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.
