Andretti Racing · RaceOS real-time car performance application

A Evidence level

Andretti Racing: RaceOS real-time car performance application

Publisher: Palantir · Vendors ' official customer information · Direct sources at the case level

Claim origin: Public disclosure by manufacturer or customer · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 8 / 12

Business context1 / 2

Transformation workflow1 / 2

Technical workflow2 / 2

Human roles & governance1 / 2

Measured outcomes1 / 2

Source traceability2 / 2

Remaining gaps: Business context, Transformation workflow, Human roles & governance, Measured outcomes

The focus of this case is to transform the “RaceOS real-time racing performance application” from a one-time analysis or single-point tool into a production stream that can be embedded into real business, verifiable and sustainable.

Business problem

Track decision-making relies on telemetry, tactics and historical energy data for real-time racing vehicles and requires the rapid transformation of multiple analyses into operational applications.

Solution

Andretti constructed RaceOS on the Palantir structure, connecting real-time vehicle performance to a series of AI-driven applications.

Technical architecture & production workflow

Step 01

Equipment/quality/process/maintenance/supply chain/historical events

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Step 02

Foundry integrates real-time and historical industry data

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Step 03

Establishment of equipment, spare parts, orders, processes and constraints

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Step 04

AIP/project/optimal identification of risks and recommended actions

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Step 05

Engineer/field personnel confirm and operate

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Step 06

Implementation results and failure feedback deposition as follow-up model and rule context

Key technology & infrastructure components

FoundryAIPOntologyReal-time telemetryRaceOS application

Human roles & accountability

Game engineering and strategy teams are responsible for final track action.

FDE delivery actions

Run a full process with the first-line team to identify nodes that really require decision-making rather than displaying data
Collapse decentralized data sources, competencies and business terms into a single, operational Ontology
Combine business rules, optimization models, LLM and traditional software according to a reliable boundary, instead of letting the LLM operate
Insert recommendations directly into existing operating workstations and design approvals, rejections, upgrades and write back to Action
Continue to modify Ontology, rules and automation with operational KPI acceptance and feedback

Reusable delivery patterns

Let's be clear about the client, the relationship, the state and the action, and then talk about Agent.
The recommendation must enter the executable stream, otherwise it's just another dashboard.
High-value deployment is often a combination of data integration + rules/ optimization + AI + Human-in-the-lop
Continuous rewriting of the results of implementation to build cumulative operational memory

Business outcomes & delivery results

The Palantir structure document lists RaceOS as a representative case of the extended client standard AIP/Foundry architecture; no unified ROI is disclosed.

Evidence boundaries & verification notes

The Palantir official structure document clearly named Andretti RaceOS and its connection to real-time racing performance with AI applications; limited disclosure of technical details.
The source can be directly located in the case; the value remains disclosed by the source and does not represent an independent audit.

Primary source: Andretti Racing: RaceOS real-time car performance application ↗