Agents-internal

About this Agent

The Compliance Risk Assessment Agent is an AI digital worker designed to streamline the process of evaluating compliance risks within financial operations. By meticulously reviewing contracts, regulatory obligations, and financial activities, this AI worker identifies and flags potential compliance issues. This proactive approach aids organizations in maintaining adherence to regulations and minimizing compliance-related risks, ensuring a smooth and compliant financial operation.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Compliance Risk Assessment Agent:

Contract_ID	Company_Name	Contract_Start_Date	Contract_End_Date	Contract_Value
501	Josh Inc	2022-01-15	2025-01-15	500000
502	Smith Consulting	2023-05-30	2024-05-30	120000
503	Green Tech	2021-08-01	2023-08-01	85000
504	Baker Manufacturing	2023-03-10	2024-03-10	200000

Transaction_ID	Account_Name	Transaction_Amount	Transaction_Date	Description
1001	Josh Inc	12500	2023-08-12	Purchase of equipment
1002	Smith Consulting	34000	2023-07-19	Consulting services
1003	Green Tech	5500	2023-09-25	Software license
1004	Baker Manufacturing	27500	2023-10-02	Materials supply

Obligation_ID	Company_Name	Obligation_Type	Due_Date	Status
301	Josh Inc	Tax Filing	2023-12-15	Pending
302	Smith Consulting	Compliance Report	2024-03-10	Completed
303	Green Tech	Annual Review	2023-11-20	Pending
304	Baker Manufacturing	License Renewal	2023-12-05	Pending

Deliverable Example

Sample output delivered by the Compliance Risk Assessment Agent:

Issue_ID	Company_Name	Issue_Type	Severity	Action_Required
1	Green Tech	Contract nearing expiration	High	Renew contract before 2023-08-01
2	Josh Inc	Pending tax filing	Medium	Submit tax filing before 2023-12-15

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.

Compliance Risk Assessment Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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