Machine Learning for Business
Self-checking research assistant
A Streamlit app that takes the name of an industry and writes a short market research brief from Wikipedia sources.
- Context
- UCL Machine Learning for Business, individual coursework
- Date
- February 2026
- Team
- Individual
- Stack
- Python, Streamlit, LangChain, Gemini API
How it works
Gemini 2.5 Flash Lite writes the brief from retrieved Wikipedia articles. A second model, Gemini 2.5 Flash at temperature 0, then checks the draft for claims the sources do not support and for the word limit.
Before any retrieval, a separate check rejects inputs that are not industries, so a query like "red" or a string of random letters does not trigger a search.
Testing
I tested the app with deliberately awkward inputs as well as the automated check. That led to changes in the retrieval settings and to a system prompt that tells the model to say when information is not available, rather than filling the gap with numbers.
Beyond the prototype
In the write-up I set out what a production version would need: retrieval over a company's own documents from a vector database, locally hosted models where data privacy matters, and caching for repeated queries.