Agents-internal

About this Agent

The Corporate Tax Review Agent is a digital AI worker designed to streamline the process of corporate tax reporting by automatically reviewing tax filings. It ensures compliance with current tax regulations and provides actionable recommendations for any necessary corrections. This AI worker aids finance departments in maintaining accurate and compliant tax documentation, reducing the risk of penalties and enhancing overall efficiency in tax reporting processes.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Corporate Tax Review Agent:

Filing ID	Company Name	Tax Year	Gross Income	Total Deductions	Tax Paid	Filing Date	Filing Status
1	Hamilton Corp	2023	1200000	300000	150000	2023-03-12	Submitted
2	Brighton Holdings	2022	850000	250000	90000	2023-01-25	Submitted
3	Evans Consulting	2023	600000	150000	70000	2023-03-18	Submitted

Record ID	Company Name	Tax Year	Income	Deductions	Expenses	Record Date
101	Hamilton Corp	2023	1200000	290000	150000	2023-02-15
102	Brighton Holdings	2022	860000	255000	120000	2022-12-30
103	Evans Consulting	2023	600000	140000	80000	2023-03-05

Deliverable Example

Sample output delivered by the Corporate Tax Review Agent:

Filing ID	Company Name	Suggested Action	Deadline
1	Hamilton Corp	Amend the 2023 filing to reflect accurate deductions	2023-05-30
2	Brighton Holdings	Submit proof of expenses for tax year 2022	2023-06-15
3	Evans Consulting	Include additional expenses as deductions for 2023	2023-04-10

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.

Corporate Tax Review Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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