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.

Strategic Development of AI-Driven Business Insights

The goal of this project is to develop a framework that enables businesses to extract actionable insights from AI-driven analytics. Students will explore how AI models can be leveraged to improve business decision-making, focusing on areas such as customer segmentation, predictive analytics, and data-driven strategy formulation. The project will involve building a prototype dashboard that visualizes AI-generated insights. This project is best suited for computer science, AI, or data analytics students interested in business intelligence and AI-driven decision-making.

Interactive Content Engagement Strategy Development for FreeFuse

FreeFuse aims to enhance user retention and platform engagement through a data-driven interactive content strategy. This project involves analyzing user behavior to identify engagement trends and improvement opportunities. Interns will develop interactive content prototypes tailored to user preferences and test engagement tactics to optimize user experience. By applying user experience design, data analysis, and content creation, learners will provide actionable recommendations that improve interaction rates and platform effectiveness.

AI Model Optimization for Data Refinement

This project focuses on improving data preparation and AI model training techniques to enhance predictive accuracy. The goal is to create a systematic process for refining datasets, ensuring high-quality input for AI models used in various business applications. Students will analyze data pre-processing methods, evaluate how data inconsistencies impact model performance, and develop an optimized approach to dataset curation. This project is best suited for computer science, AI, or data science students with experience in machine learning and data engineering.