates image quality, identifies visible damage, and generates structured inspection information for review. The platform serves insurance, rental, fleet, remarketing, and repair-related applications. Its workflows can also help organizations review claims and check submitted visual evidence for possible inconsistencies.