About this Agent

The Automated Customer Reminder Agent is a powerful AI digital worker designed to enhance the efficiency of accounts receivable processes by sending timely, automated payment reminders to customers. This intelligent agent ensures that invoices approaching their due dates are not overlooked, thereby facilitating improved cash flow management. By automating the reminder process, it reduces the manual workload on finance teams and helps maintain positive customer relationships through consistent and polite communication.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Automated Customer Reminder Agent:

Invoices input

Invoice ID	Customer Name	Customer Email	Invoice Amount	Invoice Date	Due Date
101	John Smith	john.smith@outlook.com	500	2024-09-01	2024-10-15
102	Alice Johnson	alice.johnson@techcorp.com	1200	2024-09-05	2024-10-12
103	Michael Brown	michael.brown@outlook.com	750	2024-09-10	2024-10-18
104	Emma Davis	emma.davis@outlook.com	300	2024-09-12	2024-10-11
105	William Wilson	william.wilson@outlook.com	400	2024-09-15	2024-10-20

Deliverable Example

Sample output delivered by the Automated Customer Reminder Agent:

Payment Reminder

Dear John Smith,

This is a friendly reminder that your invoice with ID 101 for the amount of $500 is due on 2024-10-15.

We kindly ask you to make the payment by the due date to avoid any late fees or penalties.

If you have already made the payment, please disregard this message. Otherwise, we appreciate your prompt attention to this matter.

Thank you for your business.

Best regards,

Finance Team

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.

Automated Customer Reminder Agent

About this Agent

Input Data Set

Deliverable Example

Payment Reminder

Company

Product

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