Engagement Optimization Agent for Interactive Journeys

FreeFuse wants to develop an AI agent that actively boosts user engagement within branching video or interactive journeys. This agent would monitor how users interact—where they pause, click, disengage—and adjust its tone, timing, and suggestions in real time. The goal is to make FreeFuse experiences feel more responsive, more personalized, and more rewarding with every session. This project supports the evolution of FreeFuse from a static tool to a live content experience. It also enables scalable deployment of AI agents that increase stickiness, watch time, and creator satisfaction—all vital to FreeFuse’s market edge and monetization. Student Team will: Define engagement metrics specific to FreeFuse’s platform (e.g., dwell time on nodes, completion %, emotional feedback) Design agent prompts that adjust guidance based on user signals Build 2–3 engagement-optimized agent personas (e.g., hype, helper, challenger) Simulate agent impact across user interaction logs Recommend deployment strategies for ongoing learning or adaptation

Dynamic Content Graph Builder for Interactive Video Journeys

The goal of this project is to develop a visual content graph editor that empowers FreeFuse users to easily build, manage, and edit non-linear video narratives. This tool will allow creators to define user decision points, branch logic, and media flows through an intuitive drag-and-drop interface. It will also support future integration with FreeFuse’s backend systems, AI assistants, and analytics modules. The project is intended to produce a functional prototype that visualizes how content nodes connect across a multi-path journey and exports logic in a format usable by the FreeFuse platform (e.g., JSON). Project Goals: Design and Build a Visual Editor Interface Create a user-friendly front end (React or Angular) for designing interactive decision trees. Include capabilities to add, label, link, and delete nodes. Support visual indicators for entry points, loops, and end nodes. Implement Interactive Logic Mapping Enable each node to include metadata such as: Content file/link Choice labels Branch conditions Show directionality and interactivity between choices (e.g., arrows, highlights). Support Logic Export Allow creators to export the graph structure into JSON or similar structured data. Ensure it maps cleanly to FreeFuse’s existing or planned content delivery system. Enable Node Preview and Edit Options Allow users to preview content or input descriptive data for each node. Build edit/delete functionality with confirmation prompts. Optional Stretch Goal: Real-Time Collaboration Simulation Add simulation of co-creator tagging or shared editing features (concept only if time-constrained).

Smart Interaction Mapping Tool for AI-Driven Media Branching

FreeFuse allows users to build interactive content journeys. This project challenges students to design and prototype a visual interface or mapping tool that allows creators to easily visualize, edit, and simulate branching logic within AI-enhanced videos. Students will build a lightweight version of a “smart storyboard” or interaction flow editor that shows how choices in media affect outcomes—and how AI might suggest or auto-generate new branches.

Strategic Multi-Touch Launch Campaign for FreeFuse: From LA Tech Week to National Expansion

FreeFuse is launching a new wave of product capabilities that merge interactive media, AI-driven personalization, and smart content delivery. This project invites students to craft a multi-phase promotional campaign across three activation pillars : LA Tech Week (October 13–19, 2025) – Used to generate buzz and early traction. Fall Pilot Program & Creator Showcase (Late October–November) – Featuring early adopters using the platform to create dynamic content. Winter Education & Innovation Push (January 2026) – Targeted expansion into schools, innovation labs, and student-led activations. The campaign should maintain thematic and messaging consistency while adapting to each milestone’s audience and intent. Students will plan content strategy, outreach, activation ideas, and engagement tracking across all phases.

Strategic Partnership Playbook for Ecosystem Growth

FreeFuse has use cases across education, creator economy, AI research, and smart media tech. This project will identify potential partners (accelerators, incubators, creator tools, universities) and define a tiered partnership strategy for platform expansion. Objective: To develop a comprehensive and actionable partnership strategy that enables FreeFuse to grow its ecosystem through aligned external organizations—such as innovation labs, educational institutions, creator platforms, AI research hubs, and enterprise collaborators. The goal is to identify and prioritize partnership types that will accelerate adoption, credibility, and product integration , while also building a structured framework FreeFuse can use to evaluate and activate new opportunities over time. Project Goals: Map the Strategic Ecosystem Identify key sectors where FreeFuse can gain traction (e.g., edtech, immersive learning, AI tools, digital media production, nonprofits). Research 10–15 potential partners and categorize them by alignment level (e.g., product synergy, shared audience, growth acceleration). Develop a Tiered Partnership Framework Define partnership levels (e.g., Community Partner, Pilot/Co-Builder, Strategic Anchor). Outline benefits and commitments required at each level. Include partner fit criteria (technical, operational, mission alignment). Create Partner Value Propositions Draft use-case-aligned messaging that explains the value of partnering with FreeFuse from each segment’s perspective (e.g., “Why a university would want FreeFuse,” “Why a tech incubator would align”). Design an Activation Strategy Recommend onboarding flows, pilot programs, and co-marketing tactics. Develop templates for outreach, onboarding checklists, and success metrics. Deliver a Visual Playbook Present a final deliverable that includes visual partner maps, case study examples, and an actionable next-steps calendar.

