The Story

The project began as an experimental initiative to integrate Google Cloud Platform (GCP) and Firebase in a non-traditional setup. Over time, it matured into a structured backend pipeline, Jbots, capable of handling multi-modal inputs like text and audio. The system processes user queries to generate AI-driven responses enriched with phonetic metadata that mimics the singer's speech patterns. It combines state-of-the-art technologies for seamless interaction and scalability.

Our client, a UK-based singer and AI enthusiast, envisioned a cutting-edge conversational assistant combining advanced AI and phonetic technology to deliver personalized, natural interactions for their audience.

Amit Jangid

Founder & CTO

Challenges

• > Converting text and audio inputs into structured data while preserving context and intent.

• > Handling high-frequency queries with low latency.

• > Ensuring integrity across Firebase Realtime Database transactions.

• > Retrieving and formatting phonetic data efficiently.

• > Deploying a secure, scalable infrastructure using Terraform.

• > Tuning Google Gemini API for personalized, context-aware responses.

The Solution

Jbots leverages a modular backend powered by Google Cloud Run functions, Pub/Sub for asynchronous communication, and Firebase for secure storage. A Flask-based framework routes data between services, while a third-party phonetic API enriches the responses. Terraform automates the deployment process, ensuring scalability and robust authentication.

The Outcome

The solution delivered a fast, scalable, and secure backend pipeline capable of real-time conversational processing. By integrating AI-driven responses and phonetic metadata, Jbots enhanced user interactions, offering a seamless, personalized experience. The modular architecture ensures adaptability for future enhancements and expansions.