By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie Policy and Privacy Policy for more information.
Product Overview

Marathon

Marathon enables you to solve problems as you go by continuously monitoring your critical equipment, suggesting prescriptive actions to prevent downtime, reduce equipment lifecycle cost and lower energy consumption.

Take prescriptive actions when they are needed! Marathon is a comprehensive Asset Reliability Management cloud-based software suite. Co-developed with digitally mature industry partners, Marathon offers Ease of Integration with existing data systems, Value Driven analytics workflows for operations and maintenance decision support, and Rapid Scalability to standardize data-driven processes across your organization.

Learn how to get started quickly and see results in weeks!

Download all information about Marathon and the Marathon Quick Starter Package now.

Let’s connect the dots!

Join other leading industrial companies and discover how Arundo’s AI Foundation adds insight and intelligence to your operations

More insights

All insights
Article
What a Cup of Coffee Can Teach Us About AI-Driven Manufacturing
Article
Using Large Language Models and AI Agent Systems to Provide AI Assistants for Improved Industrial Operations
Article
While large language models (LLMs) may struggle to generate insights directly from time-series data, their strong pattern-recognition capabilities make them well-suited for identifying and synthesizing already existing insights. We therefore argue that an architecture featuring a separate graph-based domain model and time-series data storage is the ideal architecture for a generalized AI companion serving the heavy asset industry: An agentic AI can first identify relevant nodes (assets, sensors, and a large variety of pre-computed models) for answering a user prompt using a mixture of APIs, query language generation, semantic search, and graph traversal. Only after this filtering step is time-series data accessed and retrieved to synthesize an answer.