Agents-internal

About this Agent

The Client Invoice Summarization Agent is designed to enhance the efficiency of the finance team's workflow by automatically summarizing client invoices. This AI digital worker identifies and highlights key details, allowing for faster review and approval. By reducing the time spent on manual invoice examination, the finance team can focus on more strategic tasks, ensuring timely and accurate financial processing.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Client Invoice Summarization Agent:

Client invoices

Invoice Number	Client Name	Invoice Date	Due Date	Invoice Amount	Discount
INV-1001	Johnson & Co	2024-10-01	2024-10-15	15000.0	300.0
INV-1002	Westfield Industries	2024-10-02	2024-10-16	8500.0	200.0
INV-1003	Brighton Solutions	2024-10-03	2024-10-17	10750.0	0.0
INV-1004	Kingsley Corporation	2024-10-05	2024-10-19	22000.0	500.0

Deliverable Example

Sample output delivered by the Client Invoice Summarization Agent:

Invoice Number	Client Name	Invoice Amount	Due Date	Key Highlights
INV-1001	Johnson & Co	15000.0	2024-10-15	Discount applied: $300. No penalty. Payment received on time.
INV-1002	Westfield Industries	8500.0	2024-10-16	Discount available: $200. Payment pending.
INV-1003	Brighton Solutions	10750.0	2024-10-17	Payment overdue.
INV-1004	Kingsley Corporation	22000.0	2024-10-19	Discount applied: $500. Payment received.

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.

Client Invoice Summarization Agent

About this Agent

Input Data Set

Deliverable Example

Company

Product

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