Agents-internal

About this Agent

The Cash Flow Monitoring Agent is a dedicated AI digital worker designed to continuously monitor and analyze cash flow activities. It ensures that all financial inflows and outflows are accurately tracked, offering real-time insights into an organization's liquidity status. This AI worker helps finance teams maintain optimal cash flow levels by providing detailed reports and alerts on cash positions, enabling proactive financial decision-making.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Cash Flow Monitoring Agent:

Date	Transaction ID	Client Name	Amount	Currency	Payment Method	Description
2024-10-01	TRX-1243	John Smith	5000	USD	Bank Transfer	Payment for invoice INV-987
2024-10-05	TRX-1244	Emily Johnson	3200	USD	Credit Card	Consulting Services Fee
2024-10-08	TRX-1245	Michael Brown	2500	USD	Bank Transfer	Monthly Retainer Fee
2024-10-10	TRX-1246	Sarah Williams	7500	USD	PayPal	Software Licensing

Date	Transaction ID	Vendor Name	Amount	Currency	Payment Method	Description
2024-10-02	TRX-2243	Alpha Tech Solutions	1500	USD	Bank Transfer	Equipment Maintenance
2024-10-07	TRX-2244	Office Supplies Ltd	650	USD	Credit Card	Purchase of office supplies
2024-10-09	TRX-2245	Blue Shield Insurance	2100	USD	Check	Monthly Health Insurance Premium
2024-10-12	TRX-2246	Global Consulting	4800	USD	Bank Transfer	Consulting Services

Deliverable Example

Sample output delivered by the Cash Flow Monitoring Agent:

Date	Total Inflows	Total Outflows	Net Cash Flow	Liquidity Status
2024-10-01	5000	1500	3500	Positive
2024-10-05	3200	650	2550	Positive
2024-10-08	2500	2100	400	Neutral
2024-10-10	7500	4800	2700	Positive

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 Flow Monitoring Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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