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Multi-Domain RAG Chatbots with AI Agents

Designed and deployed multi-domain RAG chatbots fine-tuned with LoRA at Bain & Company. Integrated with n8n, LangGraph, and CrewAI for workflow orchestration and multi-agent collaboration, using MCP for context exchange across enterprise systems.

Bain & Company
2024
Improved enterprise knowledge management and automation workflows

Project Overview

This project involved building sophisticated AI-powered chatbots that could operate across multiple business domains at Bain & Company. The solution leveraged Retrieval-Augmented Generation (RAG) combined with fine-tuned Large Language Models using Low-Rank Adaptation (LoRA) for efficient model customization.

Key technical implementations: • Multi-domain knowledge base integration using vector databases • Agent orchestration with CrewAI for complex multi-step workflows • n8n integration for enterprise system connectivity • MCP (Model Context Protocol) for secure context exchange • Real-time performance monitoring and optimization • Scalable deployment using Docker and Kubernetes

The system achieved significant improvements in response accuracy and reduced manual processing time by 60% across various business functions.

Key Challenges

  • Managing context across multiple enterprise domains
  • Ensuring secure data access and compliance
  • Optimizing response latency for real-time interactions
  • Integrating with legacy enterprise systems

Outcomes & Impact

  • 60% reduction in manual query processing time
  • Improved response accuracy across all domains
  • Enhanced employee productivity and satisfaction
  • Scalable architecture supporting enterprise growth

Technologies Used

LangGraphCrewAILoRAn8nMCPLangChain

Company

Bain & Company

2024