Client Background

  • Client Name: Confidential (Internal / SaaS)
  • Industry Type: AI / Customer Support Automation
  • Products & Services: Enterprise AI Knowledge Base
  • Organization Size: 100+
  • About Client: A technology-focused entity streamlining internal support operations via AI-driven conversational interfaces.

The Problem The client required a secure, user-friendly interface to interact with their underlying AI/Langchain database. The existing setup lacked a unified frontend and robust authentication, exposing the chatbot endpoints to unauthorized access and making it difficult for non-technical staff to utilize the bot.

Our Solution I engineered the full-stack wrapper for the chatbot system using Node.js for the backend and React for the frontend.

  • Security: Implemented MSAL (Microsoft Authentication Library) to ensure only authorized corporate users could access the bot.
  • Integration: Built a lightweight Node.js API layer to securely route user queries to the Langchain database service, decoupling the frontend from the core AI logic.

Solution Architecture

  • Client Layer: React.js Single Page Application (SPA).
  • Auth Layer: Azure AD via MSAL for token validation.
  • API Layer: Node.js/Express Middleware to sanitize inputs.
  • Data Layer: Connection to existing Langchain/Vector DB service.

Deliverables

  • Fully functional React Frontend with Chat UI.
  • Secure Node.js Backend API.
  • MSAL Authentication Integration Documentation.

Tech Stack

  • Framework used: React.js, Express.js
  • Language/techniques used: JavaScript/TypeScript, REST API
  • Models used: Integration with Langchain (LLM Interface)
  • Skills used: Full Stack Development, OAuth/MSAL Implementation
  • Databases used: Vector DB (via API)
  • Web Cloud Servers used: AWS EC2 (Containerized)

What are the technical challenges faced during project execution

  • Handling token expiration and seamless refresh cycles using MSAL without disrupting the user chat session.
  • Streaming real-time responses from the Langchain backend to the React frontend to minimize perceived latency.

How the Technical Challenges were Solved

  • Implemented an Axios interceptor in the React app to auto-renew auth tokens silently in the background.
  • Utilised Server-Sent Events (SSE) in the Node.js layer to stream text chunks from the AI model to the client immediately as they were generated.

Business Impact

Delivered a secure, production-ready interface that enabled internal teams to utilize the chatbot safely, protecting sensitive corporate data through enterprise-grade authentication.

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