Xeno CRM

A modern, AI-powered Customer Relationship Management (CRM) platform built with Next.js, designed to help businesses manage customers, campaigns, and orders with advanced analytics and automation.

---

Table of Contents

• Project Overview
• Live Demo
• Demo Video
• Features Implemented
• Tech Stack Used
• Architecture Diagram
• Getting Started
  • Prerequisites
  • Installation & Setup
• AI Integration (Groq LLM)
• Known Limitations & Assumptions
• Future Scope

---

Project Overview

Xeno CRM is a full-stack CRM solution that enables businesses to:

• Upload and manage customer and order data
• Create and track marketing campaigns
• Analyze sales and customer engagement with AI-powered insights
• Use natural language queries and AI-generated campaign messages

---

Live Demo

Live Demo Link

---

Demo Video

[Watch the Demo]

---

Features Implemented

• User Authentication: Sign up, sign in (email/password & Google OAuth)
• Dashboard: Visual stats, quick actions, and campaign summaries
• Campaign Management: Create, filter, and track campaigns with AI-assisted message generation
• Order Management: Upload CSVs, preview data, and analyze product sales
• Customer Management: Import, filter, and manage customer data
• AI Insights: Use Groq LLM for natural language analytics and campaign message enhancement
• Responsive UI: Modern, card-based, mobile-friendly design
• Role-based Navigation: Dynamic navbar for authenticated and guest users
• API Documentation (Swagger):
  Comprehensive and interactive API documentation is provided using the Swagger (OpenAPI) standard.
  The swagger.json file describes all available endpoints, request/response schemas, and authentication methods.
  You can view and test the API directly using Swagger UI by importing the swagger.json file from the public directory.

---

Tech Stack Used

• Frontend: Next.js (App Router), React, Tailwind CSS, Shadcn UI, Chart.js
• Backend: Next.js API Routes, MongoDB, Mongoose
• Authentication: NextAuth.js (Credentials & Google)
• AI Integration: Groq LLM API
• CSV Parsing: PapaParse
• Other: React Query Builder, React Toastify

---

Architecture Diagram

---

🔐 Authentication: Users log in via Google OAuth2.

🖥️ Frontend: UI for Dashboard, Campaign Creation, and History. Interacts with the backend to view data and trigger campaigns.

🧠 Backend (API): Manages campaigns, orders, customers, and delivery receipts.

🗃️ Data Store: MongoDB stores customers, campaigns, and message logs.

📡 Vendor API: Simulates message delivery and sends status updates.

---

Getting Started

Follow these instructions to set up and run the project locally.

---

Prerequisites

• Node.js (v18.x or later recommended)
• npm (v9.x or later) or yarn
• MongoDB instance (local or cloud, e.g., MongoDB Atlas)
• Google Cloud Platform project with OAuth 2.0 credentials enabled
• Groq LLM API Key (for AI features)
• (Optional for advanced features) Redis instance

---

Environment Variables

1. Copy .env.example to .env in the project root.
2. Fill in all required values:
   • NEXTAUTH_SECRET
   • NEXTAUTH_URL
   • GOOGLE_CLIENT_ID
   • GOOGLE_CLIENT_SECRET
   • MONGODB_URI
   • GROQ_API_KEY
   • CALLBACK_URL

---

Installation & Setup

1. Clone the repository:

   https://github.com/abhinav2805-ux/XENO.git

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev
   # or
   yarn dev

The app should start on http://localhost:3000.

---

AI Integration

AI-Driven Message Suggestions

The platform integrates with Groq LLM to provide AI-powered message suggestions and analytics for campaigns.

• Campaign Message Generation:
  When creating a campaign, users can click a button to "Enhance Message with AI".
  The frontend sends the campaign name, description, and user-written message to a Next.js API route (/api/llm/suggest-message).
• Order/Product Insights:
  Users can ask natural language questions about their uploaded orders (e.g., "Which product has the maximum sales?").
  The frontend sends the prompt and the relevant uploadId to /api/llm/product-analysis, which uses Groq LLM to generate insights.
• How it works:
  The backend constructs a prompt and calls the Groq LLM API using your GROQ_API_KEY from .env.
  The generated message or insight is returned and displayed in the UI.

---

Known Limitations & Assumptions

• Google Auth:
  Currently, Google OAuth is implemented for authentication, but account linking between Google and credentials (email/password) is not automatic. Users must use the same method they registered with.
• AI Features:
  AI integration is focused on campaign message enhancement and product/order analytics. Other advanced AI use cases (e.g., natural language to segment rules, performance summarization) are not yet implemented.
• Error Handling:
  Basic error handling is present, but more granular user feedback and comprehensive backend logging could be improved.

---

Future Scope

• Multi-Channel Delivery:
  Extend delivery capabilities beyond simulated SMS to include email, WhatsApp, push notifications, and in-app messages.
• Advanced Dashboard:
  Build a dashboard with live tracking of campaign performance, customer engagement, and delivery heatmaps.
• More AI Features:
  Integrate additional AI-powered features such as natural language to segment rules, MongoDB-powered message suggestions, and campaign performance summarization.
• Enhanced UX/UI:
  Add more interactive and user-friendly elements to the interface.
• Automated Testing:
  Implement comprehensive automated testing (unit, integration, end-to-end).
• Improved Error Handling:
  Enhance error handling and system logging for better reliability and maintainability.

---

Thank you for checking out Xeno CRM!