Agents-internal

About this Agent

The User Feedback Analysis Agent is designed to revolutionize IT support by automating the collection and analysis of user feedback from resolved help desk tickets. Utilizing advanced sentiment analysis techniques, this AI agent identifies user satisfaction levels and pinpoints areas needing improvement. By transforming qualitative feedback into actionable insights, it empowers IT departments to enhance service quality and user experience effectively.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for User Feedback Analysis Agent:

Help Desk Feedback Data

Ticket ID	User ID	Feedback Text	Resolution Date	Agent ID
101	1001	The issue was resolved quickly, great service!	2024-10-01	201
102	1002	The agent took too long to respond and didn't solve my problem.	2024-10-02	202
103	1003	I'm satisfied with the resolution, but it could have been faster.	2024-10-03	203
104	1004	Terrible service, very disappointed with the outcome.	2024-10-04	204
105	1005	The agent was helpful, but the process was confusing.	2024-10-05	205

Deliverable Example

Sample output delivered by the User Feedback Analysis Agent:

Ticket ID	User ID	Feedback Text	Sentiment Score	Sentiment Label	Feedback Length	Resolution Date	Improvement Suggestion
101	1001	The issue was resolved quickly, great service!	1	Positive	7	2024-10-01	N/A
102	1002	The agent took too long to respond and didn't solve my problem.	-1	Negative	12	2024-10-02	Consider providing quicker response time.
103	1003	I'm satisfied with the resolution, but it could have been faster.	1	Positive	11	2024-10-03	N/A
104	1004	Terrible service, very disappointed with the outcome.	-1	Negative	7	2024-10-04	Consider providing quicker response time.
105	1005	The agent was helpful, but the process was confusing.	1	Positive	9	2024-10-05	N/A

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.

User Feedback Analysis Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

Contact us

Get more information

In this personalized demo, you will:

Explore ZBrain Platform

Discover Workflow Integrations

Engage in Q&A Session