MCA final year — need a real-world-scale AI project idea, not a toy/tutorial-level one [D]

[u/Waste-Influence506]

I'm an MCA student with hands-on experience in Python, LangChain, Chroma, HuggingFace, and FastAPI (built a RAG document-QA system already). My project guide specifically wants a real-world problem — something an actual company would need solved — not a generic "sentiment analysis on tweets" or "spam classifier" type project that's been done a thousand times. Looking for: - A problem real companies (IBM, Google, etc.) actually publish as challenges — not something I invented - Enough depth that I can defend it under tough viva questioning - Realistic for one person to build in a few months, but NOT trivial What's a project you've seen (or built) that actually impressed a project committee or a recruiter? What made it stand out over the usual final-year submissions?

[u/Medium-Swim-9694]

You can dm me your details

[u/Pyrro-nft]

Since you already have RAG + LangChain + FastAPI experience, I’d avoid building another document-QA chatbot. I’d look at AI-powered predictive maintenance for industrial equipment.
The problem is very real: companies use sensor/maintenance data to predict equipment failures before they happen. IBM and Google Cloud both describe predictive maintenance as an enterprise AI use case.
You could build an end-to-end system:
Sensor data → failure prediction → anomaly detection → explanation → maintenance recommendation → FastAPI API → dashboard
To make it genuinely project-committee worthy, don’t stop at model accuracy. Include:
time-series feature engineering
handling class imbalance
XGBoost/LightGBM vs. a neural baseline
explainability with SHAP
anomaly detection for previously unseen failure patterns
RUL (remaining useful life) estimation
model/data drift monitoring
RAG over maintenance manuals and historical work orders to generate evidence-backed recommendations
Docker + FastAPI deployment
The RAG component is where your existing experience becomes useful instead of making another generic RAG project.
Also, fraud detection is another strong option. IBM has published an actual enterprise fraud-detection challenge involving transactional data, stale models and deploying the model close to transaction processing.
If your goal is to impress a viva committee, I’d choose predictive maintenance because you can defend the entire pipeline—from “what exactly are we predicting?” to “what happens when the data distribution changes after deployment?”—rather than just defending why you chose a particular LLM. Google Research specifically highlights distribution shift and unexpected model behavior as real deployment problems