Agents-internal

About this Agent

The Categorization Agent is an AI digital worker designed to streamline the process of analyzing customer feedback. By automatically categorizing feedback into predefined categories such as product quality, delivery issues, and support experience, this AI worker enables businesses to efficiently process and understand customer sentiments. This targeted approach aids in identifying areas for improvement and enhances overall customer service strategies.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Categorization Agent:

Categorization Agent input

Customer Name	Feedback Date	Feedback Text
John Smith	2024-10-10	The product arrived late and was damaged.
Emily Johnson	2024-10-09	Customer support was very helpful in resolving my issue.
David Brown	2024-10-08	Quality of the product is not up to the mark.
Sarah Williams	2024-10-07	Delivery was quick and the packaging was excellent.
Michael Lee	2024-10-06	Had trouble getting help from customer support.

Deliverable Example

Sample output delivered by the Categorization Agent:

Customer Name	Feedback Date	Feedback Text	Category
John Smith	2024-10-10	The product arrived late and was damaged.	Delivery Issues
Emily Johnson	2024-10-09	Customer support was very helpful in resolving my issue.	Support Experience
David Brown	2024-10-08	Quality of the product is not up to the mark.	Product Quality
Sarah Williams	2024-10-07	Delivery was quick and the packaging was excellent.	Delivery Issues
Michael Lee	2024-10-06	Had trouble getting help from customer support.	Support Experience

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.

Categorization Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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