Automotive OEMs face billions in costs from repeat repairs that frustrate customers and erode brand loyalty. Learn how AI-powered analytics can tackle the root causes of these issues, transforming quality management with proactive detection, root cause analysis, and predictive insights.

While 93% of automotive leaders are rushing to adopt generative AI, off-the-shelf LLMs aren't built to handle the complex time series data that powers vehicle health and predictive maintenance. Learn why purpose-built AI solutions are essential for reducing downtime and catching early warning signals in connected vehicle data.

In our new Viaduct Visionaries series, Viaduct COO Matej Drev speaks with Scott Garberding, former Chief Manufacturing and Quality Officer at FCA, about transforming automotive quality using data-driven insights.

What does it take to go from collecting to actually using connected vehicle data at scale? In this blog post, we break down the key considerations for teams looking to build a scalable data infrastructure for analytics and insights from their data.

Despite the rise of telematics technology in agriculture, equipment downtime continues to be a major challenge for manufacturers. Learn why raw data alone isn’t solving the problem—and how Viaduct’s AI-powered solution can transform it into insights to minimize downtime and boost efficiency.

A surprisingly common reason that analytics teams struggle to deploy models on connected vehicle data at scale isn’t actually technical in nature – it’s an unclear definition of the problem they're trying to solve. Here's how analytics and business leaders can build a common language for defining analytical problems and measuring modeling success.

Quality engineers in the automotive industry face staggering challenges as vehicles become more sophisticated — and traditional methods of quality investigation struggle to keep up. Learn how a heavy-duty truck manufacturer used AI to “narrow the search area” for a systemic quality defect and accelerate the issue investigation process by over 90%.

Traditional approaches to quality management are struggling to keep pace as vehicles become more complex and connected. Harnessing AI for quality has the potential to transform quality as a business driver on multiple dimensions – from time to identify issues to warranty costs to brand reputation and market position. Read on for real-world examples of the AI revolution in quality from global OEMs.

We're announcing the close of a $10 million Series B funding. The round was led by FM Capital, a venture capital firm which invests in the people and technologies that are transforming the automotive and transportation industries. FM Capital was joined by Innovation Endeavors, Exor Ventures, Stellantis Ventures and Sumitomo Rubber.