Agents-internal

About this Agent

The Cash Application Automation Agent is designed to enhance the efficiency of the accounts receivable process by automating the application of cash receipts to the correct customer accounts and invoices. This digital worker ensures accurate and timely reconciliation, reducing manual errors and accelerating the cash application process. By leveraging advanced algorithms, it matches payments with open invoices, updates records, and provides real-time insights into cash flow, enabling financial teams to focus on strategic tasks.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Cash Application Automation Agent:

Cash application input

Receipt ID	Customer Name	Invoice Number	Payment Amount	Payment Date	Payment Method	Customer ID	Outstanding Invoices	Total Outstanding	Last Payment Date
R1001	Josh Lautner	INV-10234	1200.0	2024-10-10	Credit Card	CUST-001	INV-10234, INV-10230	1200.0	2024-09-20
R1002	Alice Smith	INV-10235	950.0	2024-10-11	Bank Transfer	CUST-002	INV-10235	950.0	2024-09-25
R1003	Michael Brown	INV-10236	700.0	2024-10-12	Check	CUST-003	INV-10236, INV-10231	700.0	2024-09-30
R1004	Emma Johnson	INV-10237	2500.0	2024-10-12	ACH	CUST-004	INV-10237	2500.0	2024-09-22
R1005	William Taylor	INV-10238	1500.0	2024-10-13	PayPal	CUST-005	INV-10238, INV-10232	1500.0	2024-09-28

Deliverable Example

Sample output delivered by the Cash Application Automation Agent:

Receipt ID	Customer Name	Applied Invoice Number	Applied Amount	Status
R1001	Josh Lautner	INV-10234	1200.0	Fully Applied
R1002	Alice Smith	INV-10235	950.0	Fully Applied
R1003	Michael Brown	INV-10236	700.0	Fully Applied
R1004	Emma Johnson	INV-10237	2500.0	Fully Applied
R1005	William Taylor	INV-10238	1500.0	Fully Applied

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.

Cash Application Automation Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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