About the Agent

The Order Confirmation Email Agent uses generative AI to automate the creation and delivery of personalized order confirmation emails. This eliminates manual email drafting, freeing customer service teams to focus on more complex inquiries. By accessing real-time order data, the agent ensures accurate and timely communication, improving customer satisfaction and building trust. Personalized emails include details such as order summaries and estimated delivery dates, enhancing the customer experience. This automation streamlines order processing workflows, reduces errors, and boosts customer confidence, ultimately contributing to a more efficient and customer-centric operation.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Order Confirmation Email Agent:

Order data sample

OrderNumber	CustomerName	CustomerEmail	ItemDetails	TotalPrice	EstimatedDeliveryDate
60494	Jennifer Green	jgreen23@gmail.com	Shirt- White	$285	2024-10-19
86465	Colleen Taylor	colleen.taylor@outlook.com	T-shirt- Black	$515	2024-10-15
19665	Melinda Kennedy	mkennedy87@yahoo.com	Pant - White	$371	2024-10-17
11840	Ryan Page	ryan.page@email.com	Shorts - Red	$539	2024-10-16
84384	Robert Thomas	robert.thomas@email.com	Socks - Red	$25	2024-10-14

Deliverable Example

Sample output delivered by the Order Confirmation Email Agent:

Order Confirmation - #60494

Dear Jennifer Green,

Thank you for your order! We’re excited to confirm that your purchase has been processed successfully.

Order Summary

Order Number: 60494
Items Purchased: Shirt - White
Total Price: $285
Estimated Delivery Date: 2024-10-19

If you have any questions or need assistance, please don't hesitate to contact our customer service team.

Best regards,
Delivery Executive Techsoft Pvt Ltd

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 Confirmation Email Agent

About the Agent

Input Data Set

Deliverable Example

Order Confirmation - #60494

Order Summary

Company

Product

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