Gemi – Voice AI Database Agent

Gemi is a Voice AI Database Agent that lets users interact with databases using natural speech. Powered by Gemini AI, it converts voice queries into database operations and returns results instantly.

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Inspiration

Interacting with databases usually requires writing technical queries and understanding database syntax. Many users, analysts, and beginners struggle with this barrier.

The idea behind Gemi – Voice AI Database Agent is to make database interaction simple and natural using voice. Instead of writing queries manually, users can simply speak their request and let AI translate it into database operations.

What it does

Gemi allows users to interact with databases using natural speech.
The system captures the user's voice input, processes it using Gemini AI, converts it into database queries, and returns the results.

Users can ask questions like:

  • "Show all merchants"
  • "Find merchant with ID 1001"
  • "List all active records"

The AI agent understands the request and retrieves the relevant information from the database.

How we built it

The system is built using a combination of AI, backend APIs, and cloud deployment.

  1. The user speaks a query using the browser voice interface.
  2. The speech is converted to text.
  3. Gemini AI interprets the request and determines the database operation.
  4. The backend API processes the request.
  5. MongoDB retrieves the relevant data.
  6. The results are returned to the user interface.

The application is deployed on Google Cloud Run so it can be accessed publicly through a web browser.

Challenges we ran into

Building a voice-driven database interface required integrating multiple components including voice input, AI processing, database queries, and cloud deployment.

Ensuring smooth communication between the frontend, backend, and database was one of the main challenges. Deploying the application to the cloud and configuring the container environment was another key challenge that required careful setup.

What we learned

Through this project we learned how to integrate voice interfaces with AI models and databases. We also gained experience deploying containerized applications on Google Cloud Run and connecting AI agents with real backend data systems.

What's next for Gemi

Future improvements could include support for more complex queries, better natural language understanding, additional database integrations, and advanced analytics capabilities.

The goal is to make database interaction as simple as having a conversation.

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