Agents-internal

About this Agent

The Payroll Discrepancy Detection Agent is an AI-driven solution designed to enhance the accuracy and reliability of payroll processing. By utilizing advanced AI algorithms, this agent effectively identifies and flags any discrepancies in payroll calculations, ensuring that employees are paid correctly and on time. This tool is invaluable for HR departments looking to streamline their payroll administration processes and minimize errors.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Payroll Discrepancy Detection Agent:

Payroll data

Employee ID	Employee Name	Pay Period Start	Pay Period End	Base Salary	Hours Worked	Overtime Hours	Overtime Pay	Deductions	Total Pay
1001	Michael Harris	2024-09-01	2024-09-15	3000	80	5	100	200	2900
1002	Jennifer Collins	2024-09-01	2024-09-15	3500	82	2	50	300	3300
1003	David Anderson	2024-09-01	2024-09-15	2800	75	8	160	150	2810
1004	Emily Carter	2024-09-01	2024-09-15	3200	78	3	60	250	3010
1005	Christopher Scott	2024-09-01	2024-09-15	4000	85	0	0	500	3500

Deliverable Example

Sample output delivered by the Payroll Discrepancy Detection Agent:

Employee ID	Employee Name	Discrepancy Type	Expected Value	Actual Value	Comments
1001	Michael Harris	Total Pay Mismatch	2950	2900	Total pay calculated is lower than expected.
1002	Jennifer Collins	Overtime Pay Difference	82	50	Overtime pay calculation error.
1003	David Anderson	Deductions Error	130	150	Deductions higher than expected.

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 Discrepancy Detection Agent

About this Agent

Input Data Set

Deliverable Example

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