MemoMate

A web application that streamlines note-taking and summarization powered by AI. Upload documents, create AI chatbots from your notes, and generate diagrams via natural language.

Features

• Note Workspaces — Hierarchical tree-based file/folder organization
• Document Upload — Support for PDF, DOCX, and Excalidraw formats
• AI Chatbot Creation — RAG-based chatbots from uploaded notes
• Diagram Generation — Natural language to Mermaid syntax (flowcharts, Gantt charts)
• Whiteboard — Excalidraw-based collaborative drawing
• Command Bar — kmenu-powered quick actions
• Shareable Bot Links — Create bots from notes and share via unique URLs
• Admin Dashboard — Site metrics, token usage, and user engagement

Technical Highlights

• LangChain + LangGraph for RAG workflow orchestration
• Faiss vector store for similarity search
• OpenAI GPT-4o-mini for chat and diagram generation
• Excalidraw integration for whiteboard functionality
• Redux Toolkit for state management
• AWS EC2 deployment with GitHub Actions CI/CD

Tech Stack

Architecture

User uploads documents (PDF, DOCX)
         ↓
Document Parser (pdf-parse, mammoth)
         ↓
Text Splitter (RecursiveCharacterTextSplitter)
         ↓
Embedding Generator (OpenAI)
         ↓
Faiss Vector Store
         ↓
User chats → Retrieve relevant chunks → LLM generates response

Media Bot

An AI-powered media planning platform with a conversational interface for campaign optimization. Combines LLMs with mathematical optimization to maximize ROI across marketing channels.

Features

• Conversational AI Interface — Stage-based chat that gathers campaign requirements (budget, objectives, audience, channels, duration)
• Dataset Upload — Drag & drop CSV/Excel files with historical performance data
• Budget Optimization — SciPy SLSQP algorithm for optimal channel allocation
• ROI Analysis — Automatic calculation of cost efficiency and audience reach metrics
• Channel Support — Meta, Snapchat, Google, TikTok (expandable)
• Session Persistence — SQLite-based conversation history

Technical Highlights

• Together AI (Llama 3.1 70B) for natural language understanding and plan generation
• SciPy optimization for mathematical budget allocation
• Pandas for data processing and analysis
• Framer Motion for smooth UI animations
• Glassmorphic design with dark theme

Tech Stack

How It Works

1. Requirements Gathering — Chat collects budget, objectives, target audience
2. Data Analysis — Upload historical performance CSV/Excel
3. Optimization — SciPy finds optimal budget split across channels
4. Plan Generation — AI creates detailed channel-specific recommendations

Mithaq Management System

An enterprise-grade Lead Management System designed for educational institutions to centralize, automate, and optimize their lead generation and conversion pipeline.

Features

• Lead Lifecycle Management — Complete tracking from NEW → CONTACTED → FOLLOW_UP → REGISTERED/NOT_REGISTERED
• Automated Distribution — Round-robin algorithm with database-level locking to prevent race conditions
• SLA Monitoring — Automated tracking of initial contact timeframes with breach detection
• Role-Based Access Control — Four user roles (Admin, Manager, Moderation, Sales) with different permissions
• Real-Time Synchronization — WebSocket-based instant updates across all connected clients
• Analytics Dashboard — Live KPIs, conversion funnel analysis, and agent productivity metrics
• Multi-Channel Notifications — Push notifications, WhatsApp integration, and email alerts

Technical Highlights

• 35,000+ lines of TypeScript and SQL
• 15+ database tables with optimized relationships and Row-Level Security
• 100+ concurrent users with sub-100ms response times
• 46x performance improvement through query optimization
• Supabase backend with PostgreSQL, Edge Functions, Auth, and Real-time subscriptions

Tech Stack

Architecture

Built with enterprise best practices: composite indexes, materialized views for dashboards, PostgreSQL cron jobs for automation, and optimistic concurrency control for conflict detection.