AI-Powered Survey Analysis

An Employee Feedback and Analytics Company

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

We implemented a comprehensive production-ready AI survey summarization platform using AWS serverless architecture and advanced MLOps practices. The solution included:

  1. Secure Authentication System- Implemented AWS Cognito to authenticate users.
  2. AI Survey Analysis- Integrated AWS Bedrock with Claude Sonnet 4 for intelligent survey summarization with better accuracy than previous models.
  3. 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.
  4. 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.
  5. 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.
  6. Real-Time Chat Interface- Developed streaming conversational AI allowing users to interact with survey insights through natural language queries with context awareness
  7. 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.
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)
  • js (Frontend Framework)
  • MongoDB (Data Source)
  • Python (Backend Development)
  • js (Frontend Build Process)
  • AWS Secrets Manager (Secure Configuration Management)
  • AWS CloudWatch (Logging & Monitoring)
Summary

We transformed a leading employee feedback and analytics company's manual AI POC into a production-ready, self-service platform with intuitive dropdown interfaces and dynamic demographic filtering, reducing manual analysis time by 85% while improving user adoption by 95% through AWS serverless architecture. The solution leveraged AWS Bedrock with Claude AI for intelligent survey insights, implemented comprehensive CI/CD pipelines with Terraform infrastructure as code, and featured contextual document and system prompt integration.

Project Diagram
Workflow Diagram
System Overview
Claude Sonnet 4 Advantage:

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