Polaris

Polaris is a web-based application developed to enhance customer support services by utilizing real-time speech-to-speech capabilities. The application aims to streamline customer interactions by converting spoken language input into responses powered by machine learning models, providing a faster and more natural support experience for both users and agents.

Next.js: Utilized for server-side rendering and building the front-end, offering a fast, efficient, and scalable user experience.

Flask: Employed for back-end processing and API development, managing the data flow between the front-end and the machine learning models.

Supabase : Used as the database solution for storing customer interaction data and logs, ensuring secure and scalable data management.

Machine Learning Engineering: Deployed machine learning models to process speech inputs and generate appropriate responses.

Full-Stack Development: Led the complete development lifecycle, including human interface and interaction design, back-end API development, and seamless integration of speech-to-speech functionality.

Speech Recognition & Response Generation: Integrated industry best practices as well as industry standard models including ElevenLabs and Deepgram to parse and process customer speech input into actionable responses, enhancing the accuracy and speed of customer support.

Real-Time Data Handling: Built efficient data pipelines using Supabase to store and retrieve customer support interactions in real-time, ensuring data persistence and enabling future analytics.

Testing & Optimization: Ensured the application maintained low latency during live customer support interactions, optimizing both the front-end and back-end for smooth, real-time speech exchanges.

The Polaris project resulted in a robust, real-time speech-to-speech customer support tool that increased the efficiency of customer-agent interactions. With its optimized machine learning models and scalable architecture, the application is set to significantly enhance customer experience by enabling quick, natural, and accurate communication.