Growth Signals Engine – Early Indicator Analysis for Partner Performance

Core Path Partners seeks to empower its ecosystem of partner organizations by identifying leading indicators of transformation, growth, or risk across multiple operational and behavioral data points. This project invites students to create a signal detection framework that identifies early markers of success or concern based on patterns in simulated or historical partner performance data. Rather than relying on lagging indicators (e.g., revenue drop), this model would surface real-time soft signals like reduced initiative velocity, engagement drop-offs, or stalled milestone progress—enabling preemptive advisory action.

Pathway Intelligence: Forecasting Interactive Journey Effectiveness on FreeFuse

FreeFuse is an AI-powered platform for building interactive, multi-path digital experiences. As the company expands into personalized content journeys and Agentic AI assistance, there is growing interest in understanding which types of interactive pathways lead to higher engagement and long-term user retention. This project will focus on analyzing and forecasting content journey effectiveness using structural data and behavioral metrics from FreeFuse pathways. In addition to traditional engagement data (e.g., completion rates, drop-offs), students will explore time-to-decision—how long a user takes between choice points—as a signal of content clarity, complexity, and user confidence. Learners will apply data science, predictive modeling, and visualization techniques to identify high-performing pathways, segment engagement styles, and forecast content success based on journey composition and user behavior.

Unified Intelligence Roll-Up - Designing a Multi-Partner Analytics Dashboard for Core Path Partners

Core Path Partners is a collective initiative designed to bring together partner organizations under a shared framework of growth, innovation, and impact. Each partner brings unique strengths, offerings, and data streams. However, there is currently no unified system to monitor performance, surface collective insights, or generate real-time advisory intelligence across the ecosystem. This project challenges student teams to develop a modular, dynamic analytics dashboard system that enables Core Path Partners to: Understand individual and aggregate partner performance Identify early warning signals or success trends Visualize phase-specific metrics across organizations Support advisory storytelling through data-backed insights

AI-Powered Lead Qualification for Sales & Business Development

The objective of this project is to analyze and optimize an AI-driven lead qualification model, ensuring it accurately identifies high-potential leads for sales teams. Students will evaluate different lead-scoring methodologies, refine key input parameters, and assess the AI model’s predictive capabilities. By conducting research and analyzing real-world data, students will provide data-backed recommendations to improve the model’s effectiveness. This project is specifically designed for students in data science, artificial intelligence, or business analytics programs , allowing them to apply classroom knowledge in AI model optimization, data analysis, and performance evaluation. The focus will be on refining the model rather than integrating it into a broader sales automation system, ensuring that the project remains within a single academic discipline.