Agents-internal

About this Agent

The VAT Compliance Monitoring Agent is a specialized AI digital worker designed to streamline and ensure adherence to value-added tax (VAT) obligations. This intelligent agent meticulously monitors all financial transactions to verify compliance with local VAT regulations. It provides accurate and timely filings, reducing the risk of errors and penalties, thereby enhancing overall tax management efficiency. Ideal for finance departments seeking to automate and simplify the complex process of VAT compliance, this agent ensures that your business remains compliant with evolving tax laws.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for VAT Compliance Monitoring Agent:

VAT Compliance Input

Transaction ID	Date	Client Name	Transaction Amount (USD/GBP)	VAT Rate (%)	VAT Paid (USD/GBP)	Country
1001	2024-01-15	James Anderson	2500	20	450	UK
1002	2024-01-16	Emma Roberts	4000	20	0	UK
1003	2024-01-17	Liam Johnson	3200	20	700	UK
1004	2024-01-18	Olivia Smith	5000	20	1200	UK
1005	2024-01-19	Noah Brown	2000	20	400	UK

Deliverable Example

Sample output delivered by the VAT Compliance Monitoring Agent:

Transaction ID	Date	Client Name	Transaction Amount (USD/GBP)	VAT Rate (%)	VAT Paid (USD/GBP)	Country	Issue Type	Status	Filing Date
1001	2024-01-15	James Anderson	2500	20	450	UK	VAT Underpaid	Non-compliant	2024-02-01
1002	2024-01-16	Emma Roberts	4000	20	0	UK	VAT Underpaid, Missing VAT Payment	Non-compliant	2024-02-01
1003	2024-01-17	Liam Johnson	3200	20	700	UK	VAT Overpaid	Non-compliant	2024-02-01
1004	2024-01-18	Olivia Smith	5000	20	1200	UK	VAT Overpaid	Non-compliant	2024-02-01
1005	2024-01-19	Noah Brown	2000	20	400	UK	None	Compliant	2024-02-01

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.

VAT Compliance Monitoring Agent

About this Agent

Input Data Set

Deliverable Example

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Product

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