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Nowadays, every training you undergo ends with a Capstone project.
The idea sounds great on paper: apply what you’ve learned in a final hands-on project. But in reality?
Capstone projects have become a checkbox exercise. They’re often too generic, disconnected from your real work, and don’t really solve your challenges.
So we asked: Why not flip the model? Why not let you bring the problem you’re actually struggling with and solve it during the course?
That’s exactly why we introduced the Bring Your Own Problem Statement (BYOPS) approach.
Here’s what we believe:
You joined the course for a reason. You had a real challenge at work. So why waste time building a dummy project when you can solve the actual problem that brought you here?
Let’s look at how some of our learners turned their real-world problems into real-world solutions:
The Problem: In his company, whenever something broke, engineers would spend 15–20 minutes digging through logs to figure out what went wrong.
The Idea: What if an AI could do that in seconds?
The Solution: Hari built a smart LLM-powered tool using a RAG pipeline. Logs were stored in a vector database, and the AI would instantly retrieve and analyse logs from the time of the incident. Engineers now get insights even before they arrive on the scene.
The Problem: LLMs can write SQL queries, but they usually assume fake tables and columns. That doesn’t work in the real world.
The Idea: What if the AI knew the actual database schema?
The Solution: Vinay created an AI SQL Agent that uses the real schema as a tool. The agent explores the schema, picks the right tables and columns, and then generates the exact query needed, no assumptions, just accurate results.
The problem statement might look simple and straightforward today but it was not back then. Kanti needed a chatbot that could answer questions based on her company’s own documents.
The temporary solution they designed was to send the information along with the prompt. This solution was working fine but obviously has scalability issues.
He built a full RAG pipeline so the bot could dynamically fetch relevant internal info.
It was a leap forward in making LLMs truly useful for the workplace, back when it was still new.
The Bottom Line
At “AI ML etc.”, we don’t do things for the sake of tradition. If something doesn’t add real value, it goes, and Capstone Projects were on the chopping block.
Instead, we help you solve real problems. Your problems. Because that’s what actually matters.
Join our course. Bring Your Own Problem Statement. Solve it. We’ll be right there with you.
See you in class.
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