In this Intel on AI podcast episode: When making car insurance claims it can take a lot of time to have a claims adjuster inspect the damage to your car and then get the estimate from a body shop reviewed and approved by your insurance company. This process is costly and complicated for both the insurance company and consumers and can be a pain point for all parties involved. Vladimir Starostenkov, a Machine Learning Architect at Altoros, joins the Intel on AI podcast to discuss how the Altoros Car Parts Identification Solution allows users to upload photos of damaged vehicle on location and uses a machine learning (ML) algorithm to assess the vehicle body to provide a real-time estimate on the damage. He points out that this solution can not only help consumers have a better experience when assessing car damage, but that it can save insurance companies, body shops, and consumers an incredible amount of time and money. Vladimir also describes another solution that Altoros provides that automates discovery and derivation of information from PDF documents using techniques like PDF parsing and natural language processing. He highlights how Altoros has worked with Intel to help optimize their solutions using the Intel distribution of OpenVINO toolkit to provide greater value and performance to their customers.

To learn more, visit:
altoros.com
cardamage.altoros.com

Visit Intel AI Builders at:
builders.intel.com/ai

Tags: AI, Altoros, Altoros Car Parts Identification Solution, artificial intelligence, Car Insurance, deep learning, machine learning, ML, natural language processing, PDF Mining, Supercomputing, Vladimir Starostenkov, Xeon Processors
 
Posted in: Artificial Intelligence, Audio Podcast, Intel, Intel on AI