Agents-internal

About this Agent

The Payment Dispute Resolution Agent is an AI-driven solution designed to efficiently manage and resolve disputes between vendors and the company over invoice discrepancies or payment terms. This digital worker automates the dispute resolution process, ensuring timely and accurate handling of payment issues, thereby reducing friction and fostering stronger vendor relationships. By leveraging advanced AI capabilities, the agent analyzes dispute cases, communicates with relevant parties, and proposes solutions, significantly reducing the time and effort traditionally required for resolution.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Payment Dispute Resolution Agent:

Payment dispute input

Vendor	Invoice Number	Dispute Reason	Invoice Amount	Paid Amount	Disputed Amount
Smith Supplies	INV1001	Incorrect Amount	1500	1400	100
Johnson Industries	INV1002	Late Delivery	2000	2000	0
Baker Distribution	INV1003	Item Missing	1250	1250	0
King Electronics	INV1004	Wrong Item	800	800	0

Deliverable Example

Sample output delivered by the Payment Dispute Resolution Agent:

Vendor	Invoice Number	Resolution	Adjusted Invoice Amount	Dispute Status
Smith Supplies	INV1001	Partial Refund of $100	1400	Resolved
Johnson Industries	INV1002	No Discrepancy Found	2000	Resolved
Baker Distribution	INV1003	No Discrepancy Found	1250	Resolved
King Electronics	INV1004	No Discrepancy Found	800	Resolved

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.

Payment Dispute Resolution Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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