SOC-2 Cybersecurity Compliance Assistance with Vanta

FreeFuse is seeking cybersecurity-focused learners to assist in achieving SOC-2 compliance using Vanta, our security compliance automation provider. This project will involve evaluating our security posture, reviewing policies, and assisting with technical implementation to meet compliance standards. Learners will work closely with Wendy Chen, FreeFuse’s Tech Lead, to identify vulnerabilities, document security controls, and ensure alignment with industry best practices. By completing this project, learners will gain hands-on experience in cybersecurity compliance, risk mitigation, and IT security governance, contributing to FreeFuse’s broader security infrastructure.

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 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.