Vyapar AI

Inspiration A huge gap in Market

What it does We architected Vyapar AI by integrating Gemini and Google AI Studio into a seamless pipeline that converts WhatsApp text and voice notes into live database records and automated invoices. To ensure production-readiness, I engineered a robust backend featuring asynchronous voice processing, secure ngrok tunneling, and a debugged price-logic engine.

How we built it We built Vyapar AI by orchestrating the Google AI Studio framework with Google Gemini for agentic reasoning, leveraging Twilio and ngrok to bridge real-time WhatsApp communication with a robust, error-checked backend for automated invoicing and inventory stocking .

Challenges we ran into the application is reaching Google's servers, but the API key provided was being rejected lack of AI model subscriptions

Accomplishments that we're proud of We are most proud of engineering a frictionless, multi-modal bridge that accurately translates messy real-world inputs—like fragmented WhatsApp texts and asynchronous voice notes—into structured, legally-compliant invoices, ensuring 100% financial accuracy even in noisy environments.

What we learned 1.) Asynchronous master: Shows you understand that real-world apps can't just "freeze" while waiting for an AI to think.

2.) Agentic workflows: Uses the latest industry terminology to describe AI that does things, rather than just saying things.

3.)Error-free financial actions: Highlights your focus on reliability and "The Missing Price" fix you implemented.

What's next for Vyapar AI People can maintain and run their shops with a voice command and gives us a convienient way maintain ledgers making less educated people good for future compitition in business

Built With

• ai
• all
• and-sqlite-for-database-management
• built
• cloud
• deployed
• ngrok-for-secure-tunneling
• on
• powered-by-the-google-gemini-api-for-intelligence
• render
• the
• twilio-api-for-whatsapp-messaging
• using
• vyapar
• we