A multi-agent sample and workshop for a retail banking scenario. Implemented in both C# and Python, using Angular as Frontend!

AgenticShop is a solution accelerator that demonstrates how to build an intelligent, multi-agent retail experience powered by Azure Database for PostgreSQL.

The Retail Shopping Assistant is an AI-powered blueprint that provides a comprehensive interface for an intelligent retail shopping advisor. Built with LangGraph for agent orchestration, it features multi-agent architecture, real-time streaming responses, image-based search, and intelligent shopping cart management.

This project builds a **Multi-Agent AI System** that collaborates across retail components (Stores, Warehouses, Suppliers, Customers) to optimize inventory management

An AI-Powered Multi-Agent Solution for Retail Banking Products.

Gemini Enterprise workshop: grocery retail agent with Discovery Engine StreamAssist, ADK multi-agent architecture, BigQuery analytics, and Vertex AI Agent Engine deployment

An AI-driven multi-agent system for retail optimization, automating inventory management, dynamic pricing, and demand forecasting using Llama 3.2-1b, reinforcement learning, and real-time analytics.

Finsage AI is an AI-powered multi-agent system for retail investors and analysts, built with Python, LLAMA 3.1 (8B Instant), and Phidata. It delivers real-time financial insights, news summaries, and simulations to support smarter, data-driven investment decisions.

A Python-based multi-agent system for retail inventory management. This project leverages intelligent agents to automate demand forecasting,inventory replenishment and pricing optimization to streamline operations in a retail setting.

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(Tarkibi –Tact) Retail Inventory Optimization – Multi-Agent AI System

LangGraph-based multi-step agent for retail tasks using Azure OpenAI

An agentic payment reconciliation engine for multi-processor sports venues and enterprise retail.

A multi-agent AI system that enables intelligent, proactive inventory management across retail networks.

Multi-agent AI system for retail sales analytics with enterprise-grade security (BigQuery AI-level)

Agentic Retail AI Platform with LangGraph multi-agent orchestration, RAG pipeline, FastAPI backend, fallback LLM handling, evaluator agents, and LangSmith monitoring for scalable production GenAI workflows.

AlphaDesk is a multi-agent AI investment research system that automates the daily workflow of a serious retail investor

An AI-powered multi-agent system using LLM to automate Customer 360 data product design for retail banking. (Accenture Hackathon Project)

A Multi-Agent System (MAS) designed to automate retail supply chains by predicting demand, calculating ROP/Safety Stock, and generating smart transfer action plans.

This project implements a multi-agent AI system for optimizing retail inventory management. It uses specialized agents to collaborate and provide comprehensive inventory optimization recommendations with an intuitive web-based dashboard.

A multi-agent AI application that helps users find the cheapest egg prices in their city by scraping retail websites and providing detailed price comparisons.

Multi-agent AI system for retail decision intelligence. Built with .NET Aspire & Microsoft Agent Framework. Includes 8 specialized agents to analyze decisions via fan-out/fan-in orchestration with real-time SSE streaming. React frontend with full OpenTelemetry observability.

An Agentic AI system for retail that monitors stock, forecasts demand, and auto-requests supplier restocks. Built with CrewAI, LangChain, and Pinecone, it uses multi-agent orchestration, RAG, and API integration to enable autonomous, end-to-end inventory management.”

An AI-powered market intelligence platform for Indian retail investors, combining real-time NSE/BSE data with multi-agent analysis for signal discovery, chart insights, portfolio x-ray, and streaming market chat.

A high-performance, multi-agent pipeline designed to neutralize information asymmetry in Indian equity markets. By utilizing Volume Z-Score Analysis and Llama 3.3 70B on Groq LPUs, NEXUS mathematically validates price breakouts to distinguish between institutional conviction and retail bull traps. Built for the ET GenAI Hackathon 2026.

Customer 360 Data Product Designer is an AI-powered multi-agent system built for Accenture's "Hack the Future" hackathon. It automates the design of Customer 360 data products for retail banking using Ollama (Phi-3 Mini), YAML outputs, and SQLite memory. Deployed on Streamlit Community Cloud with ngrok for local Ollama access. Team BMLians

multiagent Retail Bot