AWS Case Study - A Korean Law Firm

Case Study of A Korean Law Firm on Cloud-Based IT Optimization: Enhancing Data Operations and Automating AI-Driven Business Support with SmileShark

Optimization of Business Process Enhancing Operational Efficiency Through AI-Driven Automation

40% Improvement in Curation Page Loading Speed Providing stable data delivery through CloudFront and S3 optimization

Overview

A Korean law firm, leveraged AWS Cloud to enhance its IT infrastructure. SmileShark provided technical consulting and architectural design for the implementation of a curation page system using AWS-based cloud solutions. This initiative contributed to building a scalable and reliable IT system.

Challenge

• Managing High Traffic and Data Processing:

  • Required an infrastructure capable of reliably handling increasing user requests for the curation pages and automated responses.

• Efficient Business Support System:

  • Needed an automation of initial inquiries, along with an AI-driven system to classify requests and streamline response automation.

• Ensuring Stability and Scalability:

  • Required an architecture capable of stable operation under high traffic conditions.

Solutions

💪 Strengths of SmileShark SmileShark ensured the stability and scalability of the IT infrastructure by providing customized architectural design through AWS cloud solutions and AI technology consulting.

• Implementation of AI and Search Systems:

  • Enhancing Data Management and Search System Performance by adopting an AWS-based RAG(Retrieval → Recommendation) structure and Large Language Model (LLM).

  • Proposing a data processing workflow that enables the automated conversion of large-scale PDF documents for efficient management.

• Providing a Technical Guide for LangChain-OpenSearch:

  • Providing guidelines on implementing and utilizing LangChain-OpenSearch-Retriever.

  • Assisting in implementing advanced search functionality by offering guidance on building an OpenSearch vector index.

  • Maximizing search efficiency and accuracy by analyzing keyword relationships using vector similarity.

• AWS Architecture Design and Optimization:

  • Improving page loading speed by 40% through Amazon S3 and CloudFront.

  • Ensuring real-time data processing and service stability with AWS Lambda and API Gateway.

  • Maintaining stable operations under increasing traffic by leveraging Auto Scaling and DynamoDB.

Outcome

• Enhanced IT System Performance

  • Improved data processing speed by 60% through the implementation of and AWS-based search system.

  • Minimized data loss and service disruptions by optimizing cloud infrastructure.

• Optimized Business Support System

  • Adopted an AI-powered business support system to automate inquiries and improve response speed.

  • Implemated an automated response system to enhance system processing efficiency.

• Strengthened Data Search and Management Efficiency

  • Enhanced search accuracy with LangChain-OpenSearch-Retriever implementation.

  • Leveraged vector-based search and relationship analysis to identify complex data correlations.

• Cloud Infrastructure for Digital Transformation

  • Supported IT system modernization through cloud and AI technologies.

  • Ensured long-term service scalability with continuous technical consulting.

Architecture

*This architecture is for illustrative purpose only and may not reflect the actual architecture.