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Logistics Chatbot for Airport Facilities and Dock Management

Project associated with:



In the fast-paced logistics and transportation industry, the need for real-time information is paramount. Freight forwarders, trucking companies, and airport authorities require up-to-date data on airport facilities, docks, and live statuses based on Airway Bill (AWB) numbers to efficiently manage cargo. A chatbot solution was developed to address these needs, providing seamless access to crucial information and enhancing overall cargo management.


Real-Time Information Retrieval:

  1. Provide up-to-date flight status based on AWB numbers.

  2. Offer real-time updates on dock management and delivery details.

Dynamic Query Handling:

  1. Understand and respond to a wide range of user queries related to airport facilities, docks, and cargo statuses.

  2. Identify and manage different intents, ensuring accurate responses.

User Access and Interface:

  1. Ensure the chatbot is easily accessible to freight forwarders, trucking companies, and airport authorities and provide a user-friendly interface for interaction.

Scalability and Reliability:

  1. Ensure the solution can handle multiple queries simultaneously without performance degradation.

  2. Maintain high availability and reliability for continuous operations.


Requirement Analysis:

  1. Detailed discussions with stakeholders to understand their needs and expectations. 

  2. Identification of key features and functionalities required in the chatbot.

Design and Architecture:

  1. Designing the architecture of the chatbot solution, focusing on scalability, reliability, and real-time data processing.

  2. Planning the integration of various components such as AWS Lex, Lambda, EC2, and FastAPI.


  1. Developing the chatbot using AWS Lex for natural language understanding and dialogue management. 

  2. Utilizing AWS Lambda for serverless computing to handle backend logic and integrate with other services. 

  3. Deploying FastAPI on EC2 instances to handle API requests and responses efficiently. 

  4. Integrating Large Language Models (LLMs) to enhance dynamic query handling and intent identification.

Testing and Validation:

  1. Conducting extensive testing to ensure the chatbot accurately understands and responds to user queries.

  2. Validating the real-time information retrieval process to ensure data accuracy and timeliness.

Deployment and Monitoring:

  1. Deploying the solution on AWS infrastructure, ensuring scalability and reliability.

  2. Implementing monitoring and logging to track performance and address any issues promptly.

Technologies Used

AWS Lex:

  • Cloud Platforms: AWS

  • Purpose: Natural language understanding and dialogue management. Handles user interactions and manages the flow of conversation.

AWS Lambda:

  • Languages: Python

  • Cloud Platforms: AWS

  • Purpose: Serverless computing for backend logic and integration. Executes code in response to triggers such as user queries.

Amazon EC2:

  • Cloud Platforms: AWS

  • Purpose: Deploying FastAPI to handle API requests and responses. Ensures high availability and scalability of the API layer.


  • Languages: Python

  • Libraries/Frameworks: FastAPI

  • Purpose: A modern, fast (high-performance) web framework for building APIs with Python. Handles API requests related to flight status, dock management, and delivery details.

Large Language Models (LLMs):

  • Languages: Python

  • Libraries/Frameworks: Hugging Face Transformers, Langchain

  • Cloud Platforms: AWS Bedrock

  • Purpose: Enhances dynamic query handling and intent identification. Ensures the chatbot can manage a wide range of user queries accurately.

AWS CloudWatch:

  • Cloud Platforms: AWS

  • Purpose: Monitoring and logging of chatbot performance and interactions. Provides insights into usage patterns and helps in identifying issues.

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