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Letting Data Speak!
Case Study
AI-Powered Survey Analysis
About the Client
A leading employee feedback and analytics company that humanizes data to help organizations improve employee and organizational performance. The client specializes in providing comprehensive talent insight solutions through cloud-based technology platforms and advisory services, serving organizations of all sizes across various industries with their employee engagement, retention, and culture improvement initiatives.
Challenge
The client required a comprehensive enterprise-grade survey analysis platform to transform their employee feedback capabilities into actionable business insights. We were tasked with developing a comprehensive end-to-end solution that encompasses an intuitive self-service frontend, a robust serverless backend architecture, and an advanced AI-powered analytics engine. Key deliverables included designing a scalable AWS infrastructure with a unified API architecture and implementing secure authentication. The platform needed to support real-time organizational data discovery, intelligent document management, summary export functionality, streaming chat interfaces, and seamless integration between MongoDB and AWS services. Additionally, the project required establishing automated CI/CD deployment pipelines with Terraform infrastructure as code and implementing enterprise-grade monitoring and logging across all components.
Key Results
Reduced manual survey analysis time by 85% through automated AI-powered summarization with dynamic context enhancement
Increased survey insight accuracy by 70% through contextual document integration and demographic filtering
Reduced infrastructure complexity by 60% through consolidation from multiple Lambda functions to a single, endpoint-driven Lambda architecture
Solution
JashDS implemented a comprehensive production-ready AI survey summarization platform using AWS serverless architecture and advanced MLOps practices. The solution included:
Secure Authentication System - Implemented AWS Cognito to authenticate users.
AI Survey Analysis - Integrated AWS Bedrock with Claude Sonnet 4 for intelligent survey summarization with better accuracy than previous models.
Dynamic Context Enhancement - Enabled users to upload context documents and files in multiple formats (PDF, DOCX, XLSX) to enrich survey analysis with organizational background and provide more accurate, relevant insights.
Data Segmentation - Built an advanced demographic filtering system allowing users to segment survey data across multiple dimensions, including geography, departments, business units, and custom fields for precise targeted analysis.
Self-Service Frontend Platform - The Frontend Platform is designed to enable users to independently input contextual information, upload files for survey enrichment, and configure data segmentation parameters. The platform provides self-service capabilities for downloading surveys and maintaining a comprehensive history of generated survey summaries.
Real-Time Chat Interface - Developed streaming conversational AI allowing users to interact with survey insights through natural language queries with context awareness
Automated CI/CD Pipeline - Established a comprehensive CI/CD pipeline using Terraform infrastructure as code, AWS CodePipeline, and CodeBuild for automated testing, building, and deployment.
Project Diagram
Workflow Diagram
System Overview
Claude Sonnet 4 Advantage:
Technologies Used
AWS Bedrock (Generative AI)
AWS Lambda (Serverless Computing)
AWS API Gateway (REST API Management)
AWS Cognito (Authentication & Authorization)
AWS S3 (Static Website Hosting & Storage)
AWS CloudFront (Content Delivery Network)
AWS CodePipeline (CI/CD Orchestration)
AWS CodeBuild (Build & Deployment Service)
Terraform (Infrastructure as Code)
React.js (Frontend Framework)
MongoDB (Data Source)
Python (Backend Development)
Node.js (Frontend Build Process)
AWS Secrets Manager (Secure Configuration Management)
AWS CloudWatch (Logging & Monitoring)
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