Cummins

Customer Overview

Cummins is a global powertrain and technology leader with more than 100 years of innovation in design, manufacturing, and engineering excellence. Over the past two decades, the company has strategically expanded its global footprint, diversified its business lines, and invested in advanced tools and systems across engineering, operations, and corporate functions.

With a workforce of more than 69,000 employees worldwide, including 15,000 engineers, Cummins operates at a scale where precision, speed, and access to institutional knowledge are critical. Its engineers rely on decades of technical documentation and domain expertise to make high-impact design, quality, and warranty decisions. To remain at the forefront of innovation and continue attracting top-tier, AI-native talent, Cummins launched a bold initiative to modernize how employees discover, access, and apply enterprise knowledge.

Challenges

As Cummins expanded globally and technologically, its information ecosystem naturally grew in complexity. Critical knowledge and data were distributed across multiple specialized systems including enterprise document tools (ex. SharePoint), Product Lifecycle Management tools, and more.

New hires, particularly AI-native talent, expected intelligent, unified search experiences, domain-aware assistants, and seamless access to consolidated views of this enterprise knowledge and data.

Solutions & Results

Cummins was an early adopter of generative AI technologies, exploring their potential across the enterprise. While these tools demonstrated promise, the company quickly recognized that generalized AI solutions were not designed to address the depth, complexity, and security requirements of Cummins’ engineering and operational environment.

Cummins conducted a rigorous proof of concept, evaluating leading large language model tools alongside enterprise search platforms to assess their ability to meet the company’s standards for productivity, decision quality, performance, and data security. Out-of-the-box tools were unable to provide authoritative answers grounded in Cummins’ internal knowledge, such as product data, engineering standards, warranty history, or enterprise processes.

Deployment and Results

Following this evaluation, Cummins deployed Sinequa by Argonos to deliver context-aware answers by drawing from the most relevant and authoritative enterprise sources, including deep integration with core engineering systems to accurately interpret highly technical product and design data. The solution progressed from proof of concept to enterprise production in approximately 12–14 weeks while meeting the rigorous security, governance, and privacy standards required of a global Fortune 500 manufacturer.

Responses are clear, structured, and fully cited, giving engineers confidence in the accuracy and traceability of the information. This improves access to evidence for design, warranty, and failure analysis.

By approaching generative AI as a strategic enterprise capability rather than a standalone tool, Cummins achieved its objectives: productivity improvement for engineers, improved quality of answers provided, and delivering a highly attractive ROI. This approach unlocked the full value of the company’s institutional knowledge while maintaining the rigor, trust, and security that define its brand.

What Cummins says