Agents-internal

About this Agent

The Ticket Categorization Agent is an AI-powered digital assistant designed to streamline the help desk support process by automatically categorizing incoming help desk tickets. Utilizing advanced AI-driven text analysis, this agent accurately identifies and classifies tickets based on issue types such as hardware, software, and network issues. By automating the categorization process, the Ticket Categorization Worker enhances the efficiency of IT support teams, reduces response times, and ensures that tickets are directed to the appropriate specialists for faster resolution.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Ticket Categorization Agent:

Ticket input sample

Ticket ID	Ticket Description	Submitted Date
1	The laptop is not turning on.	2024-10-01
2	Unable to connect to the Wi-Fi network.	2024-10-02
3	Software installation is failing.	2024-10-03
4	Printer is not working.	2024-10-04
5	Cannot access the company VPN.	2024-10-05

Deliverable Example

Sample output delivered by the Ticket Categorization Agent:

Ticket ID	Ticket Description	Submitted Date	Category	Confidence Score
1	The laptop is not turning on.	2024-10-01	Hardware	0.9
2	Unable to connect to the Wi-Fi network.	2024-10-02	Network	0.9
3	Software installation is failing.	2024-10-03	Software	0.9
4	Printer is not working.	2024-10-04	Hardware	0.9
5	Cannot access the company VPN.	2024-10-05	Network	0.9

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.

Ticket Categorization Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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