Business Problem

• Modern individuals face stress, career dilemmas, and relationship challenges but lack affordable, continuous, and accessible life coaching.
• Traditional coaching services are costly, limited by geographical constraints, and often lack historical context for long-term guidance.
• Users need a private, AI-powered solution that offers personalized, context-aware, and interactive coaching experiences.

About

A leading AI-driven solutions provider specializing in generative AI applications, leveraging advanced NLP and voice technologies to create hyper-personalized user experiences. The company focuses on building scalable, real-time AI solutions that enhance well-being and productivity.

Solution

GoML provided a cutting-edge AI-powered virtual life coach that delivers real-time emotional support, relationship guidance, and career advice through seamless voice interaction. The solution integrates advanced Generative AI capabilities to ensure context-aware, highly personalized coaching for users.

Conversational AI Engine: Built using Claude API (Anthropic) to generate empathetic, insightful responses for personalized coaching.

Real-Time Response Optimization:
• FastAPI / Flask for fast and scalable backend processing.
• Optimized API architecture ensures low-latency responses.

Voice Interaction System:
• AWS Transcribe for high-accuracy speech-to-text conversion.
• AWS Polly for natural, human-like voice synthesis.

Security & Privacy-First Design:
• End-to-end encryption for user interaction data.
• Access controls to ensure confidentiality and protection of personal insights.

Intelligent Context Retention:
• MongoDB with Vector Search for storing past interactions and retrieving relevant context.
• Retrieval-Augmented Generation (RAG) to personalize advice based on user history and patterns.

Architecture

• User Interaction Layer
  • User provides input via voice.
  • The system captures audio input using a microphone.
• Speech Processing Layer
  • Voice-to-Text Conversion:
      o Uses AWS Transcribe to convert speech into text.
• Prompt Processing & Context Retrieval Layer
  • Prompt Layer:
       o The system processes the text input.
       o Date-Time Provider fetches contextual information if required.
  • Context Retrieval & Memory:
      o Uses MongoDB with Vector Search to fetch relevant past interactions.
      o Implements Retrieval-Augmented Generation (RAG) to
  enhance AI responses.
• AI Response Generation Layer
  • Claude API (Anthropic):
      o Generates insightful, empathetic coaching responses.
      o Utilizes the retrieved historical data for personalization.
• Response Processing & Storage Layer
  • MongoDB Storage:
      o Stores user interactions as vector embeddings for future context.
• Text-to-Speech & Final Response Layer
  • Text-to-Voice Conversion:
      o Uses AWS Polly to generate natural-sounding speech responses.
  • Final Response Output:
      o Delivers the AI-generated coaching response back to the user as voice output.