This real-world case study explores key phases of the TripoAi product lifecycle. Chosen for its relevance to 3D production and creative tools, it reflects my approach to AI product strategy, cross-functional alignment, and lifecycle execution. It is grounded in user experience, research, and deep familiarity with emerging AI products.
These four were chosen to reflect how I define vision, align teams, prioritize user needs, and assess AI opportunity from the start. Complete phase deliverables available upon request:
Market Requirements Document
Competitive Landscape / SWOT
Initial Success Metrics
Preliminary Business Case
TripoAI is a browser-based generative AI platform designed to simplify and accelerate the creation of 3D scenes for users across multiple industries. By eliminating the technical barriers, high costs, and complex workflows common to traditional 3D tools, TripoAI empowers indie game developers, animation and VFX studios, AR/VR creators, educators, and students to rapidly visualize ideas. Its user-friendly interface, affordability, and speed enable anyone to generate professional-quality 3D assets quickly, making it ideal for rapid prototyping, creative experimentation, and scalable content creation.
AI-generated 3D assets from images; supports game-ready models
Enterprise-only access; lacks rigging and animation flexibility
Free; robust toolset; large open-source community
Steep learning curve; not AI-native; complex for beginners
Industry-standard; powerful animation and rigging
Manual, time-intensive; expensive; not AI-assisted; limited for casual creators
This Project Charter outlines the strategic goals, team structure, and key milestones for developing TripoAI. The platform leverages generative AI to simplify the creation of professional-quality 3D models for game developers, animators, AR/VR designers, and educators, making advanced 3D content creation fast, accessible, and intuitive.
Creating 3D assets is traditionally slow, expensive, and limited to experts. TripoAI leverages generative AI to eliminate these barriers, enabling indie developers, artists, and non-technical creators to quickly generate high-quality, usable 3D content. By automating complex workflows, TripoAI reduces costs, accelerates production, and opens new creative possibilities across gaming, animation, VFX, AR/VR, and marketing industries. This document evaluates the opportunity, feasibility, potential impacts, and risks associated with implementing generative AI as a strategic advantage for TripoAI
A behind-the-scenes look at the thinking that shaped each of these deliverables.
I approached the Product Concept Document as a chance to push beyond the surface of what AI tools do and focus instead on why certain features actually matter in real creative workflows. I prioritized problems that I’ve personally seen stall or break production, like asset iteration bottlenecks, long turnaround times, and the overhead of managing multiple DCC tools. I deliberately centered industries like animation and VFX because I know how different their needs are from typical game or AR pipelines, and I wanted to show that TripoAI isn’t a one-size-fits-all solution. I anchored the concept in speed, accessibility, and compatibility, because I knew those would be the deciding factors for adoption.
I leaned heavily on my experience working across animation and production teams to shape the structure of this charter. I focused on how teams actually move through delivery, where they get stuck, and how communication either supports or blocks momentum. I gave the timeline the same kind of attention I’d give to a real-world production schedule, making sure every milestone was there for a reason. The meeting cadence was designed to support fast iteration while still creating space to surface blockers and capture team successes. I included the roles that would matter most in a tool like TripoAI and backed them with justifications based on how technical and creative workflows intersect. Everything in this doc was written to support clear execution...not just alignment.
What mattered most in this deliverable was clearly articulating where AI would bring real value, and where it might create friction. I pulled directly from my own experience in animation and production to define the kinds of issues that derail creative workflows, like unpredictable output quality, lack of control, and inconsistent tooling. I focused on feasibility, not just potential, because I’ve seen how easily products can fall apart when that’s ignored. My goal was to frame AI as a tool that had to prove it belonged in the workflow, and then show exactly what that success would look like. I introduced the idea of using open-source and community datasets to begin to set the stage for ethical AI practices that pass strict AI governance standards and will expound on this in the upcoming phase deliverables.
