AI Solution Builder: From Real-World Problem to Working Prototype & Pitch

Sourcing problems from business and everyday life

Conducting quick user interviews and pain-point research

Evaluating feasibility for an AI-based approach

Writing a clear problem statement

Landscape of AI APIs and models (language, vision, prediction)

Choosing between pre-trained models, fine-tuning, and no-code platforms

Cost, latency, and data considerations

Hands-on: calling your first AI API

Mapping user flow and core features

Wireframing a simple interface

Defining success metrics for your prototype

Scoping a minimum viable prototype (MVP)

Setting up your development or no-code environment

Integrating an AI model or API into your app

Building a basic front-end interface

Handling errors and edge cases gracefully

Testing core functionality

Running lightweight usability tests

Collecting and prioritizing feedback

Iterating on features and UX

Measuring impact against success metrics

Identifying bias and fairness risks

Data privacy and security basics

Transparency and explainability for end users

Checklist for responsible AI deployment

Defining your target user and market

Articulating value proposition and differentiation

Basic monetization and cost models

Anticipating stakeholder questions

Structuring a compelling problem-solution narrative

Designing an effective pitch deck

Demo storytelling: showing, not just telling

Handling Q&A confidently

Mock pitch sessions with peer feedback

Refining timing and delivery

Polishing the prototype for demo day

Final checklist before presenting

Final prototype presentations

Feedback from mentors and guest reviewers

Next steps: scaling or continuing your project

Course wrap-up and resources for further learning