Chargeback Handling Agent

About the Agent

The Chargeback Handling Agent utilizes GenAI to automate the chargeback claims process by retrieving transaction data to validate disputes, gathering supporting documentation, and facilitating efficient responses to payment processors. By ensuring comprehensive and accurate chargeback responses, the agent reduces the financial impact of disputed charges, upholds precise billing records, and minimizes revenue loss from unverified claims.

Seamlessly integrating with existing transaction systems, the Chargeback Handling Agent ensures smooth data flow across platforms and provides real-time access to transaction history for effective chargeback management. Additionally, it incorporates a human feedback loop, allowing continuous improvement in dispute resolution. As human experts review and provide feedback on chargeback responses, the agent refines its decision-making process, enhancing its accuracy and effectiveness over time.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Chargeback Handling Agent:

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Deliverable Example

Sample output delivered by the Chargeback Handling Agent:

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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.

Chargeback Handling Agent

About the Agent

Input Data Set

Deliverable Example

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Product

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