Details:

1. Model Training: Use TensorFlow to develop a machine learning model for understanding and generating human-like responses. Train the model using a dataset of customer service interactions, employing techniques like sequence-to-sequence learning and attention mechanisms.

2. Natural Language Processing: Utilize NLTK for text preprocessing tasks such as tokenization, stemming, and named entity recognition. Implement sentiment analysis to gauge the tone of customer inquiries.

3. Backend Development: Create a backend service with Flask to manage chatbot interactions and API requests. The Flask app will handle incoming messages, process them using the TensorFlow model, and send back appropriate responses.

4. Serverless Deployment: Deploy the chatbot backend on AWS Lambda to ensure scalability and cost-effectiveness. Use AWS Lambda functions to process requests and interact with the TensorFlow model.

5. API Management: Set up AWS API Gateway to handle API requests and route them to the appropriate Lambda functions. This will enable secure and efficient communication between the frontend and backend.

6. Frontend Integration: Integrate the chatbot with a customer service platform or a website. Implement a chat interface that allows users to interact with the bot in real-time.

7. Testing and Optimization: Conduct extensive testing to ensure the chatbot handles various customer queries effectively. Continuously monitor and optimize the model based on user interactions and feedback.