Zapata Case Studies Industrial Generative AI for Next-Generation Race Analytics: A Case Study on Andretti Autosport

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	Industrial Generative AI for Next-Generation Race Analytics: A Case Study on Andretti Autosport Zapata

Industrial Generative AI for Next-Generation Race Analytics: A Case Study on Andretti Autosport

Zapata

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Automotive Construction & Infrastructure
Applicable Functions	Maintenance Quality Assurance
Use Cases	Predictive Maintenance Vehicle-to-Infrastructure
Services	Data Science Services Testing & Certification
Challenge	Andretti Autosport, a renowned racing team, was grappling with the challenge of effectively analyzing the massive amount of data generated during races, approximately 1 terabyte per car. The team was in search of innovative ways to leverage this data to gain a competitive edge and win more races. The primary objective was to upgrade their existing data analytics infrastructure by incorporating proprietary Zapata Generative AI techniques to drive their race strategy. The challenge was not only to manage and analyze the vast amount of data but also to make real-time strategic decisions based on the insights derived from the data. Read More
About Customer	Andretti Autosport is a well-known racing team that competes in various racing events. The team is always on the lookout for innovative ways to gain a competitive edge over their rivals. They generate a significant amount of data during races, approximately 1 terabyte per car, and were looking for better ways to analyze this data to enhance their race strategy and win more races. They aimed to upgrade their existing data analytics infrastructure by incorporating advanced AI techniques. The team partnered with Zapata to leverage their proprietary Generative AI techniques and the Orquestra platform to achieve this goal. Read More
Solution	In response to the challenge, Zapata began upgrading Andretti’s analytics infrastructure in 2022, piloting advanced Industrial Generative AI applications and techniques. The Orquestra® platform was deployed within the Andretti Autosport \| Zapata Computing Race Analytics Command Center (RACC), a mobile engineering environment. This hybrid infrastructure combined data lake integration, cloud, and dashboards to drive decision-making, all managed with Orquestra. Engineers from both Zapata and Andretti worked together to test various use cases in machine learning and optimization. The solutions being tested included a machine learning model for strategic tire change decisions, advanced analytics for fuel consumption optimization, and a predictive model for anticipating yellow flags based on various factors. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Automotive

Construction & Infrastructure

Applicable Functions

Maintenance

Quality Assurance

Use Cases

Predictive Maintenance

Vehicle-to-Infrastructure

Services

Data Science Services

Testing & Certification

Challenge

Andretti Autosport, a renowned racing team, was grappling with the challenge of effectively analyzing the massive amount of data generated during races, approximately 1 terabyte per car. The team was in search of innovative ways to leverage this data to gain a competitive edge and win more races. The primary objective was to upgrade their existing data analytics infrastructure by incorporating proprietary Zapata Generative AI techniques to drive their race strategy. The challenge was not only to manage and analyze the vast amount of data but also to make real-time strategic decisions based on the insights derived from the data.

About Customer

Andretti Autosport is a well-known racing team that competes in various racing events. The team is always on the lookout for innovative ways to gain a competitive edge over their rivals. They generate a significant amount of data during races, approximately 1 terabyte per car, and were looking for better ways to analyze this data to enhance their race strategy and win more races. They aimed to upgrade their existing data analytics infrastructure by incorporating advanced AI techniques. The team partnered with Zapata to leverage their proprietary Generative AI techniques and the Orquestra platform to achieve this goal.

Solution

In response to the challenge, Zapata began upgrading Andretti’s analytics infrastructure in 2022, piloting advanced Industrial Generative AI applications and techniques. The Orquestra® platform was deployed within the Andretti Autosport | Zapata Computing Race Analytics Command Center (RACC), a mobile engineering environment. This hybrid infrastructure combined data lake integration, cloud, and dashboards to drive decision-making, all managed with Orquestra. Engineers from both Zapata and Andretti worked together to test various use cases in machine learning and optimization. The solutions being tested included a machine learning model for strategic tire change decisions, advanced analytics for fuel consumption optimization, and a predictive model for anticipating yellow flags based on various factors.

Impact #1	The deployment of the Orquestra platform and the implementation of advanced Industrial Generative AI applications and techniques have provided Andretti Autosport with an AI-driven advantage in a field where every millisecond counts. The Race Analytics Command Center has become a hub for real-time decision-making on race day, with engineers from both Zapata and Andretti working together to test various use cases in machine learning and optimization. The solutions being tested, including strategic tire change decisions, fuel consumption optimization, and predictive yellow flag anticipation, are not only applicable to racing but also translate to predictive maintenance and disruptive event response problems across various industries.

Impact #1

The deployment of the Orquestra platform and the implementation of advanced Industrial Generative AI applications and techniques have provided Andretti Autosport with an AI-driven advantage in a field where every millisecond counts. The Race Analytics Command Center has become a hub for real-time decision-making on race day, with engineers from both Zapata and Andretti working together to test various use cases in machine learning and optimization. The solutions being tested, including strategic tire change decisions, fuel consumption optimization, and predictive yellow flag anticipation, are not only applicable to racing but also translate to predictive maintenance and disruptive event response problems across various industries.

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Overview

Industrial Generative AI for Next-Generation Race Analytics: A Case Study on Andretti Autosport

Operational Impact