Agents-internal

About this Agent

The Inquiry Routing Agent is an advanced AI digital worker designed to enhance customer support efficiency by automatically routing customer inquiries to the most suitable support agent or department. Utilizing sophisticated content analysis and categorization techniques, this AI worker ensures that each inquiry reaches the right destination quickly, reducing response times and improving customer satisfaction.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Inquiry Routing Agent:

Inquiries

Inquiry ID	Customer Name	Email	Subject	Description	Category
1	John Smith	john.smith@outlook.com	Billing issue	I was overcharged this month.	Billing
2	Emma Brown	emma.brown@outlook.com	Technical support needed	My internet is not working properly.	Technical Support
3	Liam Johnson	liam.johnson@outlook.com	Product Inquiry	Can you provide more details on the premium package?	Sales
4	Olivia Wilson	olivia.wilson@outlook.com	General question	What are your business hours?	General Inquiry
5	Noah Taylor	noah.taylor@outlook.com	Refund request	I would like a refund for my last order.	Billing

Deliverable Example

Sample output delivered by the Inquiry Routing Agent:

Inquiry ID	Customer Name	Assigned Department	Assigned Agent	Status
1	John Smith	Billing Team	Alice Cooper	Routed
2	Emma Brown	Tech Support	Brian Adams	Routed
3	Liam Johnson	Sales	Catherine Hughes	Routed
4	Olivia Wilson	Customer Service	David Clarkson	Routed
5	Noah Taylor	Billing Team	Alice Cooper	Routed

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.

Inquiry Routing Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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