Agentic AIadvanced
Cold Chain Logistics - AI Assistant
This project transforms a legacy cold-chain logistics database into an autonomous AI dispatch console powered by a LangGraph ReAct agent. By integrating spatial SQL telemetry, live weather APIs, and Vector RAG compliance rulebooks, the system proactively anticipates and resolves cargo spoilage risks before they occur. Built for enterprise reliability, it features an invisible, immutable SQL audit trail and is fully deployed to a live AWS EC2 environment using automated CI/CD pipelines.
13 lectures
What You Will Learn
Design and deploy a LangGraph ReAct agent that acts as a central brain, autonomously deciding when to use tools, query databases, or read documents.
Successfully bridge Structured Data (SQL Server telemetry) with Unstructured Data (Vector RAG compliance rulebooks) in a single workflow.
Teach an LLM to dynamically translate natural language geographic terms into precise GPS bounding-box SQL queries.
Engineer an invisible, immutable SQL logging system that permanently records every LLM thought process, tool execution, and decision for enterprise trust.
Move beyond local hosting by implementing a complete CI/CD pipeline using GitHub Actions to deploy the application securely to an AWS EC2 instance.
System Architecture
High-level architecture overview of the Cold Chain Logistics - AI Assistant .
What You'll Build
- You will build an Autonomous Cold-Chain Logistics Dispatch Console. Moving beyond static dashboards, you will transform a legacy telemetry database into a proactive, real-time reasoning engine. Your AI copilot will dynamically query fleet GPS data, fetch live external weather APIs to detect environmental threats (like route heatwaves), and cross-reference Vector RAG corporate rulebooks to prevent cargo spoilage. The entire system will be wrapped in a professional Streamlit UI and deployed live to AWS.
Project Instructor
Monal Singh
5 years exp
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Krish AI
Beta