Python Machine Learning Deep Learning Computer Vision Natural Language Processing
Gen AI Projects
Exploring the world of generative AI
Agentic AI

  • Multi-Agent GenAI Presentation Builder

    A multi-agent Generative AI system that transforms simple user prompts into professional presentations by coordinating tasks like slide structuring, content generation, visual selection, and formatting. Built using OpenAI GPT-4, LangGraph for agent orchestration, LangChain for tool integration, and Azure services for cloud-hosted deployment. The system supports end-to-end automation—from understanding user intent to generating .pptx files.

    OpenAI LangGraph LangChain Azure AI python-pptx Flask Generative AI Presentation Automation Multi-Agent Systems
    External Link Commming Soon

    Project Demo

    Comming Soon

  • Multi-Agent Conversational SQL Interface

    An intelligent multi-agent system that enables users to interact with relational databases through natural language. This project leverages OpenAI LLMs for intent detection and query generation, LangChain SQL agents for structured query translation, and LangGraph for orchestrating interactions between tools such as SQL executors, error handlers, and data visualizers. Beyond returning raw SQL results, the system uses Matplotlib to automatically generate charts (bar, line, pie, etc.) when appropriate, providing a more intuitive and visual understanding of the queried data. The entire solution is hosted on Azure, with secure access to Azure SQL Database.

    LangChain LangGraph OpenAI SQL Azure SQL Matplotlib NLP Conversational AI Database Agent Data Visualization Natural Language Interface
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  • Multimodal, Extensible ChatGPT-Like AI Assistant

    A full-stack, highly extensible LLM-powered chat application that replicates and surpasses the core functionality of ChatGPT. Users can chat with the assistant in real time while uploading images, documents, or custom prompts to get personalized outputs. The system supports advanced capabilities like code generation, content creation, web search, and image generation. Built with OpenAI GPT-4, orchestrated via LangGraph for tool and agent management, and extended through LangChain for dynamic prompt engineering, memory handling, and context switching. Real-time updates are enabled using WebSockets, and the platform is deployed via Azure App Services for scalable access.

    OpenAI LangGraph LangChain GPT-4 WebSockets Multimodal Code Generation Content Creation Image Generation Web Search Custom Chatbot LLM Azure App Services Real-Time AI
    External Link Comming Soon

    Project Demo

    Comming Soon

LLM Apps

  • Enterprise-Grade Retrieval-Augmented Generation System

    A sophisticated virtual assistant designed for enterprise-grade document and knowledgebase interaction using a multi-step Retrieval-Augmented Generation (RAG) pipeline. The system combines OpenAI GPT-4, LangChain, LangGraph, and Azure Cognitive Search, orchestrating a dynamic network of agents for retrieval, synthesis, and response generation. This assistant goes beyond traditional RAG by integrating custom-built components for intelligent data ingestion, structured chunking, and multi-tiered search. It provides grounded, source-linked answers and is optimized for deep document understanding and precise contextual response.

    OpenAI LangGraph LangChain GPT-4 Azure Cognitive Search Vector Search Web Scraping PDF Parsing Document Intelligence Form Recognizer Custom Chunking Multi-Agent RAG Enterprise AI Knowledge Retrieval Source Attribution
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  • AI-Powered LinkedIn Content Planner & Generator

    A smart content creation and scheduling tool designed to help professionals and marketers craft impactful LinkedIn posts with ease. Built using OpenAI GPT-3.5, LangChain components, and Azure OpenAI, the system generates tailored posts based on user-defined topics, tone, audience, and goals. Beyond content generation, it features a fully interactive Content Calendar where users can schedule, edit, and attach hooks to their posts for higher engagement. The platform also includes rewriting and optimization agents that enhance clarity, tone, and reach.

    OpenAI LangChain Azure OpenAI Content Generation LinkedIn Marketing Hook Generator Content Calendar Web App Prompt Engineering Social Media Automation
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  • AI-Based Meeting Notes Generator

    A lightweight NLP application that transforms uploaded meeting transcripts into clean, structured notes. Users can upload .docx or .vtt files, and the system extracts key information including a concise meeting summary, detailed discussion notes, action items, and a list of participants. Built using Python, Flask, and OpenAI models via LangChain, the application is streamlined for speed and usability without relying on agents or complex orchestration frameworks.

    Python Flask OpenAI LangChain NLP Meeting Summarization Transcript Parser Document Upload
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AI Apps

  • Flyer Designer

    AI-Powered Marketing Material Creation

    Automatically designs professional marketing flyers from content inputs, utilizing AI for layout, design elements, and visual composition.

    Generative Design Image Generation UI/UX
    External Link Comming Soon

    Project Demo

    Comming Soon

  • Text-to-Speech

    Natural Voice Synthesis Application

    Converts written text into natural-sounding speech with multiple voice options, language support, and customizable speech parameters.

    Speech Synthesis Audio Processing Web API
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  • Face Landmarks Webcam

    Real-time Facial Feature Detection

    Uses MediaPipe to detect and track facial landmarks in real-time through a webcam, enabling applications like expression recognition and AR filters.

