Most people who take online AI courses stop at the theory. You finish the modules, collect the certificate, and then move on — but you never actually build something that solves a real problem. I don’t want to fall into that trap.

Over the past few months I’ve completed the IBM professional certificate in data science and progressed with AI development. I’ve gained some strong foundations, but the reality is that knowledge doesn’t stick unless you put it into practice.

That’s why I’ve set myself a new challenge: to build an AI product end-to-end.

It’s a daunting step. I don’t have a background in software engineering or AI delivery, so I know there will be plenty of challenges. But with the tools available today, along with the support of my network and the wider AI community, I believe it’s possible.

The Project

I’m building an enterprise-ready vertical AI assistant platform with four main capabilities, each designed to address common business needs:

1. Booking and diary management
   • A Postgres-compatible backend that syncs with Google Calendar
   • The scheduling and operations backbone for the assistant
2. AI FAQ bot
   • A retrieval-augmented generation (RAG) model with confidence thresholds and human handoff
   • Designed to handle queries transparently and reliably
3. Sentiment dashboard
   • Tracks employee or customer sentiment
   • Links insights directly to actions, such as alerts or workflows
   • Provides leadership with visibility on satisfaction and risk signals
4. Forecasting engine
   • Time-series forecasting using Prophet
   • Produces operational recommendations, not just predictions
   • Helps with planning demand, staffing, or campaign timing

Why This Matters

AI adoption in businesses is often slowed by generic tools that don’t adapt to real problems. I want to build something practical: a platform that saves time on repetitive tasks, surfaces insights from feedback, and supports better decisions through forecasting.

The Plan

I’ll be dedicating 10–15 hours a week to this project. My roadmap looks like this:

• Months 1–2: Build a demo-ready MVP
• Months 3–4: Expand to full feature coverage
• Months 5–6: Prepare for pilots and external demos

The Goal

This is about more than software. My aim is to move from learning in theory to delivering in practice, building the technical skills to become an AI delivery specialist. I want to help businesses adopt AI in a way that genuinely improves how they operate.

It won’t be easy, but I’m looking forward to the challenge.

I’d love to hear your thoughts: If you were tackling this project, what would you focus on first? What pitfalls should I watch out for?

Thanks for reading.