Order Status Update Agent

About the Agent

The Order Status Update Agent uses generative AI to automate order status updates, keeping customers informed throughout the order lifecycle (e.g., received, processing, shipped, delivered). This eliminates manual status checks, freeing customer service teams to handle more complex inquiries. By integrating with the enterprise's order management system, the agent provides timely and accurate updates through preferred communication channels (e.g., email, SMS). Personalized messages enhance the customer experience and build trust. This automation streamlines workflows, improves customer satisfaction and strengthens customer relationships, ultimately contributing to greater business success.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Order Status Update Agent:

Order status updates

OrderID	CustomerName	Email	PhoneNumber	Status	Date
1001	John Smith	john.smith@yahoo.com	+14155550101	Order Received	2024-10-10
1002	Emily Brown	emily.brown@hotmail.com	+61234567890	Processing	2024-10-11
1003	Michael Johnson	michael.johnson@outlook.com	+14155550102	Shipped	2024-10-12
1004	Sarah Wilson	sarah.wilson@gmail.com	+61412345678	Delivered	2024-10-13

Deliverable Example

Sample output delivered by the Order Status Update Agent:

Order Status Update: Delivered

Dear Sarah Wilson,

We are happy to inform you that your order (Order ID: 1004) has been delivered on 2024-10-13. We hope everything meets your expectations. If you have any questions, feel free to contact us.

Thank you for shopping with us!

Best regards,
Customer Service Team

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

Order Status Update Agent

About the Agent

Input Data Set

Deliverable Example

Order Status Update: Delivered

Company

Product

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