Stop re-explaining your project to AI every session. Automatic context memory for Claude, VS Code, Cursor, and 13+ AI tools.

A sophisticated LangGraph-based agent that automates financial options analysis with real-time data from Polygon.io, smart caching, persistent memory, and professional-grade analysis. Built for traders, analysts, and developers who need intelligent options data processing

An AI-powered tool that generates Cypress and Playwright end-to-end tests from natural language requirements using OpenAI's GPT-4, LangChain, LangGraph workflows, and vector store pattern learning.

AI-powered support copilot for ticket classification and query resolution. RAG, Chroma DB, Streamlit. Atlan AI Engineer Internship.

Semantic Retrieval Engine for Contrasting Ideas and Opposing Viewpoints.

A realtime Concierge Agent made using Pipecat and LanGraph with COT reasoning.

Local RAG chatbot that answers questions about pizza restaurant reviews using LangChain, Ollama, and Chroma vector database - fully offline with no API dependencies

Agentic RAG System – A multi-agent Retrieval-Augmented Generation (RAG) system built with CrewAI for business intelligence and document analysis. It integrates ChromaDB for document storage and retrieval, real-time web search, and specialized agents for code execution and visualization, enabling automated trend analysis and insights generation

Multimodal AI assistant with RAG, web search & email generation – Built for Esprit

An AI-powered YouTube Content Synthesizer using RAG (Retrieval-Augmented Generation). Built with Streamlit, LangChain, and Gemini 2.0 to transform video transcripts into structured notes, important topics, and an interactive chatbot.

A fully local RAG pipeline that answers natural language questions about movie reviews. Uses Ollama for embeddings + local LLM, Chroma for the vector store, and LangChain to retrieve, summarize, and generate answers.

A Retrieval-Augmented Generation (RAG) Web App powered by LangChain and Flask. Live Demo: https://rag-chatbot-1090137452187.us-west1.run.app/

Powerful book recommender using Large Language Models (LLMs) and semantic vector search. It analyzes book descriptions to recommend contextually and emotionally similar titles. Includes zero-shot classification and an interactive Gradio interface for seamless user experience.

Advanced RAG System for Intelligent Document Querying. Built with Python, FastAPI, and React, leveraging Gemini Pro and ChromaDB for high-precision context retrieval from PDFs and codebases.

AI-powered book recommendation system using semantic search, sentiment analysis, and interactive web interface. Find books through natural language queries and emotional tone filtering.

Adaptive RAG is an advanced retrieval-augmented generation system that intelligently combines dynamic query analysis with self-corrective mechanisms to choose the most effective strategy for answering user queries.