    MediaPipe Computer Vision JavaScript
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Azure AI Projects

  • Document Insights

    Intelligent Document Analysis

    Leverages Azure Document Intelligence to extract, analyze, and organize information from various document types, providing meaningful insights and structured data.

    Azure Document Intelligence OCR Data Extraction
    GitHub YouTube External Link Comming Soon

  • Face Detection

    Advanced Facial Recognition System

    Utilizes Azure Face API to detect, identify, and analyze human faces in images and video streams, with features for emotion recognition and demographics.

    Azure Face API Computer Vision Biometrics
    External Link Comming Soon

    Project Demo

    Comming Soon

  • Speech Lab

    Voice Recognition & Processing Platform

    A comprehensive speech processing platform using Azure Speech Service for voice recognition, transcription, translation, and voice synthesis.

    Azure Speech Service Speech-to-Text Language Translation
    GitHub YouTube External Link Comming Soon

  • Object Detection

    Real-time Object Recognition

    Implements Azure Computer Vision for real-time object detection, identification, and tracking in images and video streams.

    Azure Computer Vision Object Recognition Video Analysis
    External Link Comming Soon

    Project Demo

    Comming Soon

  • AI Search

    Intelligent Knowledge Base Search

    A powerful search platform built on Azure AI Search that provides semantic search capabilities across diverse content types with relevance ranking and filtering.

    Azure AI Search Semantic Search Content Indexing
    External Link Comming Soon

    Project Demo

    Comming Soon

Advanced Logging

I build systems with Granular, High-Fidelity Logs

From session initialization to model loading and document processing, my systems log every critical operation with precision-timestamped entries and rich context. This ensures full traceability, faster debugging, and transparent monitoring in production environments—empowering teams to move fast and with confidence.

Project Logs Visualization

Architecting Advanced AI Systems

I design and build sophisticated AI architectures that scale from prototype to production. With a focus on modularity, maintainability, and performance, my systems integrate seamlessly with existing infrastructure while providing flexibility for future enhancements and extensions.

RAG Architecture
LLM Architecture
ML Pipeline
AI System Design
Deployment Architecture
RAG Architecture
LLM Architecture

Prompt Engineering

Expert LLM Prompting Techniques

I craft precise, effective prompts that unlock the full potential of large language models. These carefully engineered prompts drive consistent, high-quality outputs across various applications—from content generation to complex reasoning tasks.

RAG Context Enrichment Prompt
This prompt template optimizes retrieval-augmented generation by providing precise instructions on how to use retrieved context.
RAG Context-Aware Information Retrieval
Optimized for: GPT-3.5/4, Claude, LLaMA
You are an AI assistant with access to contextual information. Your task is to answer the user's question accurately based on the provided context. Context: {context} Question: {question} Instructions: 1. Read the provided context carefully. 2. Answer the question based ONLY on the provided context. 3. If the context doesn't contain enough information to answer fully, acknowledge this limitation. 4. Do not fabricate information or draw from knowledge outside the provided context. 5. Cite specific parts of the context where appropriate. 6. Format your response for clarity and readability. Answer:
Few-Shot Chain-of-Thought Reasoning
This prompt template improves complex reasoning by showing the model examples of step-by-step thinking processes.
Chain-of-Thought Few-Shot Learning Step-by-Step Reasoning
Optimized for: GPT-4, Claude 2
I'm going to give you a complex reasoning problem. Please solve it step-by-step, showing your thinking process. Example 1: Problem: If John has 5 apples and gives 2 to Mary, then buys 3 more and gives half of all his apples to Susan, how many apples does John have left? Reasoning: 1. John starts with 5 apples 2. He gives 2 to Mary, leaving him with 5 - 2 = 3 apples 3. He buys 3 more, giving him 3 + 3 = 6 apples 4. He gives half of his 6 apples to Susan, which is 6 Ă· 2 = 3 apples 5. After giving 3 apples to Susan, John has 6 - 3 = 3 apples left Answer: John has 3 apples left. Example 2: Problem: A train travels at 60 mph. How long will it take to travel 150 miles? Reasoning: 1. We know that time = distance Ă· speed 2. Time = 150 miles Ă· 60 mph 3. Time = 2.5 hours Answer: It will take 2.5 hours. Now solve this problem: Problem: {problem} Reasoning:
Structured Code Generation Prompt
This prompt template ensures high-quality code generation with proper structure, error handling, and documentation.
Code Generation Software Engineering Documentation
Optimized for: GPT-4, Codex, Claude
Generate clean, efficient, and well-documented code based on the requirements below: Task: {task_description} Requirements: - Programming language: {language} - Include proper error handling - Follow best practices and coding standards for {language} - Add comments explaining complex logic - Include documentation for functions/methods Additional context: {additional_context} The solution should be: 1. Maintainable and follow SOLID principles 2. Properly structured with logical organization 3. Optimized for performance where appropriate 4. Secure and handle edge cases Your response should include: 1. A brief explanation of your approach 2. The complete code solution 3. Instructions for how to use/run the code 4. Any assumptions you made
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Have a project in mind? Let's get to work. 👋📬