Strategic Product Development Framework for Multi-Modal Interactive Media & AI Integration

FreeFuse is a platform driving innovation at the intersection of interactive video, AI, and smart content delivery. As the product grows across creators, enterprise partners, and IoT-driven experiences, a unified, flexible product development framework is critical. This project challenges students to act as cross-functional product leaders, designing a full product development and management strategy. It will include a core roadmap, modular epic breakdowns, a prioritization model, and a lightweight Agile playbook suited to FreeFuse’s hybrid content/tech environment.

CRM Strategy Blueprint for Multi-Segment User Engagement at FreeFuse

FreeFuse has a diverse user base that includes creators, educators, enterprise clients, and innovation partners. This project will help develop a CRM strategy blueprint that identifies key user segments, maps out lifecycle stages, and outlines communication cadences. Students will create a data-driven CRM engagement plan that addresses how to manage creators differently from enterprise contacts, and how to transition engaged leads into partner-level relationships. The focus will be on enabling FreeFuse to scale engagement without losing personalization.

SegmentFuse - Adaptive UX through User Journey Clustering

FreeFuse content is navigated in non-linear paths. This project will analyze those user journeys and develop a clustering engine that groups viewers into segments based on interaction styles. These clusters will enable real-time content personalization and adaptive UX. Students will build a backend microservice to identify behavioral patterns and build content delivery logic for distinct personas.

Live Video Stream Interaction Engine for Smart Content Control

This project challenges students to build a system that allows real-time object interaction within live video streams. The core deliverable is a web-based interface that detects and tracks physical objects through a webcam/IP stream, enabling user interaction via overlays that can trigger logic inside the FreeFuse platform—or even control IoT devices. The team will explore real-time video pipelines, object tracking, and full-stack orchestration of interaction data to backend triggers or hardware responses.

Strategic Partnership Mapping & Activation Plan for FreeFuse Ecosystem Expansion

FreeFuse is building a media and AI innovation ecosystem that touches education, creator commerce, enterprise L&D, and smart interaction environments. To scale, FreeFuse needs a more structured partnership strategy to identify and prioritize collaboration with incubators, content platforms, edtech providers, and accelerators. This project invites student consultants to create a strategic partner discovery and outreach plan, including analysis of high-fit collaborators, co-marketing opportunities, and ecosystem leverage points. The project will help FreeFuse define its role as a connective platform in the emerging AI-powered media and learning space.

Procurement Decision Modeling with Generative AI: Optimizing Partner Selection Through Interactive Content Signals

FreeFuse is exploring how the choices users make within interactive content can reflect deeper preferences, risk tolerances, or learning gaps—which could also inform procurement or partnership decisions. In this project, students will design a procurement decision model that uses: User behavior data from FreeFuse content to infer buyer intent or partner alignment Generative AI tools (like ChatGPT or Claude) to simulate decision trade-offs (cost vs. risk, speed vs. quality) Scoring frameworks to evaluate which supplier or vendor path makes the most sense The final system should help recommend vendor or fulfillment options based on soft signals (user decisions), AI-driven inference, and traditional procurement factors.

Interactive Object-Based UX for Video Environments

FreeFuse is developing next-generation tools that allow creators to embed object-based interactions within video content. This project invites students to design an intuitive UX/UI system where users can visually tag objects in video frames and assign interactive actions—enabling dynamic, clickable media experiences without needing technical expertise. Students will focus on designing creator-facing interfaces that make complex interaction logic simple, visual, and engaging. The outcome will be a prototype showcasing how creators define clickable zones, assign triggers, and preview interactive video behavior.

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.

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

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.

Strategic Partnership Expansion Initiative

FreeFuse is seeking to strategically expand its partnership network and explore new market opportunities. The primary goal of this project is to identify and evaluate potential partnership opportunities that align with FreeFuse's business objectives. By conducting market research and analysis, the team will identify adjacent markets that FreeFuse can expand into, thereby increasing its market presence and reach. Additionally, the project aims to develop a framework for creating brand ambassadors beyond the initial partners, enhancing FreeFuse's brand visibility and influence. This initiative will provide learners with the opportunity to apply their knowledge of market analysis, strategic planning, and partnership development in a real-world context. The project will involve tasks such as researching potential partners, analyzing market trends, and developing strategies for ambassador engagement.

Agentic AI for Smart User Assistance and Automated Onboarding

FreeFuse seeks to develop a lightweight but intelligent onboarding assistant that helps new users navigate and adopt the platform effectively. The AI-powered assistant will greet new users, guide them through setup, and answer commonly asked questions in natural language. The core functionality will include: Conversational onboarding that adjusts based on user behavior (e.g., creator vs. viewer). FAQ automation, enabling the assistant to address typical support questions. Contextual nudges, such as suggesting what to do next or how to get the most out of a feature. The system will be designed using pre-trained NLP tools and rule-based logic to avoid the complexity of building custom AI models. The emphasis will be on flow design, interaction quality, and integration simulation—not deploying advanced machine learning or computer vision.

