Agents-internal

About this Agent

The Payroll Audit Compliance Agent is an AI digital worker designed to streamline the auditing process of payroll records. It ensures that all payroll activities are in full compliance with local, state, and federal regulations. This intelligent agent meticulously scans payroll data, identifies any discrepancies, and flags potential compliance issues, providing businesses with peace of mind and reducing the risk of costly errors.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Payroll Audit Compliance Agent:

Payroll records

Employee ID	First Name	Last Name	Pay Date	Gross Salary	Federal Tax Deduction	State Tax Deduction	Local Tax Deduction	Net Pay
1	John	Smith	2024-09-30	5000	500	250	50	4200
2	Sarah	Johnson	2024-09-30	6000	600	300	60	5040
3	Emily	Brown	2024-09-30	4500	450	225	45	3780
4	William	Wilson	2024-09-30	7500	750	375	75	6300
5	Olivia	Davis	2024-09-30	5800	580	290	58	4872

Deliverable Example

Sample output delivered by the Payroll Audit Compliance Agent:

Employee ID	First Name	Last Name	Pay Date	Audit Status	Discrepancy Description
1	John	Smith	2024-09-30	Compliant	No discrepancies
2	Sarah	Johnson	2024-09-30	Compliant	No discrepancies
3	Emily	Brown	2024-09-30	Discrepancy Found	State tax deduction incorrect, should be $240
4	William	Wilson	2024-09-30	Compliant	No discrepancies
5	Olivia	Davis	2024-09-30	Discrepancy Found	Local tax deduction missing

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.

Payroll Audit Compliance Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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