Sudharsan SM

Sudharsan SM

AI/ML Manager
Ford Motor Company

Bio

Sudharsan is an AI/ML Manager at Ford with 7+ years of experience building scalable data science and Generative AI solutions across automotive, analytics, and consulting domains. Proven track record of leading cross-functional teams to deliver production-grade AI systems that drive measurable business impact.

Expert in designing and deploying end-to-end machine learning pipelines, large language model (LLM) solutions, and advanced analytics platforms. Strong background in translating complex business problems into actionable, data-driven strategies.

An IIT Madras graduate with a passion for solving real-world problems using AI, fostering innovation, and mentoring high-performing teams. Committed to staying at the forefront of emerging technologies and delivering practical, high-impact AI products.

Hybrid GenAI and ML for Early Quality Detection Using Repair Order Text with an ever Growing Taxonomy

Featuring: Saha Rimpa

This session offers two key takeaways:

(1) a business blueprint for proactively identifying and fixing emerging vehicle quality issues using service data, and

(2) a technical pattern for building a hybrid GenAI + ML system that can classify textual data into an ever-growing set of labels.

You’ll learn how to turn high-volume, messy repair order text into reliable early-warning signals. This approach applies not only to automotive, but to any domain where unstructured text must be continuously organized into a taxonomy that evolves over time.

The technical novelty is a hybrid architecture that combines traditional machine learning with a multi-agent Generative AI workflow designed for an evolving taxonomy, where new failure modes appear continuously. The framework reuses existing categories when the evidence matches known patterns, and creates new categories only when truly novel themes emerge, so reporting and trend continuity remain intact while the label space grows. This makes the session broadly useful for anyone working with textual data who faces a growing multi class classification problem, whether in automotive, warranty, customer support, healthcare, or industrial service.

From a business perspective, the product helps an automotive OEM reduce warranty cost and repeat dealer visits, improve customer satisfaction and NPS, and reduce operational burden across quality, engineering, and field teams by surfacing issues earlier and enabling faster, more proactive fixes.

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AI/ML Manager Ford Motor Company

Hybrid GenAI and ML for Early Quality Detection Using Repair Order Text with an ever Growing Taxonomy

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AI/ML Manager
Ford Motor Company