Interactive Content Engagement Analytics for FreeFuse

FreeFuse is an interactive content platform looking to analyze user engagement and retention trends to optimize content performance. This project will involve extracting and analyzing platform data to generate insights on how users interact with different types of media. The goal is to identify trends, predict user engagement patterns, and create interactive dashboards for decision-making. By completing this project, learners will gain experience in data-driven decision-making, business intelligence tools, and content analytics, contributing directly to FreeFuse’s marketing and user engagement strategies.

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.

Smart Self-Service Kiosk – Market Feasibility & Prototyping

This project will focus specifically on the self-service kiosk industry , examining how video-driven decision-making can enhance automation in retail environments such as convenience stores, fast food outlets, and vending machines. Instead of broadly covering multiple industries, students will concentrate on market research and prototyping for a single use case , making the project more structured and feasible within an academic setting. Students will conduct research on industry feasibility, develop a conceptual prototype , and present business insights for implementing an interactive video-based self-service kiosk.

Interactive Video-Controlled Retail Automation System

This project focuses on developing an interactive video system that enhances customer engagement in retail by allowing users to make real-time choices in a FreeFuse-powered interactive media experience. Instead of integrating a full IoT system, this project will simulate product selection and recommendation processes, helping students gain practical experience in interactive media design and decision-based user journeys. By simplifying the project scope, students will develop a robust understanding of interactive video technologies and how they can influence consumer behavior in retail settings.

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.

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.

Interactive Video-Triggered Smart Machine System for Retail Automation

Develop a video-interactive smart machine system that allows users to engage with FreeFuse-powered interactive media (e.g., decision-based video journeys) to control an IoT-connected machine in a retail setting. Users will make real-time choices within an interactive video, and these choices will trigger machine actions such as displaying product recommendations, activating a robotic system, or dispensing a selected product. This project will focus on integrating interactive video decision-making with IoT automation in a controlled environment, demonstrating how media-driven engagement can seamlessly connect digital content with physical systems.

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.

Cloud Intelligence Backbone for Real-Time Content Decisions

FreeFuse’s next-gen platform needs to support real-time decision-making as users interact with branching content or control hardware. This project focuses on architecting the cloud-side intelligence layer : a scalable foundation that receives user interaction events, makes smart suggestions, and pushes updates to the UI, hardware layer, or AI agent. Skills Gained: Designing hybrid (edge/cloud) data flow architectures AWS/GCP/Azure services (e.g. Lambda, Firehose, Cloud Pub/Sub, EventBridge) NoSQL data modeling (for real-time decisions) Integration planning for AI agent frameworks Cloud-native scalability + resilience best practices

Subscription Churn Prediction and Alert System

FreeFuse is seeking to gain insights into the patterns and timing of customer churn within its subscription offerings. The goal of this project is to analyze existing subscription data to identify key indicators and trends that precede churn events. By understanding these patterns, FreeFuse aims to develop a proactive alert system that can notify the company when users are at risk of discontinuing their subscriptions. This project will allow learners to apply data analysis techniques and predictive modeling skills acquired in the classroom. The tasks will include data cleaning, exploratory data analysis, and the development of a predictive model. The project is designed to be completed by a team of data science students within a single academic program, ensuring a focused approach to the problem.

B2B Creator Acquisition & Partnership Strategy for FreeFuse

FreeFuse is preparing to launch its Agentic AI-powered creation tools—designed not just for solo creators, but also for educators, training providers, creative agencies, and branded content teams. This project challenges student teams to develop a B2B go-to-market strategy that positions FreeFuse as a transformational creative platform for professional creator teams, institutions, and studios. The team will identify ideal B2B creator segments, map out a value-driven acquisition strategy, and propose strategic partnership models that align with FreeFuse’s growth goals.

Agentic AI Content Strategist for Interactive Creators

FreeFuse is developing an Agentic AI Creative Assistant that helps content creators by autonomously planning, adjusting, and suggesting creative strategies based on platform data. This project focuses on building just one critical component: the Agentic Planning Framework — a self-contained module that can: Accept a creator’s profile or content goals Analyze example inputs or mock behavior patterns Autonomously break down a content plan into sequential goals/tasks Decide when to revise, stop, or hand off to a human The goal is to simulate how an agent might reason through planning creative output and growth strategies — without needing a full platform UI or live AI deployment.

AI-Powered Sentiment & Emotion Analysis for Interactive Content

FreeFuse, in partnership with Not Your Father's AI and LinkedIn Influencer Oliver Yarbrough, is looking to integrate AI-driven sentiment and emotion analysis to better understand user reactions and emotional responses to interactive video content. This project will involve analyzing real-time user interactions, comments, and reactions to assess which types of content elicit positive engagement, frustration, or drop-off behavior. Students will build a sentiment classification model using NLP techniques, generate insights through behavioral clustering, and create actionable recommendations for optimizing content to improve user retention. By completing this project, learners will develop skills in real-world AI modeling, user behavior prediction, and data storytelling for content strategy optimization.