Agents-internal

About this Agent

The Liquidity Planning Optimization Agent is designed to automate and enhance the process of liquidity planning within an organization. By analyzing cash reserves and upcoming financial obligations, this AI worker ensures optimal cash flow management. It provides financial teams with actionable insights to maintain adequate liquidity levels, minimize risks associated with cash shortages, and optimize the deployment of surplus cash. This results in improved financial stability and efficiency in treasury operations.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Liquidity Planning Optimization Agent:

Liquidity input

Date	Account Holder	Bank Name	Account Number	Cash Reserve (USD)	Upcoming Obligation (USD)	Obligation Date
2024-10-15	John Smith	Wells Fargo	1234567890	150000	80000	2024-10-18
2024-10-15	Emily Johnson	Bank of America	9876543210	220000	120000	2024-10-20
2024-10-15	Michael Williams	Chase	1928374650	350000	175000	2024-10-25
2024-10-15	Sarah Brown	Citibank	5647382910	500000	300000	2024-10-30

Deliverable Example

Sample output delivered by the Liquidity Planning Optimization Agent:

Date	Account Holder	Optimized Cash Flow (USD)	Recommendation	Surplus/Deficit (USD)
2024-10-15	John Smith	70000	Reduce obligations or seek short-term funding	-10000
2024-10-15	Emily Johnson	100000	Adequate liquidity for upcoming obligations	20000
2024-10-15	Michael Williams	175000	No action needed, liquidity sufficient	0
2024-10-15	Sarah Brown	200000	Consider optimizing investment options	100000

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.

Liquidity Planning Optimization Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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