andretti autosport.

Industrial Generative AI for
next-generation race analytics, powered by Orquestra

Andretti Autosport is upgrading their analytics infrastructure to enhance decision-making and win more races.

Season 1 Recap

Take a behind-the-scenes look at what’s happened in our first season with Andretti Autosport, from the cross-team collaboration in the Race Analytics Command Center (RACC) to upgrading Andretti’s analytics infrastructure.

The Challenge

Races generate a lot of data — about 1 terabyte per car. Andretti Autosport is always looking for better ways to analyze that data to gain an edge over the competition. Their goal? Upgrade their existing data analytics infrastructure with proprietary Zapata Generative AI techniques to drive their race strategy and win more races.

 

Our Approach

In 2022, Zapata began upgrading Andretti’s analytics infrastructure and piloting advanced Industrial Generative AI applications and techniques. In the future, as Generative AI technology improves and advanced hardware (such as quantum computers) matures, the team can test the performance of new devices and algorithms on Orquestra, and easily swap in whichever backend delivers the best performance.

 

The Race Analytics Command Center

In early 2022, we deployed the Orquestra® platform within the Andretti Autosport | Zapata Computing Race Analytics Command Center (RACC). The RACC is a mobile engineering environment where engineers from Zapata and Andretti work side-by-side in pursuit of a real-time advantage on race day. The hybrid infrastructure combines data lake integration, cloud and dashboards to drive decision-making — all managed with Orquestra. Engineers working from the RACC are testing various use cases in machine learning and optimization.

 

 

Not all the action happens on the track—

there is an entire engineering world inside this truck.

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Our work together

An AI-driven advantage when every millisecond counts.

Zapata is testing and piloting next-gen solutions using Andretti proprietary data on advanced compute and architecture. Built and run on the Orquestra platform, Andretti’s applications are forward-compatible with future generative AI and hardware advancements — to ensure Andretti will remain in pole position.

Exploring Use Cases in Machine Learning and Optimization

 

Tire Degradation Analysis

Tires wear out quickly going over 200MPH, requiring time-consuming pit stops to change tires. Zapata is working with Andretti to create a machine learning model that can guide strategic decisions around tire changes, such as when a car should swap out tires, which tires should be used, and how often they should change tires based on current conditions. This use case translates to predictive maintenance problems across industries.

Fuel Savings Optimization

The fewer times a car has to refuel, the more time it can save in the race. Zapata and Andretti are applying machine learning and advanced analytics to help drivers optimize their fuel consumption and determine the best timing for refueling. Similar fuel savings solutions have wide applicability in any industry looking to shrink its carbon footprint or time to delivery.

Predictive Modeling: Yellow Flag

When there’s an accident or debris on the track, drivers are required to reduce their speed and are prohibited from passing other cars. This is a yellow flag, and since cars aren’t going full speed, it’s often a good time for a pit stop. Zapata and Andretti are creating a model to predict when a yellow flag is likely based on track conditions, the status of various cars, the drivers in those cars, and other factors. This ability to predict and preemptively respond to disruptive events has wide applicability beyond racing.

“INDYCAR racing is all about finding every possible edge and then maximizing it. Zapata’s expertise gives us that advantage through their Orquestra quantum software platform and expert scientists and engineers.”

Michael Andretti
CEO and Chairman, Andretti Autosport

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