#404 - David Hallac - CEO, Viaduct

“We're building a large scale knowledge graph across millions upon millions of potential signals and sensors across time, across hundreds of thousands of vehicles. What we developed first at Stanford and and more recently at Viaduct is a method for learning from these signals, the first order, second order, third order relationships in a very computationally efficient manner.”

Vehicle quality issues that lead to recalls and lawsuits cost automotive OEMs tens of billions of dollars in cost and lost revenue each year. Given the explosion of connected vehicle data, one might expect that this data could be leveraged to reduce this cost. Things are rarely that straightforward. Why is that?

I invited David Hallac, CEO of Viaduct to the AI in Automotive Podcast to find out more. David’s 5-year old startup finds patterns and relationships amongst billions of connected vehicle data points, and delivers two powerful, commercially sound use cases to automotive OEMs. One, it helps automotive OEMs proactively identify and address quality issues, saving hundreds of millions of dollars in warranty costs and recalls. Two, it helps predict failures, call vehicles in for proactive maintenance, and helps bump up up-time - a god-send, especially for fleet customers. 

The big penny drop moment for me during my conversation with David was that connected vehicle applications don’t have to be bold, visible and sexy, delivering massive incremental revenue at near 100% margin. In fact, the connected vehicle applications most likely to succeed in the near-term are those that deliver commercial value today, often by way of substantially reduced costs. Viaduct’s quality management and maintenance prediction use cases check those boxes, and how. Listen to my chat with David to find out more.

If you enjoyed my chit-chat with David Hallac, please give the AI in Automotive Podcast a solid five stars on Apple Podcasts and Spotify - I am always thankful for your support.

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