Supply Chain Intelligence Assistant using Mistral + RAG

Description

This project builds an AI assistant using Mistral and Retrieval-Augmented Generation (RAG) to provide real-time insights and decision support in supply chain operations. It integrates structured and unstructured data—like shipment logs, inventory reports, supplier contracts, and performance records—to answer complex queries and detect potential issues. The assistant helps supply chain managers make faster, data-driven decisions by generating summaries, anomaly alerts, and predictive insights. Ideal for logistics companies, manufacturing firms, and procurement teams, this solution reduces operational delays, enhances visibility, and improves responsiveness across the supply chain lifecycle.

Key Features:

• Multi-source Data Integration – Parses PDFs, spreadsheets, emails, and ERP exports to build a searchable knowledge base.
• Real-time Q&A – Supports natural language queries like “Which supplier had the most delays last quarter?”
• Risk & Delay Detection – Flags anomalies in lead times, inventory levels, or vendor performance using rule-based and LLM-assisted logic.
• Scenario Simulation – Assists with what-if analysis by generating hypothetical outcomes (e.g., “What happens if Port A is delayed by 5 days?”)

Ideal Use Cases or Scenarios:

• Procurement teams reviewing supplier reliability and pricing terms
• Logistics managers tracking shipment flow and anticipating bottlenecks
• Manufacturers planning inventory based on predictive insights
• Consulting firms analyzing client supply chain data for reports

Deliverables:

• Backend pipeline with Mistral + LangChain + RAG integration
• Supply chain data processing scripts for CSVs, PDFs, and DOCs
• Web-based dashboard with file upload, chat-based Q&A, and alert views
• Sample dataset including delivery logs, vendor SLAs, and warehouse reports
• Documentation for deployment, customization, and prompt design
• Optional visualization module for supplier scores and delay trends