This project involves using llamaindex Multi Agents concierge system and Qdrant vector database to customize the RAG application with user preferences over LLMs, Embeddings, Search types, and Reranking Models.

This project is a multi-agent system, it uses Amazon Bedrock AgentCore to create a proactive travel concierge that understands natural language. The system incorporates specialized agents for planning, booking, and real-time monitoring and demonstrates the use of tools to securely interact with external APIs.

AI Travel Concierge: A multi-agent orchestration system based on LangGraph and FastAPI. The architecture, powered by Groq (Llama-3.3-70B), uses the ReAct pattern for autonomous travel planning. It offers persistent user preferences, real-time API integrations (Google/Weather), and interactive mapping with SQLAlchemy. Data-driven smart logistics app

Multi-agent AI system for investment research - a personal investment research concierge for Indian stocks

A modern multi-agent AI concierge system that provides seamless hotel services through natural conversation.

Orchestrates meal planning, task management, and personalized recommendations Uses simulated agent responses for demonstration (no API calls required)

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A modular multi-agent disaster-response system using Planner → Worker → Evaluator architecture. Provides alerts, shelter info, and guidance through tool calls, session memory, and safe evaluation. Includes full project structure, observability, A2A protocol, and a runnable demo.

An evidence-based skincare concierge built using Google ADK and Gemini Flash. Multiple agents handle ingredient safety, evidence retrieval, routine generation, and calendar reminders. The system avoids diagnosis and provides simple, science-backed skincare guidance personalized to each user

AI-intensive capstone project implementing intelligent agents to solve complex tasks using machine learning and decision-making algorithms. Includes end-to-end implementation, experiments, and analysis in a reproducible notebook.

To build an end-to-end multi-agent system capable of managing real-time booking logic, user preference matching, and customer service inquiries within a unified interface

Multi-agent AI concierge system with planner, worker, evaluator agents

AI-powered travel concierge system with multi-agent architecture

AI-powered Multi-Agent Concierge System for Smarter Resource Assistance

ET AI Concierge - Multi-Agent AI system for Economic Times ecosystem

"Presentation on the rise and architecture of Al-powered concierge agents and multi agent systems."

The Adaptive Life Concierge Agent – AI multi-agent productivity system with ML predictions & LLM integration.

Weekender: A Multi-Agent Concierge system using Gemini 2.5 to plan conflict-free weekend itineraries.

Multi-agent AI system powered by Google Gemini API. Features SmartTravel AI travel concierge with weather intelligence and modular agent architecture.

A concierge-style multi-agent system that automatically plans study schedules, generates learning materials, quizzes students, and evaluates

Rahagir: Multi-agent concierge system that proactively plans, optimizes, and personalizes travel by resolving conflicts and packing lists.

AI-powered smart concierge that detects user life signals and provides personalized recommendations using a multi-agent system.

LifeTrack AI is a multi-agent concierge system designed to help users track, analyze, and improve their daily routines.

Multi-agent AI concierge system for hotel booking, payment, guest support, and service management using Google ADK and Gemini.

A simple multi-agent concierge system that searches availability, manages bookings, and demonstrates tools, memory, and observability for the Google Agents Intensive capstone.

Production-grade multi-agent conversational AI system inspired by Financial Services pioneers in Chat Concierge platforms. Demonstrates scalable multi-agent architectures and showcases expertise in applying agentic AI within regulated industries.

The GoNow AI Travel Concierge is a multi-agent system that uses Gemini and real-time search to orchestrate 8 specialized sub-agents, delivering personalized, budget-conscious travel plans instantly.

MACS Project Multi-Agent Concierge System (MACS) leverages advanced AI agents to streamline workflows, enhance productivity, and provide seamless user interactions. Features include RAG-based indexing, vector DB integration, and multi-agent orchestration for efficient task management and intelligent decision-making.

A multi-agent concierge system that transforms messy to-do lists into realistic 25-minute focus plans, reducing decision fatigue and context switching for knowledge workers.