CRM Platform with AI Integration
This project is a complete CRM platform developed to streamline lead management, sales processes, and customer relationships. The system combines traditional CRM functionality with advanced AI integration, real-time notifications, and comprehensive automation. Built with React 19, Django 5, PostgreSQL with pgvector, and supporting both cloud-based and local AI providers.
2 months
Development Time
Solo project
Collaboration
Fullstack
Architecture
AI-Powered
Features
The Challenge
Managing leads, customers, and sales processes manually is time-consuming and inefficient. Many companies use multiple separate tools for different parts of the process, leading to fragmented data, lost information, and difficulties getting an overview of the entire sales process. The challenge was to create a unified system that combines traditional CRM functionality with modern AI capabilities while maintaining performance and scalability.
My Approach
I developed a fullstack CRM platform using React 19 and Django 5, with PostgreSQL and pgvector for AI-powered semantic search. The system is built with a modular architecture, making it easy to customize and adapt for different businesses. Each component is designed as an independent module, which simplifies both development and maintenance. The system includes a provider abstraction layer supporting both cloud-based (OpenAI) and local AI (LM Studio) providers. Real-time updates are handled through Django Channels with Redis, while background jobs use Celery. The platform features comprehensive automation and advanced analytics with caching for optimal performance.
Technical Features
Built with modern technologies and best practices for reliability, performance, and scalability
AI Integration
Provider abstraction supporting OpenAI API and LM Studio with RAG pipeline for context-aware suggestions
Vector Search
pgvector integration for semantic search across leads, deals, projects, and activities using 1536-dimensional embeddings
Real-time Updates
Django Channels with Redis Channel Layer for WebSocket-based real-time notifications and updates
Background Processing
Celery with Redis broker for asynchronous task processing including AI generation
Performance
Redis caching for analytics endpoints, database indexes, and optimized queries for scalability
Modular & Customizable
Modular architecture enables easy customization for any business, simplifying both development and maintenance
Key Features
Lead and customer management with pipeline stages
Deal management with automatic conversion to projects
Activity tracking and timeline
AI-powered semantic search with pgvector
Similar leads detection using vector similarity
Next Best Action (NBA) recommendations
Workflow automation engine
Real-time WebSocket notifications
CRM analytics dashboard with caching
Visitor statistics with GA4 integration
SEO analytics with PageSpeed Insights
Technologies Used
Project Impact
The CRM platform provides a complete solution for lead management, sales processes, and customer relationships. With AI integration, companies can leverage semantic search and intelligent recommendations to streamline their sales processes. The system's modular architecture makes it straightforward to adapt the platform for any business, regardless of industry or size. This modularity also simplifies ongoing development and bug fixes, as each component can be updated independently without affecting the entire system. The system's flexibility in supporting both cloud-based and local AI allows companies to choose the solution that fits their needs and budget. Real-time notifications and comprehensive automation reduce manual work and increase productivity.
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