Top U.S. Automotive Manufacturer Turns to Automated Deep Learning to Make Data-Driven Business Decisions


One of the top three U.S. automotive manufacturers has made it a priority to use Artificial Intelligence to better understand trends in its data, enable more informed, data-driven decisions, and ultimately increase revenue and shareholder value.

To achieve these goals the company planned to build an internal AI platform for sharing data-driven insights across all business units including Sales, Engineering, Research & Development, and Customer Service.

The Challenge

With the shortage of data scientists and the rapid advancement in technology, it’s a massive challenge for any company, even one of the world’s largest, to find enough of the right talent to fully take advantage of all that Artificial Intelligence can do. And with most business units lacking deep technical expertise with AI, the company faced roadblocks to designing and implementing better models and achieving its goal of more informed decisions based on data insights.

Even when you do have the right team members on staff, working with terabytes of disparate data—some structured, some unstructured—is a months long process that involves designing, training, iterating, and deployment. Every project has its own complexities given the amount of structured and unstructured data, which makes it easy to miss the finest clues that can unlock business value.

With massive historical datasets to leverage and terabytes of new data being generated daily, designing a system to pull various data types from many sources together for the purpose of designing high performing models was a task that would require months of development before any positive impact would be seen. The team was looking for a cutting edge AI platform that would support both current and future needs as the company continues to evolve and adopt AI.

The Solution

Following a rigorous evaluation process of multiple platforms that spanned months of testing, ultimately the auto manufacturer chose because of its ability to completely automate the process of generating custom deep learning models in hours instead of months. delivered results that greatly outperformed those generated by their team of data scientists and the platform supported a wide range of applications, which was a critical requirement in their search.

The Engine R&D team is now using generated models to predict various components’ output to better calibrate engines, leading to better performance and lower costs. The team was also able to implement a model to more accurately monitor oxygen sensors to increase quality control on the cars being pushed into the market.

The Big Data Analytics team is using’s Natural Language Processing (NLP) models to find auto component issues resulting from customers’ complaints to call centers.

Following the successful implementation with key business units like R&D and Big Data Analytics, the company now plans to roll out to a broader audience, largely because of how user friendly the platform is—even for those who do not have a background in data science.

Finally, by leveraging advanced deep learning technologies, the various business units are now able to support an additional rich set of applications with better model performance. The time previously invested in designing and deploying models into production is has been reallocated to experimenting, optimizing the business, and identifying ways to drive additional revenue.

Key Benefits

  • Improved accuracy: generated models the R&D team and the Customer Service team that both immediately returned more accurate results than the models that were running in production.
  • Fully automated model generation: Once the team uploaded their data, customized deep learning models were automatically generated, saving months of development and testing time.
  • Cross-functional application: In testing various platforms, the auto manufacturer found that was the only platform that supported a wide range of business applications.
  • Data-driven decision-making: The company was able to efficiently uncover the insights in both unstructured and structured data, and arm its employees with the most precise view of the business through these data-driven insights, leading to more informed decisions across teams.


Schedule a Demo

About aims to help both tech and non-tech users to adopt advancements in AI technologies. has automated the process and transformed how AI projects are planned and implemented in businesses. Working with all data types (numerical, categorical, text, image and time series or dates – and any combination together), without any AI technical background required, users can benefit both the efficiency and quality to build various AI applications like sales forecasting, customer retention, click prediction, image classification, object recognition, recommendation systems, etc.

To learn more about how can help your business adopt the value of AI, visit or email to request a free trial account.

Comments are closed.