Business Problem

SurePeople is a leading provider of advanced platforms designed to empower individuals, teams, and organizations through people science. Powered by cutting-edge machine learning and the Prism psychometric algorithm, their platform delivers valuable insights into human behavior to elevate team performance.

• Growing demand for more personalized and scalable solutions as businesses expand.
• We need to enhance the user experience to stay competitive.
• Pressure to deliver more targeted insights and actionable recommendations. 
• Integrating advanced voice interaction capabilities is required to meet evolving customer expectations. 

Solution

The proposed solution is a comprehensive, end-to-end approach that includes data preparation, model development, prompt design and engineering, and the integration of advanced text-to-voice and voice-to-text capabilities.

Key use cases identified by SurePeople that this solution addresses include:

Coaching for Team Leaders: Leverage Lyzr's Content Re-synthesizer SDK, powered by RAG, to provide targeted coaching guidance for team leaders. 

Harvard Content & SurePeople Practices Mapping: Apply Lyzr’s Content Tagging SDK with RAG-powered Hybrid Search for effective content categorization and tagging. 

Conversational Interactions: Use Lyzr’s RAG-based SDK for intelligent conversations.

RLHF Fine-Tuning SDK: Integrate RLHF to improve GPT model performance with human feedback.

Meetings Module: Use the same SDK to offer customized suggestions for various meeting scenarios, which RAG enables. 

Website QA Bot: Deploy Lyzr’s QA Bot SDK, utilizing RAG to query SurePeople website data efficiently. 

Voice Bot: Add transcription, summarization, and text-to-speech for enhanced user experience.

Architecture

• Data Sources: Primary Staged DataSource, Coaching Guides, Meeting Module, Static DataWarehouse, Group Framework, Prism Profiles, Meeting Framework. 
• Data Ingestion: Webhooks, Airflow, Amazon S3, EC2 Containers.
• Data Processing: Data Processing APIs, Real-time Processing, RAG Pipeline, Lyzr SDKs. 
• Primary Data Storage: Amazon S3, MongoDB (for Structured and Semi-Structured Data), MongoDB Atlas (for Vector DataStore). 
• Secondary Data Storage: Static Data Warehouse (MongoDB).
• Natural Language Processing: OpenAI (for Query Extraction APIs, Generative AI). 
• AI Models: Query, Output, User Metadata.
• API Gateway: Amazon API Gateway for managing API calls and integrating with different components. 
• Webhooks: For triggering actions based on events. 
• Microservices Architecture: The architecture seems to be designed as a collection of microservices, each responsible for a specific function.
• Data Flow: Data flows from data sources through ingestion, processing, and storage components, eventually being used by AI models and applications.
• Real-time Processing: The use of real-time processing indicates the need for immediate data analysis and response.