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Empowering Organizations with AI: Revolutionizing People Science

Deveshi Dabbawala

November 21, 2024
Table of Content

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. 

Outcomes

50%
Enhanced user experience
60%
Improved team performance with personalized insights
45%
Time and resources saved