Speakers:
Scaling Predictions: The Journey from Model Development to Industry Value
Date:
Tuesday, June 3, 2025
Time:
3:55 pm
Summary:
Hahn is an analytics services firm—but they’ve also developed a unique PR solution called FuseLight that combines predictive AI with genAI: You give it the name of a brand and an influencer and it both predicts the media outcome and generates proposals for a joint PR initiative.
Turning a predictive model into a market-ready product involves more than just accuracy—it’s about delivering actionable insights, intuitive usability, and market traction. In this talk, Michael will show FuseLight’s journey from a predictive analytics model to a product that empowers brands to forecast public fascination with brand partnerships. Along the way, he’ll tackle three key questions:
- How do you make predictions truly actionable for clients?
- What’s the trick to turning complex data into a simple, intuitive interface?
- And how do you acquire market share in a competitive space?
Join Michael for an inside look at the technical, strategic, and practical steps that transformed a model into a scalable solution driving real-world impact.
Speakers:
Synthetic Time Series with LLMs: A Case Study in Automotive Brake Design
Date:
Tuesday, June 3, 2025
Time:
4:20 pm
Summary:
This session presents a novel framework leveraging Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to create synthetic time-series data for designing automotive braking systems. Mantek’s approach addresses the challenge of limited real-world data by generating realistic simulations of braking performance for new material compositions. Mantek will demonstrate how LLMs can capture complex temporal dependencies and statistical properties of braking system behavior, even when trained on limited data. He’ll compare our approach to traditional time-series methods, showcasing its superior performance in generating high-fidelity synthetic data. Finally, he’ll show how this synthetic data can be used to improve downstream tasks, such as anomaly detection and material selection, leading to accelerated design cycles in automotive manufacturing.