Business Problem

GroupSolver faced key challenges in improving its data handling capabilities:

• Needed AI-enhanced data extraction and summarization to generate efficient insights.
• Sought to improve data summarization quality to provide more actionable insights.
• Required a streamlined, efficient information processing solution to enhance research workflows.

About GroupSolver

GroupSolver is an innovative marketing research and online survey platform that helps large retail clients gather faster, more reliable insights. Using its proprietary tools, GroupSolver aids businesses in brand and market research through data-driven ideation, evaluation, and synthesis.

Solution

To help GroupSolver overcome these challenges, GoML designed a 5-week Proof of Concept (POC) leveraging AWS infrastructure. Key components of the solution included:

AI-Driven Data Collection: Developed serverless data collection functions using AWS Lambda to fetch relevant data from GroupSolver’s repository, ensuring data accuracy and relevance.

Testing and Validation: Conducted comprehensive testing to validate the accuracy and effectiveness of AI-generated insights, ensuring high-quality output.

LLM Model Development: Fine-tuned Large Language Models (LLMs) using AWS Bedrock with Claude V3 to analyze data and produce insightful summaries.

Deployment: Implemented a streaming API for efficient data flow management, with Python for scripting and automation.

System Integration: Integrated AI models seamlessly within GroupSolver’s AWS environment, enabling smooth operation within their existing platform. 

Architecture

• AWS Cloud InfrastructureCloudWatch:
  Monitors and logs application and infrastructure metrics, providing insights to detect performance issues and improve reliability.
• Networking and Security
  VPC (Virtual Private Cloud):
  Public Subnet
  Web Interface: Hosts the publicly accessible web application for end-users.
  API Gateway: Acts as the main entry point for HTTP requests, routing incoming traffic to internal services within the VPC and ensuring secure access control.
  Private Subnet: Contains backend services that are not directly accessible from the public internet, enhancing security and access control.
• Processing and Data Management
  Lambda: Executes serverless functions for backend processing, automatically scaling to meet demand while reducing infrastructure management overhead.
  ECR (Elastic Container Registry): Stores Docker container images, enabling seamless deployment of containerized applications and services within the architecture.
  Prompt Engineering Module: Manages and processes input prompts for the AI system to optimize interaction with the language model.
• AI Model and API
  Anthropic Claude v3: An advanced large language model (LLM) integrated within the architecture, capable of processing natural language prompts generated by the prompt engineering module, handling complex tasks, and generating responses.
  FastAPI: A lightweight and high-performance Python framework used to build RESTful APIs, enabling interaction between different components of the application.
• External Integration and Source Control
  Bitbucket: Manages source code and integrates with the deployment pipeline, enabling version control, collaboration, and streamlined code deployment.
• Security and Access Control
  WAF (Web Application Firewall): Protects the application from common web threats like SQL injection and cross-site scripting (XSS) by filtering and monitoring HTTP requests between users and the web interface.