Agents-internal

About this Agent

The Client Payment Tracking Agent is an AI digital worker designed to enhance the efficiency of the accounts receivable process. This AI worker diligently tracks client payments and updates their status in real-time, providing a transparent view of all transactions. By ensuring accurate reporting of receivables, it aids in maintaining financial accuracy and streamlining payment management. Ideal for businesses aiming to optimize their financial operations, this AI worker reduces manual effort and minimizes errors in payment tracking.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Client Payment Tracking Agent:

Client payments input

ClientID	ClientName	InvoiceNumber	InvoiceDate	AmountDue	PaymentStatus	DueDate	PaymentMethod	LastUpdated
101	James Carter	INV-12345	2024-09-15	$5000.00	Pending	2024-10-15	Credit Card	2024-09-15
102	Sarah Johnson	INV-12346	2024-09-20	$7500.00	Paid	2024-10-20	Bank Transfer	2024-10-05
103	Oliver Bennett	INV-12347	2024-09-25	$2000.00	Overdue	2024-10-05	Check	2024-10-10
104	Emma Thompson	INV-12348	2024-09-30	$10000.00	Pending	2024-10-30	Bank Transfer	2024-09-30
105	Michael Green	INV-12349	2024-10-01	$3000.00	Pending	2024-10-31	Credit Card	2024-10-01

Deliverable Example

Sample output delivered by the Client Payment Tracking Agent:

ClientID	ClientName	InvoiceNumber	AmountDue	PaymentStatus	PaymentDate	UpdatedByAgentAt
101	James Carter	INV-12345	$5000.00	Paid	2024-10-10	2024-10-10
102	Sarah Johnson	INV-12346	$7500.00	Paid	2024-10-05	2024-10-05
103	Oliver Bennett	INV-12347	$2000.00	Overdue	-	2024-10-10
104	Emma Thompson	INV-12348	$10000.00	Pending	-	2024-10-11
105	Michael Green	INV-12349	$3000.00	Paid	2024-10-12	2024-10-12

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.

Client Payment Tracking Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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