Mquery

Inspiration

We have identified a market that is often overlooked and that is, the market of knowing consumer's data. A business owner who wants to gather analytics about their customers can now do so with Mquery, an AI product that gathers keywords to target customers efficiently.

What it does

A business owner submits a business proposal on a text prompt which is fed to Gemini API's AI agent that queries the Melissa ConsumerData dataset with the Melissa API. Then, this query outputs the target customers' contact information like name, phone number, and address.

How we built it

We used a React frontend and Python backend. We ranked and got the most relevant categories by prompting Google's Gemini model. Then, we filtered Melissa ConsumerData dataset with the Pandas library.

Challenges we ran into

Connecting the frontend to the backend.

Accomplishments that we're proud of

This was the first hackathon that most of our teammates have participated in, therefore it was an accomplishment to experience the full breadth of the hackathon experience.

What we learned

We have learnt the volatility of LLM and how hard it is to accurately create a useable prompt, additionally we learnt an amazing lesson in time management.

What's next for Mquery

Our next goals is to acquire more users and add additional metrics using the dataset to provide a more personal analysis of the data to the relevant customers. Additionally we wish to further our cause for women empowerment.